In [5]:

    

# Data: time-serie smartwatch or wristband or smartband data
# %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import scipy.io as spio

# Data reading
# # Linux-Ubuntu
# data_path = '/home/arasdar/data/Training_data/DATA_01_TYPE01.mat'
# Macbook
data_path = '/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat'
watch = spio.loadmat(data_path)
data = watch['sig']
data = np.array(data, dtype=float)
data.shape, watch['sig'].size/6, data.dtype

# Normalizing each batch of the data, each batch = each file
# Can we normalize them all at the same time?
mean = np.mean(data, axis=1)
std = np.std(data, axis=1)
var = np.var(data, axis=1)
std2 = np.sqrt(var)
mean.shape, var.shape, std.shape, std2.shape, std, std2, std-std2

# mean = mean.reshape(-1, 1)
# std = std.reshape(-1, 1)
# mean.shape, std.shape
# data_norm = (data - mean)/std
# data.shape, data_norm.shape

# # how to normalize, how to take mean, variance, and std
# mean2 = np.mean(data, axis=1).reshape(-1, 1) # mean_nx1: n for dim and t for samples
# var2 = data - mean2
# var2 = np.square(var2)
# var2 = np.mean(var2, axis=1)
# std2 = np.sqrt(var2).reshape(-1, 1)
# std2 - std

# # The features/ channels of the data = ['HR-ECG', 'HR-PPG', 'ACC-X', 'ACC-Y', 'ACC-Z']
# plt.plot(data[0, :1000], label='ECG')
# plt.plot(data[1, :1000], label='PPG-1')
# plt.plot(data[2, :1000], label='PPG-2')
# # plt.plot(x_sig[3, :1000], label='ACC-X')
# # plt.plot(x_sig[4, :1000], label='SCC-Y')
# # plt.plot(x_sig[5, :1000], label='ACC-Z')
# plt.legend()
# plt.show()

# # The features/ channels of the data = ['HR-ECG', 'HR-PPG', 'ACC-X', 'ACC-Y', 'ACC-Z']
# plt.plot(data_norm[0, :1000], label='ECG')
# plt.plot(data_norm[1, :1000], label='PPG-1')
# plt.plot(data_norm[2, :1000], label='PPG-2')
# # plt.plot(x_sig[3, :1000], label='ACC-X')
# # plt.plot(x_sig[4, :1000], label='SCC-Y')
# # plt.plot(x_sig[5, :1000], label='ACC-Z')
# plt.legend()
# plt.show()

# # ECG
# Y = data_norm[0]
# X = data_norm[1:]
# Y.shape, X.shape
# Y = Y.reshape(1, -1)
# Y.shape, X.shape
# plt.plot(Y[0, :1000], label='HR-ECG-1: Output signal')
# plt.plot(X[0, :1000], label='HR-PPG-1: Input signal')
# plt.plot(X[1, :1000], label='HR-PPG-2: Input signal')
# plt.plot(X[2, :1000], label='ACC-X-3: Input signal')
# plt.plot(X[3, :1000], label='ACC-Y-4: Input signal')
# plt.plot(X[4, :1000], label='ACC-Z-5: Input signal')
# plt.legend()
# plt.show()
# X_train = X.T
# Y_train = Y.T
# X_train.shape, Y_train.shape

# # Preparing the training data for seq2seq learning
# Y_train_in = Y_train[:-2]
# Y_train_out = Y_train[:-1]
# #     Y_train_in.shape, Y_train.shape
# firstrow = np.zeros([1, 1])
# #     firstrow.shape, Y_train_in.shape
# Y_train_in = np.row_stack((firstrow, Y_train_in))
# #     Y_train_in.shape, Y_train_in[:5], Y_train[:5]
# #     Y_train_out = Y_train.copy()
# X_train.shape, Y_train_in.shape, Y_train_out.shape
# XY_train = (X_train, Y_train)

# # Read and normalize one batch/file of data
# def read_data(data_path):
#     band = spio.loadmat(data_path)
#     data = band['sig']
#     data = np.array(data)

#     # Normalizing each batch of the data, each batch = each file
#     # Can we normalize them all at the same time?
#     mean = np.mean(data, axis=1).reshape(-1, 1)
#     std = np.std(data, axis=1).reshape(-1, 1)
#     data_norm = (data - mean)/std
    
#     # Input-XX and Output-Y signals
#     # Y: ECG, X: PPG: HR 2 signals + ACC: XYZ
#     Y = data_norm[0]
#     X = data_norm[1:]
#     Y = Y.reshape(1, -1)    
#     X_train = X.T
#     Y_train = Y.T
#     return X_train, Y_train


    

    
Out[5]:





((6,),
 (6,),
 (6,),
 (6,),
 array([ 130.24030289,   96.46027439,  162.32339447,    0.57881244,
           0.77382828,    0.73508067]),
 array([ 130.24030289,   96.46027439,  162.32339447,    0.57881244,
           0.77382828,    0.73508067]),
 array([ 0.,  0.,  0.,  0.,  0.,  0.]))

In [3]:

    

# Model or Network
import impl.layer as l
from impl.loss import *

class GRU:

    def __init__(self, D, C, H, L):
        self.D = D # number of input dimensions
        self.H = H # number of hidden units
        self.L = L # number of hidden layers
        self.C = C # number of output classes/dimensions
        self.losses = {'train':[]} # , 'valid:[]', 'test':[]
        
        # Seq 1: input seq
        Z = H + D
        m_in = dict(
            Wz=np.random.randn(Z, H) / np.sqrt(Z / 2.),
            Wr=np.random.randn(Z, H) / np.sqrt(Z / 2.),
            Wh=np.random.randn(Z, H) / np.sqrt(Z / 2.),
            Wy=np.random.randn(H, D) / np.sqrt(D / 2.),
            bz=np.zeros((1, H)),
            br=np.zeros((1, H)),
            bh=np.zeros((1, H)),
            by=np.zeros((1, D))
        )
        
        # Seq 1 last layer
        m_in_last_layer = dict(
            Wz=np.random.randn(Z, H) / np.sqrt(Z / 2.),
            Wr=np.random.randn(Z, H) / np.sqrt(Z / 2.),
            Wh=np.random.randn(Z, H) / np.sqrt(Z / 2.),
            bz=np.zeros((1, H)),
            br=np.zeros((1, H)),
            bh=np.zeros((1, H))
        )
        
        # Seq 2: output seq
        Z = H + C
        m_out = dict(
            Wz=np.random.randn(Z, H) / np.sqrt(Z / 2.),
            Wr=np.random.randn(Z, H) / np.sqrt(Z / 2.),
            Wh=np.random.randn(Z, H) / np.sqrt(Z / 2.),
            Wy=np.random.randn(H, C) / np.sqrt(H / 2.),
            bz=np.zeros((1, H)),
            br=np.zeros((1, H)),
            bh=np.zeros((1, H)),
            by=np.zeros((1, C))
        )

        # Model parameters
        self.model = []
        num_modes = 2 # num of modality/sequences
        for _ in range(num_modes):
            self.model.append([])
        
        for _ in range(self.L-1):
            self.model[0].append(m_in)

        # The last layer: self.L-1
        self.model[0].append(m_in_last_layer)
        
        # Number of layers for each mode
        for _ in range(self.L):
            self.model[1].append(m_out)
            
    def initial_state(self):
        return np.zeros((1, self.H))
    
    def forward(self, X, h, m):
        Wz, Wr, Wh, Wy = m['Wz'], m['Wr'], m['Wh'], m['Wy']
        bz, br, bh, by = m['bz'], m['br'], m['bh'], m['by']

        X_in = X.copy()
        h_in = h.copy()

        X = np.column_stack((h_in, X_in))

        hz, hz_cache = l.fc_forward(X, Wz, bz)
        hz, hz_sigm_cache = l.sigmoid_forward(hz)

        hr, hr_cache = l.fc_forward(X, Wr, br)
        hr, hr_sigm_cache = l.sigmoid_forward(hr)

        X = np.column_stack((hr * h_in, X_in))
        
        hh, hh_cache = l.fc_forward(X, Wh, bh)
        hh, hh_tanh_cache = l.tanh_forward(hh)

        # h = (1. - hz) * h_old + hz * hh
        # or
        h = ((1. - hz) * h_in) + (hz * hh)
        # or
        # h = h_in + hz (hh - h_in)

        y, y_cache = l.fc_forward(h, Wy, by)

        cache = (h_in, hz, hz_cache, hz_sigm_cache, hr, hr_cache, hr_sigm_cache, hh, hh_cache, hh_tanh_cache, y_cache)

        return y, h, cache

    def backward(self, dy, dh, cache):
        h_in, hz, hz_cache, hz_sigm_cache, hr, hr_cache, hr_sigm_cache, hh, hh_cache, hh_tanh_cache, y_cache = cache
        
        dh_out = dh.copy()

        dh, dWy, dby = l.fc_backward(dy, y_cache)
        dh += dh_out

        dh_in1 = (1. - hz) * dh
        dhh = hz * dh
        dhz = (hh * dh) - (h_in * dh)
        # or
        # dhz = (hh - h_in) * dh

        dhh = l.tanh_backward(dhh, hh_tanh_cache)
        dXh, dWh, dbh = l.fc_backward(dhh, hh_cache)

        dh = dXh[:, :self.H]
        dX_in2 = dXh[:, self.H:]
        dh_in2 = hr * dh

        dhr = h_in * dh
        dhr = l.sigmoid_backward(dhr, hr_sigm_cache)
        dXr, dWr, dbr = l.fc_backward(dhr, hr_cache)

        dhz = l.sigmoid_backward(dhz, hz_sigm_cache)
        dXz, dWz, dbz = l.fc_backward(dhz, hz_cache)

        dX = dXr + dXz
        dh_in3 = dX[:, :self.H]
        dX_in1 = dX[:, self.H:]

        dh = dh_in1 + dh_in2 + dh_in3
        dX = dX_in1 + dX_in2

        grad = dict(Wz=dWz, Wr=dWr, Wh=dWh, Wy=dWy, bz=dbz, br=dbr, bh=dbh, by=dby)
        
        return dX, dh, grad

    def forward_(self, X, h, m):
        Wz, Wr, Wh = m['Wz'], m['Wr'], m['Wh']
        bz, br, bh = m['bz'], m['br'], m['bh']

        X_in = X.copy()
        h_in = h.copy()

        X = np.column_stack((h_in, X_in))

        hz, hz_cache = l.fc_forward(X, Wz, bz)
        hz, hz_sigm_cache = l.sigmoid_forward(hz)

        hr, hr_cache = l.fc_forward(X, Wr, br)
        hr, hr_sigm_cache = l.sigmoid_forward(hr)

        X = np.column_stack((hr * h_in, X_in))
        
        hh, hh_cache = l.fc_forward(X, Wh, bh)
        hh, hh_tanh_cache = l.tanh_forward(hh)

        # h = (1. - hz) * h_old + hz * hh
        # or
        h = ((1. - hz) * h_in) + (hz * hh)
        # or
        # h = h_in + hz (hh - h_in)

        cache = (h_in, hz, hz_cache, hz_sigm_cache, hr, hr_cache, hr_sigm_cache, hh, hh_cache, hh_tanh_cache)

        return h, cache

    def backward_(self, dh, cache):
        h_in, hz, hz_cache, hz_sigm_cache, hr, hr_cache, hr_sigm_cache, hh, hh_cache, hh_tanh_cache = cache
        
        dh_in1 = (1. - hz) * dh
        dhh = hz * dh
        dhz = (hh * dh) - (h_in * dh)
        # or
        # dhz = (hh - h_in) * dh

        dhh = l.tanh_backward(dhh, hh_tanh_cache)
        dXh, dWh, dbh = l.fc_backward(dhh, hh_cache)

        dh = dXh[:, :self.H]
        dX_in2 = dXh[:, self.H:]
        dh_in2 = hr * dh

        dhr = h_in * dh
        dhr = l.sigmoid_backward(dhr, hr_sigm_cache)
        dXr, dWr, dbr = l.fc_backward(dhr, hr_cache)

        dhz = l.sigmoid_backward(dhz, hz_sigm_cache)
        dXz, dWz, dbz = l.fc_backward(dhz, hz_cache)

        dX = dXr + dXz
        dh_in3 = dX[:, :self.H]
        dX_in1 = dX[:, self.H:]

        dh = dh_in1 + dh_in2 + dh_in3
        dX = dX_in1 + dX_in2

        grad = dict(Wz=dWz, Wr=dWr, Wh=dWh, bz=dbz, br=dbr, bh=dbh)
        
        return dX, dh, grad
    
    def train_forward(self, XY_train, h):
        # Adding the output layer cache
        caches = []
        num_modes = 2
        for _ in range(num_modes):
            caches.append([])
        
        h_init = h.copy()
        h = []
        for _ in range(self.L):
            h.append(h_init.copy())
            caches[0].append([])
            caches[1].append([])
            
        ys = []
        X_train, Y_train = XY_train
        
        # Input sequence or Seq 1
        for X in X_train: # X_txn, 
            X= X.reshape(1, -1) # x_1xn
            
            for layer in range(0, self.L - 1, 1):
                y, h[layer], cache = self.forward(X, h[layer], self.model[0][layer])
                caches[0][layer].append(cache)
                X = y.copy()
            
            for layer in range(self.L - 1, self.L, 1):
                h[layer], cache = self.forward_(X, h[layer], self.model[0][layer])
                caches[0][layer].append(cache)
            
        # Output sequence or seq 2
        for X in Y_train: # Y_txn
            X= X.reshape(1, -1) # X_1xn
            
            for layer in range(self.L):
                y, h[layer], cache = self.forward(X, h[layer], self.model[1][layer])
                caches[1][layer].append(cache)
                X = y.copy()

            # Output list
            ys.append(y)
            
        return ys, caches

    def loss_function(self, y_pred, y_train):
        loss, dys = 0.0, []

        for y, Y in zip(y_pred, y_train):
            loss += l2_regression(y_pred=y, y_train=Y) #/ m: t or number of samples for taking the average
            dy = dl2_regression(y_pred=y, y_train=Y)
            dys.append(dy)
            
        return loss, dys

    def train_backward(self, dys, caches):
        dh, grad, grads = [], [], []
        num_modes = 2
        for _ in range(num_modes):
            grad.append([])
            grads.append([])
        
        for _ in range(self.L):
            dh.append(np.zeros((1, self.H)))
            
        for mode in range(num_modes):
            for layer in range(self.L):
                grad[mode].append({key: np.zeros_like(val) for key, val in self.model[mode][layer].items()})
                grads[mode].append({key: np.zeros_like(val) for key, val in self.model[mode][layer].items()})

        # Output sequence = seq 2 = ECG
        dXs_2 = []
        for t in reversed(range(len(dys))):
            dy = dys[t].copy()
            for layer in reversed(range(self.L)):
                dX, dh[layer], grad[1][layer] = self.backward(dy, dh[layer], caches[1][layer][t])
                for key in grad[1][layer].keys():
                    grads[1][layer][key] += grad[1][layer][key]
                dy = dX.copy()
            dXs_2.append(dX)

        # Input sequence = seq 1 = PPG & ACC: mode == 0
        dXs_1 = []
        for t in reversed(range(len(dys))):
            # The depth and number of layers for RNN
            for layer in reversed(range(self.L-1, self.L, 1)):
                dX, dh[layer], grad[0][layer] = self.backward_(dh[layer], caches[0][layer][t])
                for k in grad[0][layer].keys():
                    grads[0][layer][k] += grad[0][layer][k]
                dy = dX.copy()
                
            # The depth and number of layers for RNN
            for layer in reversed(range(0, self.L-1, 1)):
                dX, dh[layer], grad[0][layer] = self.backward(dy, dh[layer], caches[0][layer][t])
                for k in grad[0][layer].keys():
                    grads[0][layer][k] += grad[0][layer][k]
                dy = dX.copy()
            dXs_1.append(dX)
            
        dXs = (dXs_1, dXs_2)
        
        return dXs, grads
    
    # Test/predict is only the forward pass/prop
    # def train_forward(self, XY_train, h):
    def test(self, Xs, h):

        # Initializing the hidden states == h
        h_init = h.copy()
        h = []
        for _ in range(self.L):
            h.append(h_init.copy())

        # Input sequence OR Seq 1 or Xs
        for X in Xs: # X_txn, 
            X= X.reshape(1, -1) # x_1xn
            
            for layer in range(0, self.L - 1, 1):
                y, h[layer], _ = self.forward(X, h[layer], self.model[0][layer])
                X = y.copy()
            
            # The last layer without y; The output is h
            for layer in range(self.L - 1, self.L, 1):
                h[layer], _ = self.forward_(X, h[layer], self.model[0][layer])
                
        # Output sequence OR seq 2 OR ys
        X = np.zeros((1, self.C)) # X_txn, t: num_sam, n: num_dim
        ys = []

        # for t in range(0, X_seq1.shape[0], 1): # Y_txn
        for _ in range(Xs.shape[0]): # X_txn
            X= X.reshape(1, -1) # X_1xn

            for layer in range(self.L):
                y, h[layer], _ = self.forward(X, h[layer], self.model[1][layer])
                X = y.copy()

            # Output list
            ys.append(y)
        
        return ys


    

In [3]:

    

def get_minibatch(X, Y, minibatch_size): # shuffle: this is for static data not dynamic/sequential data
    minibatches = []
    
    # for i in range(start=0, stop=X.shape[0], step=minibatch_size):
    # for i in range(0, X.shape[0] - minibatch_size + 1, 1):
    for i in range(0, X.shape[0], minibatch_size):
        X_mini = X[i:i + minibatch_size]
        Y_mini = Y[i:i + minibatch_size]
        minibatches.append((X_mini, Y_mini))

    return minibatches

def adam_rnn(nn, alpha, mb_size, n_iter, print_after, n_files):
    M, R = [], []
    num_modes = 2
    for _ in range(num_modes):
        M.append([])
        R.append([])

    for mode in range(num_modes):
        for layer in range(nn.L):
            M[mode].append({k: np.zeros_like(v) for k, v in nn.model[mode][layer].items()})
            R[mode].append({k: np.zeros_like(v) for k, v in nn.model[mode][layer].items()})

    beta1 = .9
    beta2 = .99
    state = nn.initial_state()
    eps = 1e-8 # constant
    
    # Epochs
    for iter in range(1, n_iter + 1):
        
        total_loss = 0
        
        # Read the new file, normalize it, and separate it into input and target
        for file_num in range(1, n_files + 1):
            
            if file_num == 1: data_path = '/Users/arasdar/data/Training_data/DATA_{:02}_TYPE01.mat'.format(file_num)
            else: data_path = '/Users/arasdar/data/Training_data/DATA_{:02}_TYPE02.mat'.format(file_num)
#             if file_num == 1: data_path = '/home/arasdar/data/Training_data/DATA_{:02}_TYPE01.mat'.format(file_num)
#             else: data_path = '/home/arasdar/data/Training_data/DATA_{:02}_TYPE02.mat'.format(file_num)
            print(data_path)

            # Read the mat files, normalize each batch of them, and seperate the output and input
            X_train, Y_train = read_data(data_path=data_path)
            #             X_train, Y_train_in = XY_train
            #             print(X_train.shape, Y_train_in.shape, Y_train_out.shape)

            # Minibatches/ Stochasticity
            #             minibatches = get_minibatch(X_train, Y_train, mb_size, shuffle=False)
            minibatches = get_minibatch(X=X_train, Y=Y_train, minibatch_size=mb_size) #, shuffle=False
            #             print('The number of minibatches in a sequence for each epoch iteration: {}'.format (len(minibatches)))

            # Minibatches/ Stochasticity
            #             print('The number of minibatches in a sequence for each epoch iteration: {}'.format (len(minibatches)))
            for idx in range(len(minibatches)):
                
                # Gradients
                X_mini, Y_mini = minibatches[idx]
                Y_mini_out = Y_mini[:-1] # mat[t, n]== Y_txn
                Y_mini_in = Y_mini[:-2]
                firstrow = np.zeros((1, Y_mini.shape[1])) # Y_mini.shape[0] == t, Y_mini.shape[1] == n
                Y_mini_in = np.row_stack((firstrow, Y_mini_in)) # shift foward by one

                XY_mini = (X_mini, Y_mini_in)
                ys, caches = nn.train_forward(XY_mini, state)
                loss, dys = nn.loss_function(y_train=Y_mini_out, y_pred=ys)
                _, grads = nn.train_backward(dys, caches)
                
                # MiniBatches: Drawing the learning curve for loss (Error/loss curve)
                nn.losses['train'].append(loss)
                
                # Descend
                for mode in range(num_modes):
                    for layer in range(nn.L):
                        for key in grads[mode][layer].keys(): #key, value: items
                            M[mode][layer][key] = l.exp_running_avg(M[mode][layer][key], grads[mode][layer][key], beta1)
                            R[mode][layer][key] = l.exp_running_avg(R[mode][layer][key], grads[mode][layer][key]**2, beta2)

                            m_k_hat = M[mode][layer][key] / (1. - (beta1**(iter)))
                            r_k_hat = R[mode][layer][key] / (1. - (beta2**(iter)))

                            nn.model[mode][layer][key] -= alpha * m_k_hat / (np.sqrt(r_k_hat) + eps)

            # Total loss for every batchfile/batch by accumulating them one by one
            total_loss += loss

                            
        # Print training loss
        if iter % print_after == 0:
            # print('Iter-{} training loss: {:.4f}'.format(iter, loss))
            print('Iter-{} training loss: {:.4f}'.format(iter, total_loss))
            total_loss = 0
            #             mb_num = len(minibatches)//2 # the middle one
            #             X_mini, Y_mini = minibatches[mb_num] 
            #             ys = nn.test(X_mini, state)
            #             loss, _ = nn.loss_function(y_train=Y_mini, y_pred=ys)
            #             print('Iter-{} testing loss: {:.4f}'.format(iter, loss))
                            
    return nn


    

In [4]:

    

# hyper parameters
n_iter = 200 # epochs: processing speed and how much time it takes.
n_files = 12 # dataset size: maximum number of files in dataset based on 12 subjects
print_after = 1 # print loss of train, valid, and test
time_step = 100 # width of the model or minibatch size
alpha = 1e-4 # 0.001 - This is set to 1/miniatch_size (mb_size) or num_time_steps
num_hidden_units = 64 # width of the hidden layers or number of hidden units in hidden layer
num_hidden_layers = 2 # depth or number of hidden layer
num_input_units = X_train.shape[1] # number of input features/dimensions
num_output_units = Y_train.shape[1]
# X_train.shape, Y_train.shape

net = GRU(D=num_input_units, H=num_hidden_units, L=num_hidden_layers, C=num_output_units)

adam_rnn(nn=net, alpha=alpha, mb_size=time_step, n_iter=n_iter, n_files=n_files, print_after=print_after)

# Display the learning curve and losses for training, validation, and testing
# % matplotlib inline
import matplotlib.pyplot as plt

plt.plot(net.losses['train'], label='Train loss')
plt.legend()
plt.show()


    

    




/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-1 training loss: 55.9275
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-2 training loss: 51.6111
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-3 training loss: 51.2796
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-4 training loss: 52.3286
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-5 training loss: 53.3206
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-6 training loss: 54.0809
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-7 training loss: 54.4858
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-8 training loss: 54.9366
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-9 training loss: 55.2413
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-10 training loss: 55.4444
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-11 training loss: 55.2565
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-12 training loss: 54.7581
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-13 training loss: 54.1463
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-14 training loss: 53.8075
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-15 training loss: 53.8229
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-16 training loss: 54.1816
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-17 training loss: 54.7120
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-18 training loss: 55.4389
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-19 training loss: 56.2522
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-20 training loss: 57.1427
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-21 training loss: 57.8410
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-22 training loss: 58.5144
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-23 training loss: 59.0634
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-24 training loss: 59.0297
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-25 training loss: 58.6472
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-26 training loss: 57.7636
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-27 training loss: 56.7214
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-28 training loss: 55.3277
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-29 training loss: 54.0111
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Iter-30 training loss: 53.1167
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Iter-31 training loss: 51.7619
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Iter-32 training loss: 50.5082
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/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-33 training loss: 49.2124
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/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-34 training loss: 47.9875
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-35 training loss: 47.5288
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-36 training loss: 46.8777
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/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-37 training loss: 46.2561
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-38 training loss: 45.9052
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-39 training loss: 45.8443
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-40 training loss: 45.9047
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/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-41 training loss: 45.9427
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-42 training loss: 45.9596
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-43 training loss: 45.6040
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/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-44 training loss: 46.0240
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/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-45 training loss: 44.6929
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-46 training loss: 44.7923
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-47 training loss: 44.2451
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-48 training loss: 43.4661
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-49 training loss: 44.2223
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-50 training loss: 45.1594
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-51 training loss: 43.9063
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-52 training loss: 44.8325
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-53 training loss: 43.3210
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-54 training loss: 42.6566
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-55 training loss: 40.5225
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-56 training loss: 41.2317
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-57 training loss: 40.4112
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-58 training loss: 39.5675
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-59 training loss: 38.9189
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Iter-60 training loss: 39.1566
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/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
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Iter-61 training loss: 39.1270
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-62 training loss: 38.7051
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-63 training loss: 38.6483
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-64 training loss: 38.7979
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-65 training loss: 39.5906
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-66 training loss: 38.0010
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-67 training loss: 39.5948
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-68 training loss: 39.3082
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-69 training loss: 38.4034
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-70 training loss: 36.0310
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-71 training loss: 38.4098
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-72 training loss: 38.0954
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-73 training loss: 35.3248
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-74 training loss: 34.7866
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-75 training loss: 34.8010
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-76 training loss: 37.8553
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-77 training loss: 35.1630
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-78 training loss: 32.5682
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-79 training loss: 34.1036
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-80 training loss: 40.2989
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-81 training loss: 32.7089
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-82 training loss: 34.1720
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-83 training loss: 32.4073
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-84 training loss: 31.0631
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-85 training loss: 33.7566
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-86 training loss: 31.9200
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-87 training loss: 32.5297
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
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Iter-88 training loss: 32.6765
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/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-89 training loss: 33.0377
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-90 training loss: 32.8079
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-91 training loss: 32.5467
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-92 training loss: 33.8013
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-93 training loss: 32.4278
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-94 training loss: 33.2441
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-95 training loss: 31.6209
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-96 training loss: 32.9927
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-97 training loss: 31.9585
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-98 training loss: 32.7475
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-99 training loss: 31.8822
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-100 training loss: 31.7272
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-101 training loss: 31.1940
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-102 training loss: 30.1028
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-103 training loss: 32.1562
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-104 training loss: 31.3409
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-105 training loss: 28.1004
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-106 training loss: 29.1685
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-107 training loss: 30.6204
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-108 training loss: 33.2916
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-109 training loss: 26.8723
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-110 training loss: 27.6972
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-111 training loss: 27.5635
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-112 training loss: 29.0927
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-113 training loss: 24.9428
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-114 training loss: 28.0752
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-115 training loss: 27.6007
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-116 training loss: 30.5160
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-117 training loss: 27.8103
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
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Iter-118 training loss: 30.5945
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-119 training loss: 27.8100
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-120 training loss: 27.6565
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-121 training loss: 27.3914
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
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Iter-122 training loss: 27.1220
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-123 training loss: 31.3661
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-124 training loss: 24.3937
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-125 training loss: 24.6313
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-126 training loss: 32.3109
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-127 training loss: 25.5157
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-128 training loss: 24.5032
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-129 training loss: 27.9785
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-130 training loss: 23.1976
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-131 training loss: 21.7671
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-132 training loss: 23.1900
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-133 training loss: 24.3570
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-134 training loss: 27.1572
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-135 training loss: 21.2537
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-136 training loss: 22.8065
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-137 training loss: 28.7325
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-138 training loss: 25.4222
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-139 training loss: 25.1209
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-140 training loss: 22.8090
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-141 training loss: 23.9555
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-142 training loss: 23.1481
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-143 training loss: 25.2089
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-144 training loss: 24.6751
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-145 training loss: 23.9584
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-146 training loss: 25.0176
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-147 training loss: 25.9047
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/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-148 training loss: 26.5862
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-149 training loss: 25.2055
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-150 training loss: 25.5500
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-151 training loss: 24.7325
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-152 training loss: 27.8782
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
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Iter-153 training loss: 24.8464
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-154 training loss: 26.9789
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-155 training loss: 23.4930
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-156 training loss: 24.6261
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-157 training loss: 23.6760
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-158 training loss: 26.7687
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-159 training loss: 23.0324
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-160 training loss: 22.0357
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-161 training loss: 23.7695
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-162 training loss: 27.2344
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-163 training loss: 24.2236
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-164 training loss: 24.5234
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-165 training loss: 26.7507
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-166 training loss: 24.4690
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-167 training loss: 27.1465
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-168 training loss: 26.7022
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-169 training loss: 25.4852
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-170 training loss: 24.3390
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-171 training loss: 30.7912
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-172 training loss: 29.2340
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-173 training loss: 25.6839
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-174 training loss: 24.7939
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-175 training loss: 24.3832
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-176 training loss: 28.0019
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-177 training loss: 23.5917
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-178 training loss: 23.3134
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-179 training loss: 26.5519
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-180 training loss: 24.9551
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-181 training loss: 25.4546
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-182 training loss: 22.7808
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-183 training loss: 21.6924
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-184 training loss: 25.9220
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-185 training loss: 23.4263
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-186 training loss: 22.6883
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-187 training loss: 21.3115
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-188 training loss: 21.4158
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-189 training loss: 21.6192
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-190 training loss: 30.2080
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-191 training loss: 20.4725
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-192 training loss: 32.4738
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-193 training loss: 22.1490
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-194 training loss: 22.4476
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-195 training loss: 20.3061
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-196 training loss: 23.1504
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-197 training loss: 18.7758
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-198 training loss: 18.9964
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-199 training loss: 19.1277
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_08_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_09_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_10_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_11_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_12_TYPE02.mat
Iter-200 training loss: 17.6678






    

In [4]:

    

# # hyper parameters
# n_iter = 100 # epochs: processing speed and how much time it takes.
# n_files = 7 # dataset size: maximum number of files in dataset based on 12 subjects
# print_after = 1 # print loss of train, valid, and test
# time_step = 100 # width of the model or minibatch size
# alpha = 1e-4 # 0.001 - This is set to 1/miniatch_size (mb_size) or num_time_steps
# num_hidden_units = 64 # width of the hidden layers or number of hidden units in hidden layer
# num_hidden_layers = 2 # depth or number of hidden layer
# num_input_units = X_train.shape[1] # number of input features/dimensions
# num_output_units = Y_train.shape[1]
# # X_train.shape, Y_train.shape

# net = GRU(D=num_input_units, H=num_hidden_units, L=num_hidden_layers, C=num_output_units)

# adam_rnn(nn=net, alpha=alpha, mb_size=time_step, n_iter=n_iter, n_files=n_files, print_after=print_after)

# # Display the learning curve and losses for training, validation, and testing
# # % matplotlib inline
# import matplotlib.pyplot as plt

# plt.plot(net.losses['train'], label='Train loss')
# plt.legend()
# plt.show()


    

    




/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-1 training loss: 3.2736
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-2 training loss: 3.2652
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-3 training loss: 3.3636
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-4 training loss: 3.4431
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-5 training loss: 3.4426
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-6 training loss: 3.4167
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-7 training loss: 3.4075
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-8 training loss: 3.3858
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Iter-9 training loss: 3.3576
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Iter-10 training loss: 3.3201
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Iter-11 training loss: 3.2571
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Iter-12 training loss: 3.1615
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Iter-13 training loss: 3.0356
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Iter-14 training loss: 2.8810
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Iter-15 training loss: 2.7081
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Iter-16 training loss: 2.5346
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Iter-17 training loss: 2.3752
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Iter-18 training loss: 2.2368
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Iter-19 training loss: 2.1189
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Iter-20 training loss: 2.0193
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Iter-21 training loss: 1.9356
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Iter-22 training loss: 1.8656
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/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-23 training loss: 1.8071
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-24 training loss: 1.7543
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Iter-25 training loss: 1.7036
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-26 training loss: 1.6548
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-27 training loss: 1.6050
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-28 training loss: 1.5524
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-29 training loss: 1.4958
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-30 training loss: 1.4363
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-31 training loss: 1.3785
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-32 training loss: 1.3262
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-33 training loss: 1.2816
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-34 training loss: 1.2420
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-35 training loss: 1.2062
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-36 training loss: 1.1739
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-37 training loss: 1.1452
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
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Iter-38 training loss: 1.1178
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-39 training loss: 1.0890
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-40 training loss: 1.0578
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-41 training loss: 1.0266
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-42 training loss: 1.0001
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-43 training loss: 0.9814
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-44 training loss: 0.9710
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-45 training loss: 0.9703
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-46 training loss: 0.9781
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-47 training loss: 0.9950
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-48 training loss: 1.0175
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-49 training loss: 1.0519
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-50 training loss: 1.0882
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-51 training loss: 1.1340
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-52 training loss: 1.1774
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-53 training loss: 1.2132
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-54 training loss: 1.2499
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-55 training loss: 1.2797
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-56 training loss: 1.3124
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
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Iter-57 training loss: 1.3405
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-58 training loss: 1.3627
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-59 training loss: 1.3759
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-60 training loss: 1.3808
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-61 training loss: 1.3836
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-62 training loss: 1.3740
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-63 training loss: 1.3578
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-64 training loss: 1.3356
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-65 training loss: 1.2979
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-66 training loss: 1.2666
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
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Iter-67 training loss: 1.2379
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-68 training loss: 1.1956
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-69 training loss: 1.1708
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-70 training loss: 1.1531
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-71 training loss: 1.1151
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-72 training loss: 1.0959
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-73 training loss: 1.0641
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-74 training loss: 1.0299
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-75 training loss: 1.0023
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-76 training loss: 0.9468
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-77 training loss: 0.9348
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-78 training loss: 0.8727
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-79 training loss: 0.8627
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-80 training loss: 0.8026
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-81 training loss: 0.8131
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-82 training loss: 0.8010
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-83 training loss: 0.7971
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-84 training loss: 0.7934
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-85 training loss: 0.8440
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-86 training loss: 0.8383
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-87 training loss: 0.8399
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-88 training loss: 0.7774
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-89 training loss: 0.8032
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-90 training loss: 0.7920
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-91 training loss: 0.7741
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-92 training loss: 0.8193
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-93 training loss: 0.8032
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-94 training loss: 0.7108
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-95 training loss: 0.7751
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-96 training loss: 0.7493
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-97 training loss: 0.8359
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-98 training loss: 0.7612
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-99 training loss: 0.6515
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_07_TYPE02.mat
Iter-100 training loss: 0.6842






    

In [43]:

    

# # hyper parameters
# n_iter = 100 # epochs: processing speed and how much time it takes.
# n_files = 6 # dataset size: maximum number of files in dataset based on 12 subjects
# print_after = 1 # print loss of train, valid, and test
# time_step = 100 # width of the model or minibatch size
# alpha = 1e-4 # 0.001 - This is set to 1/miniatch_size (mb_size) or num_time_steps
# num_hidden_units = 64 # width of the hidden layers or number of hidden units in hidden layer
# num_hidden_layers = 2 # depth or number of hidden layer
# num_input_units = X_train.shape[1] # number of input features/dimensions
# num_output_units = Y_train.shape[1]
# # X_train.shape, Y_train.shape

# net = GRU(D=num_input_units, H=num_hidden_units, L=num_hidden_layers, C=num_output_units)

# adam_rnn(nn=net, alpha=alpha, mb_size=time_step, n_iter=n_iter, n_files=n_files, print_after=print_after)

# # Display the learning curve and losses for training, validation, and testing
# # % matplotlib inline
# import matplotlib.pyplot as plt

# plt.plot(net.losses['train'], label='Train loss')
# plt.legend()
# plt.show()


    

    




/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-1 training loss: 5.3307
Iter-1 testing loss: 87.3933
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-2 training loss: 4.8822
Iter-2 testing loss: 74.9274
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-3 training loss: 4.9558
Iter-3 testing loss: 31.1253
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-4 training loss: 4.9154
Iter-4 testing loss: 30.2210
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-5 training loss: 4.8799
Iter-5 testing loss: 28.2631
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-6 training loss: 4.8463
Iter-6 testing loss: 33.8309
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-7 training loss: 4.8077
Iter-7 testing loss: 60.7721
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-8 training loss: 4.7767
Iter-8 testing loss: 55.7321
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-9 training loss: 4.7555
Iter-9 testing loss: 124.5915
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-10 training loss: 4.7473
Iter-10 testing loss: 95.7323
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-11 training loss: 4.7300
Iter-11 testing loss: 106.5296
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-12 training loss: 4.6998
Iter-12 testing loss: 108.2435
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-13 training loss: 4.6647
Iter-13 testing loss: 104.2152
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-14 training loss: 4.6308
Iter-14 testing loss: 100.8502
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-15 training loss: 4.6061
Iter-15 testing loss: 105.0795
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-16 training loss: 4.5987
Iter-16 testing loss: 106.0547
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-17 training loss: 4.6112
Iter-17 testing loss: 90.7526
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-18 training loss: 4.6427
Iter-18 testing loss: 51.6074
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-19 training loss: 4.6901
Iter-19 testing loss: 28.5532
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-20 training loss: 4.7488
Iter-20 testing loss: 75.3996
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-21 training loss: 4.8152
Iter-21 testing loss: 102.6117
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-22 training loss: 4.8865
Iter-22 testing loss: 98.4322
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-23 training loss: 4.9602
Iter-23 testing loss: 53.7473
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-24 training loss: 5.0389
Iter-24 testing loss: 55.6707
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-25 training loss: 5.1102
Iter-25 testing loss: 61.0456
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-26 training loss: 5.1934
Iter-26 testing loss: 63.6495
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-27 training loss: 5.2412
Iter-27 testing loss: 62.6180
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-28 training loss: 5.2945
Iter-28 testing loss: 58.9003
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-29 training loss: 5.3379
Iter-29 testing loss: 53.8730
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-30 training loss: 5.3343
Iter-30 testing loss: 49.4731
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-31 training loss: 5.3425
Iter-31 testing loss: 46.6692
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-32 training loss: 5.3243
Iter-32 testing loss: 49.7822
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-33 training loss: 5.3138
Iter-33 testing loss: 61.9155
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-34 training loss: 5.2782
Iter-34 testing loss: 66.7770
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-35 training loss: 5.2411
Iter-35 testing loss: 67.2818
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-36 training loss: 5.1865
Iter-36 testing loss: 64.5564
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-37 training loss: 5.1328
Iter-37 testing loss: 61.3080
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-38 training loss: 5.0827
Iter-38 testing loss: 55.7000
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-39 training loss: 5.0352
Iter-39 testing loss: 51.3197
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-40 training loss: 4.9550
Iter-40 testing loss: 54.1280
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-41 training loss: 4.8921
Iter-41 testing loss: 53.7926
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-42 training loss: 4.7952
Iter-42 testing loss: 50.6816
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-43 training loss: 4.6987
Iter-43 testing loss: 46.7894
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-44 training loss: 4.5702
Iter-44 testing loss: 38.5812
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-45 training loss: 4.3602
Iter-45 testing loss: 32.1605
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-46 training loss: 4.1509
Iter-46 testing loss: 34.2401
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-47 training loss: 3.9361
Iter-47 testing loss: 49.5730
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-48 training loss: 3.7739
Iter-48 testing loss: 52.3563
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-49 training loss: 3.5221
Iter-49 testing loss: 50.9594
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-50 training loss: 3.3469
Iter-50 testing loss: 85.3068
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-51 training loss: 3.1669
Iter-51 testing loss: 45.8832
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-52 training loss: 2.9549
Iter-52 testing loss: 41.1055
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-53 training loss: 2.7265
Iter-53 testing loss: 34.1863
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-54 training loss: 2.4433
Iter-54 testing loss: 46.2016
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-55 training loss: 2.2024
Iter-55 testing loss: 59.2332
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-56 training loss: 1.9275
Iter-56 testing loss: 69.1186
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-57 training loss: 1.7117
Iter-57 testing loss: 72.6399
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-58 training loss: 1.5319
Iter-58 testing loss: 74.6553
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-59 training loss: 1.2979
Iter-59 testing loss: 76.0868
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-60 training loss: 1.2517
Iter-60 testing loss: 70.9586
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-61 training loss: 1.1138
Iter-61 testing loss: 75.0610
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-62 training loss: 1.0723
Iter-62 testing loss: 66.0775
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-63 training loss: 0.9667
Iter-63 testing loss: 66.2043
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-64 training loss: 1.0075
Iter-64 testing loss: 66.3753
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-65 training loss: 0.9973
Iter-65 testing loss: 10.1871
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-66 training loss: 1.0112
Iter-66 testing loss: 32.6833
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-67 training loss: 0.8308
Iter-67 testing loss: 40.8817
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-68 training loss: 0.8693
Iter-68 testing loss: 53.0943
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-69 training loss: 0.9545
Iter-69 testing loss: 28.5214
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-70 training loss: 0.8902
Iter-70 testing loss: 11.3591
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-71 training loss: 0.9070
Iter-71 testing loss: 48.1257
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-72 training loss: 0.9291
Iter-72 testing loss: 58.8770
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-73 training loss: 0.7730
Iter-73 testing loss: 46.7029
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-74 training loss: 0.7393
Iter-74 testing loss: 53.4054
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-75 training loss: 0.8488
Iter-75 testing loss: 57.8985
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-76 training loss: 0.8596
Iter-76 testing loss: 38.0510
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-77 training loss: 0.7275
Iter-77 testing loss: 63.7255
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-78 training loss: 1.6380
Iter-78 testing loss: 42.5520
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-79 training loss: 0.6644
Iter-79 testing loss: 42.8454
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-80 training loss: 0.8367
Iter-80 testing loss: 54.6069
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-81 training loss: 0.6290
Iter-81 testing loss: 62.1604
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-82 training loss: 0.7953
Iter-82 testing loss: 16.0237
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-83 training loss: 0.5733
Iter-83 testing loss: 60.5856
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-84 training loss: 0.5395
Iter-84 testing loss: 62.8622
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-85 training loss: 0.7498
Iter-85 testing loss: 58.5963
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-86 training loss: 0.5602
Iter-86 testing loss: 56.2495
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-87 training loss: 0.6133
Iter-87 testing loss: 54.8682
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-88 training loss: 0.5232
Iter-88 testing loss: 61.3683
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-89 training loss: 0.4906
Iter-89 testing loss: 59.3029
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-90 training loss: 0.5236
Iter-90 testing loss: 58.4255
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-91 training loss: 0.5238
Iter-91 testing loss: 61.1586
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-92 training loss: 0.4830
Iter-92 testing loss: 55.5344
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-93 training loss: 0.4935
Iter-93 testing loss: 57.8791
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-94 training loss: 0.5267
Iter-94 testing loss: 48.4398
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-95 training loss: 0.5443
Iter-95 testing loss: 63.6270
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-96 training loss: 0.5230
Iter-96 testing loss: 50.8530
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-97 training loss: 1.2011
Iter-97 testing loss: 53.4605
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-98 training loss: 0.4713
Iter-98 testing loss: 40.7713
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-99 training loss: 0.5068
Iter-99 testing loss: 49.7724
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-100 training loss: 0.5846
Iter-100 testing loss: 58.7069






    

In [4]:

    

# # hyper parameters
# n_iter = 100 # epochs: processing speed and how much time it takes.
# n_files = 6 # dataset size: maximum number of files in dataset based on 12 subjects
# print_after = 1 # print loss of train, valid, and test
# time_step = 100 # width of the model or minibatch size
# alpha = 1e-4 # 0.001 - This is set to 1/miniatch_size (mb_size) or num_time_steps
# num_hidden_units = 64 # width of the hidden layers or number of hidden units in hidden layer
# num_hidden_layers = 1 # depth or number of hidden layer
# num_input_units = X_train.shape[1] # number of input features/dimensions
# num_output_units = Y_train.shape[1]
# # X_train.shape, Y_train.shape

# net = GRU(D=num_input_units, H=num_hidden_units, L=num_hidden_layers, C=num_output_units)

# adam_rnn(nn=net, alpha=alpha, mb_size=time_step, n_iter=n_iter, n_files=n_files, print_after=print_after)

# # Display the learning curve and losses for training, validation, and testing
# # % matplotlib inline
# import matplotlib.pyplot as plt

# plt.plot(net.losses['train'], label='Train loss')
# plt.legend()
# plt.show()


    

    




/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-1 training loss: 5.7932
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-2 training loss: 4.9003
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-3 training loss: 4.4744
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-4 training loss: 4.2778
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-5 training loss: 4.1928
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-6 training loss: 4.2458
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Iter-7 training loss: 4.3681
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Iter-8 training loss: 4.4814
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Iter-9 training loss: 4.5702
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Iter-10 training loss: 4.6315
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Iter-11 training loss: 4.6601
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Iter-12 training loss: 4.6653
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Iter-13 training loss: 4.6559
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Iter-14 training loss: 4.6366
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Iter-15 training loss: 4.6154
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Iter-16 training loss: 4.6018
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Iter-17 training loss: 4.5895
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Iter-18 training loss: 4.5760
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Iter-19 training loss: 4.5608
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Iter-20 training loss: 4.5452
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Iter-21 training loss: 4.5254
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Iter-22 training loss: 4.4988
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Iter-23 training loss: 4.4643
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Iter-24 training loss: 4.4190
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Iter-25 training loss: 4.3608
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Iter-26 training loss: 4.2903
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Iter-27 training loss: 4.2129
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Iter-28 training loss: 4.1348
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/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-29 training loss: 4.0595
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-30 training loss: 3.9889
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/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-31 training loss: 3.9237
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/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-32 training loss: 3.8620
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-33 training loss: 3.8009
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-34 training loss: 3.7376
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-35 training loss: 3.6713
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-36 training loss: 3.6046
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-37 training loss: 3.5421
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-38 training loss: 3.4864
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-39 training loss: 3.4385
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-40 training loss: 3.3974
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-41 training loss: 3.3618
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-42 training loss: 3.3309
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-43 training loss: 3.3045
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-44 training loss: 3.2805
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-45 training loss: 3.2581
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-46 training loss: 3.2391
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-47 training loss: 3.2221
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-48 training loss: 3.2052
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-49 training loss: 3.1834
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-50 training loss: 3.1570
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-51 training loss: 3.1303
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-52 training loss: 3.1021
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-53 training loss: 3.0662
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-54 training loss: 3.0137
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-55 training loss: 3.0152
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-56 training loss: 2.9785
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-57 training loss: 2.9740
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-58 training loss: 2.9559
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-59 training loss: 2.9459
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-60 training loss: 2.9331
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-61 training loss: 2.9551
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-62 training loss: 2.9193
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-63 training loss: 2.9520
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-64 training loss: 2.8879
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-65 training loss: 2.9125
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-66 training loss: 2.8711
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-67 training loss: 2.9170
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-68 training loss: 2.9021
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-69 training loss: 2.7893
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-70 training loss: 2.7779
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-71 training loss: 2.9487
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-72 training loss: 2.8822
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-73 training loss: 2.7162
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-74 training loss: 2.7255
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-75 training loss: 2.6833
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-76 training loss: 2.7013
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-77 training loss: 2.7183
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-78 training loss: 2.9080
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-79 training loss: 2.8872
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-80 training loss: 2.8243
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-81 training loss: 2.8278
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-82 training loss: 2.8645
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-83 training loss: 2.7185
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-84 training loss: 2.7854
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-85 training loss: 3.0684
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-86 training loss: 2.8251
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-87 training loss: 2.9653
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-88 training loss: 2.9300
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-89 training loss: 2.9718
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-90 training loss: 2.8919
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-91 training loss: 3.1095
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-92 training loss: 3.1390
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-93 training loss: 2.9064
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
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Iter-94 training loss: 2.8595
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-95 training loss: 3.0164
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-96 training loss: 3.0114
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-97 training loss: 2.8309
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-98 training loss: 2.9231
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-99 training loss: 3.1249
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_06_TYPE02.mat
Iter-100 training loss: 3.1948






    

In [5]:

    

# # hyper parameters
# n_iter = 100 # epochs: processing speed and how much time it takes.
# n_files = 5 # dataset size: maximum number of files in dataset based on 12 subjects
# print_after = 1 # print loss of train, valid, and test
# time_step = 100 # width of the model or minibatch size
# alpha = 1e-4 # 0.001 - This is set to 1/miniatch_size (mb_size) or num_time_steps
# num_hidden_units = 64 # width of the hidden layers or number of hidden units in hidden layer
# num_hidden_layers = 1 # depth or number of hidden layer
# num_input_units = X_train.shape[1] # number of input features/dimensions
# num_output_units = Y_train.shape[1]
# # X_train.shape, Y_train.shape

# net = GRU(D=num_input_units, H=num_hidden_units, L=num_hidden_layers, C=num_output_units)

# adam_rnn(nn=net, alpha=alpha, mb_size=time_step, n_iter=n_iter, n_files=n_files, print_after=print_after)

# # Display the learning curve and losses for training, validation, and testing
# # % matplotlib inline
# import matplotlib.pyplot as plt

# plt.plot(net.losses['train'], label='Train loss')
# plt.legend()
# plt.show()


    

    




/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-1 training loss: 2.9705
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-2 training loss: 2.2583
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-3 training loss: 1.9912
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-4 training loss: 1.8113
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-5 training loss: 1.6336
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-6 training loss: 1.5170
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-7 training loss: 1.4573
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-8 training loss: 1.4166
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-9 training loss: 1.3920
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-10 training loss: 1.3838
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-11 training loss: 1.3861
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-12 training loss: 1.3933
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-13 training loss: 1.4022
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-14 training loss: 1.4112
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-15 training loss: 1.4194
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-16 training loss: 1.4261
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-17 training loss: 1.4310
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-18 training loss: 1.4340
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-19 training loss: 1.4354
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Iter-20 training loss: 1.4359
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Iter-21 training loss: 1.4357
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Iter-22 training loss: 1.4351
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Iter-23 training loss: 1.4345
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Iter-24 training loss: 1.4338
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Iter-25 training loss: 1.4332
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Iter-26 training loss: 1.4326
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Iter-27 training loss: 1.4318
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Iter-28 training loss: 1.4307
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Iter-29 training loss: 1.4285
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Iter-30 training loss: 1.4246
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Iter-31 training loss: 1.4177
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Iter-32 training loss: 1.4058
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Iter-33 training loss: 1.3905
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Iter-34 training loss: 1.3869
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Iter-35 training loss: 1.3768
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Iter-36 training loss: 1.3531
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Iter-37 training loss: 1.3306
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Iter-38 training loss: 1.3099
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Iter-39 training loss: 1.2922
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Iter-40 training loss: 1.2775
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Iter-41 training loss: 1.2658
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Iter-42 training loss: 1.2563
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Iter-43 training loss: 1.2484
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Iter-44 training loss: 1.2415
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Iter-45 training loss: 1.2350
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Iter-46 training loss: 1.2287
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Iter-47 training loss: 1.2222
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Iter-48 training loss: 1.2153
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Iter-49 training loss: 1.2078
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Iter-50 training loss: 1.1997
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Iter-51 training loss: 1.1910
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Iter-52 training loss: 1.1816
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Iter-53 training loss: 1.1715
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Iter-54 training loss: 1.1612
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Iter-55 training loss: 1.1508
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-56 training loss: 1.1404
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-57 training loss: 1.1300
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-58 training loss: 1.1203
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-59 training loss: 1.1122
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-60 training loss: 1.1061
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-61 training loss: 1.1017
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-62 training loss: 1.0985
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-63 training loss: 1.0956
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-64 training loss: 1.0918
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-65 training loss: 1.0861
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-66 training loss: 1.0779
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-67 training loss: 1.0682
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-68 training loss: 1.0584
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-69 training loss: 1.0508
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-70 training loss: 1.0472
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-71 training loss: 1.0484
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-72 training loss: 1.0541
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-73 training loss: 1.0629
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-74 training loss: 1.0739
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-75 training loss: 1.0864
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-76 training loss: 1.1000
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-77 training loss: 1.1141
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-78 training loss: 1.1282
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-79 training loss: 1.1419
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-80 training loss: 1.1546
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-81 training loss: 1.1658
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-82 training loss: 1.1740
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
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Iter-83 training loss: 1.1800
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-84 training loss: 1.1824
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-85 training loss: 1.1808
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Iter-86 training loss: 1.1756
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Iter-87 training loss: 1.1679
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Iter-88 training loss: 1.1588
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Iter-89 training loss: 1.1488
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-90 training loss: 1.1388
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-91 training loss: 1.1295
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Iter-92 training loss: 1.1199
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-93 training loss: 1.1076
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-94 training loss: 1.0966
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Iter-95 training loss: 1.0770
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-96 training loss: 1.0687
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-97 training loss: 1.0456
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-98 training loss: 1.0188
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-99 training loss: 1.0138
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_05_TYPE02.mat
Iter-100 training loss: 0.9828






    

In [27]:

    

# # hyper parameters
# n_iter = 100 # epochs: processing speed and how much time it takes.
# n_files = 4 # dataset size: maximum number of files in dataset based on 12 subjects
# print_after = 1 # print loss of train, valid, and test
# time_step = 100 # width of the model or minibatch size
# alpha = 1e-4 # 0.001 - This is set to 1/miniatch_size (mb_size) or num_time_steps
# num_hidden_units = 64 # width of the hidden layers or number of hidden units in hidden layer
# num_hidden_layers = 1 # depth or number of hidden layer
# num_input_units = X_train.shape[1] # number of input features/dimensions
# num_output_units = Y_train.shape[1]
# # X_train.shape, Y_train.shape

# net = GRU(D=num_input_units, H=num_hidden_units, L=num_hidden_layers, C=num_output_units)

# adam_rnn(nn=net, alpha=alpha, mb_size=time_step, n_iter=n_iter, n_files=n_files, print_after=print_after)

# # Display the learning curve and losses for training, validation, and testing
# # % matplotlib inline
# import matplotlib.pyplot as plt

# plt.plot(net.losses['train'], label='Train loss')
# plt.legend()
# plt.show()


    

    




/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
Iter-1 training loss: 3.0640
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-2 training loss: 1.3098
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Iter-3 training loss: 0.9533
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-4 training loss: 0.8257
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-5 training loss: 0.7634
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Iter-6 training loss: 0.7315
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Iter-7 training loss: 0.7178
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Iter-8 training loss: 0.7147
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Iter-9 training loss: 0.7163
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Iter-10 training loss: 0.7185
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Iter-11 training loss: 0.7189
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Iter-12 training loss: 0.7160
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Iter-13 training loss: 0.7090
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Iter-14 training loss: 0.6980
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Iter-15 training loss: 0.6837
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Iter-16 training loss: 0.6669
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Iter-17 training loss: 0.6482
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Iter-18 training loss: 0.6285
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Iter-19 training loss: 0.6084
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Iter-20 training loss: 0.5887
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Iter-21 training loss: 0.5701
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Iter-22 training loss: 0.5532
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Iter-23 training loss: 0.5387
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Iter-24 training loss: 0.5267
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Iter-25 training loss: 0.5173
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Iter-26 training loss: 0.5102
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Iter-27 training loss: 0.5051
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Iter-28 training loss: 0.5013
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Iter-29 training loss: 0.4984
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Iter-30 training loss: 0.4958
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Iter-31 training loss: 0.4930
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Iter-32 training loss: 0.4896
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Iter-33 training loss: 0.4856
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-34 training loss: 0.4811
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-35 training loss: 0.4761
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-36 training loss: 0.4713
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Iter-37 training loss: 0.4668
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-38 training loss: 0.4631
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-39 training loss: 0.4604
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-40 training loss: 0.4589
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-41 training loss: 0.4587
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-42 training loss: 0.4596
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-43 training loss: 0.4617
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-44 training loss: 0.4647
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-45 training loss: 0.4682
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-46 training loss: 0.4719
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Iter-47 training loss: 0.4756
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Iter-48 training loss: 0.4792
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Iter-49 training loss: 0.4827
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Iter-50 training loss: 0.4860
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Iter-51 training loss: 0.4893
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Iter-52 training loss: 0.4927
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Iter-53 training loss: 0.4964
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Iter-54 training loss: 0.5006
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Iter-55 training loss: 0.5052
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Iter-56 training loss: 0.5101
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-57 training loss: 0.5151
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-58 training loss: 0.5202
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-59 training loss: 0.5256
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
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Iter-60 training loss: 0.5313
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Iter-61 training loss: 0.5371
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Iter-62 training loss: 0.5428
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Iter-63 training loss: 0.5506
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Iter-64 training loss: 0.5591
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Iter-65 training loss: 0.5650
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Iter-66 training loss: 0.5682
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-67 training loss: 0.5750
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Iter-68 training loss: 0.5785
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Iter-69 training loss: 0.5877
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Iter-70 training loss: 0.6026
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Iter-71 training loss: 0.6027
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Iter-72 training loss: 0.6056
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Iter-73 training loss: 0.6104
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Iter-74 training loss: 0.6196
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Iter-75 training loss: 0.6383
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Iter-76 training loss: 0.6500
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Iter-77 training loss: 0.6775
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Iter-78 training loss: 0.6932
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Iter-79 training loss: 0.7037
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Iter-80 training loss: 0.7185
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Iter-81 training loss: 0.6952
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Iter-82 training loss: 0.7775
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Iter-83 training loss: 0.7497
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Iter-84 training loss: 0.7530
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Iter-85 training loss: 0.7261
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Iter-86 training loss: 0.7589
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Iter-87 training loss: 0.7599
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Iter-88 training loss: 0.7082
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Iter-89 training loss: 0.6765
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Iter-90 training loss: 0.6544
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Iter-91 training loss: 0.6356
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Iter-92 training loss: 0.6334
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Iter-93 training loss: 0.6645
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Iter-94 training loss: 0.6222
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Iter-95 training loss: 0.6887
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Iter-96 training loss: 0.6415
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Iter-97 training loss: 0.6083
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Iter-98 training loss: 0.6725
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-99 training loss: 0.6303
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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/Users/arasdar/data/Training_data/DATA_04_TYPE02.mat
Iter-100 training loss: 0.6194






    

In [26]:

    

# # hyper parameters
# n_iter = 100 # epochs: processing speed and how much time it takes.
# n_files = 3 # dataset size: maximum number of files in dataset based on 12 subjects
# print_after = 1 # print loss of train, valid, and test
# time_step = 100 # width of the model or minibatch size
# alpha = 1e-4 # 0.001 - This is set to 1/miniatch_size (mb_size) or num_time_steps
# num_hidden_units = 64 # width of the hidden layers or number of hidden units in hidden layer
# num_hidden_layers = 1 # depth or number of hidden layer
# num_input_units = X_train.shape[1] # number of input features/dimensions
# num_output_units = Y_train.shape[1]
# # X_train.shape, Y_train.shape

# net = GRU(D=num_input_units, H=num_hidden_units, L=num_hidden_layers, C=num_output_units)

# adam_rnn(nn=net, alpha=alpha, mb_size=time_step, n_iter=n_iter, n_files=n_files, print_after=print_after)

# # Display the learning curve and losses for training, validation, and testing
# # % matplotlib inline
# import matplotlib.pyplot as plt

# plt.plot(net.losses['train'], label='Train loss')
# plt.legend()
# plt.show()


    

    




/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-1 training loss: 6.7396
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Iter-2 training loss: 2.3228
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Iter-3 training loss: 1.8399
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Iter-4 training loss: 1.7443
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Iter-5 training loss: 1.6975
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Iter-6 training loss: 1.6582
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Iter-7 training loss: 1.6209
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Iter-8 training loss: 1.5848
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Iter-9 training loss: 1.5524
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Iter-10 training loss: 1.5238
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Iter-11 training loss: 1.4988
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Iter-12 training loss: 1.4767
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Iter-13 training loss: 1.4572
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Iter-14 training loss: 1.4396
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Iter-15 training loss: 1.4234
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Iter-16 training loss: 1.4084
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Iter-17 training loss: 1.3944
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Iter-18 training loss: 1.3811
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Iter-19 training loss: 1.3684
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Iter-20 training loss: 1.3561
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Iter-21 training loss: 1.3441
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Iter-22 training loss: 1.3324
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Iter-23 training loss: 1.3208
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Iter-24 training loss: 1.3094
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Iter-25 training loss: 1.2978
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Iter-26 training loss: 1.2859
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Iter-27 training loss: 1.2734
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Iter-28 training loss: 1.2601
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Iter-29 training loss: 1.2457
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Iter-30 training loss: 1.2304
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Iter-31 training loss: 1.2144
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Iter-32 training loss: 1.1980
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Iter-33 training loss: 1.1814
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Iter-34 training loss: 1.1648
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Iter-35 training loss: 1.1482
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Iter-36 training loss: 1.1317
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Iter-37 training loss: 1.1154
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Iter-38 training loss: 1.0992
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Iter-39 training loss: 1.0833
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Iter-40 training loss: 1.0676
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Iter-41 training loss: 1.0521
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Iter-42 training loss: 1.0369
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Iter-43 training loss: 1.0219
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Iter-44 training loss: 1.0073
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Iter-45 training loss: 0.9929
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Iter-46 training loss: 0.9788
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Iter-47 training loss: 0.9650
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Iter-48 training loss: 0.9514
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-49 training loss: 0.9382
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-50 training loss: 0.9253
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-51 training loss: 0.9126
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-52 training loss: 0.9003
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
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Iter-53 training loss: 0.8884
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-54 training loss: 0.8769
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-55 training loss: 0.8658
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Iter-56 training loss: 0.8551
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-57 training loss: 0.8449
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-58 training loss: 0.8352
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-59 training loss: 0.8262
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-60 training loss: 0.8178
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Iter-61 training loss: 0.8101
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-62 training loss: 0.8033
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Iter-63 training loss: 0.7973
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Iter-64 training loss: 0.7924
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Iter-65 training loss: 0.7885
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-66 training loss: 0.7859
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-67 training loss: 0.7842
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Iter-68 training loss: 0.7836
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Iter-69 training loss: 0.7840
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Iter-70 training loss: 0.7851
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Iter-71 training loss: 0.7870
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Iter-72 training loss: 0.7900
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Iter-73 training loss: 0.7941
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Iter-74 training loss: 0.7987
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Iter-75 training loss: 0.8034
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Iter-76 training loss: 0.8085
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Iter-77 training loss: 0.8143
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Iter-78 training loss: 0.8208
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Iter-79 training loss: 0.8274
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Iter-80 training loss: 0.8323
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Iter-81 training loss: 0.8342
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Iter-82 training loss: 0.8382
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Iter-83 training loss: 0.8198
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Iter-84 training loss: 0.8221
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Iter-85 training loss: 0.8033
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Iter-86 training loss: 0.8060
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Iter-87 training loss: 0.7903
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Iter-88 training loss: 0.7808
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Iter-89 training loss: 0.7593
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Iter-90 training loss: 0.7581
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Iter-91 training loss: 0.7311
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Iter-92 training loss: 0.6543
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Iter-93 training loss: 0.7181
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Iter-94 training loss: 0.6986
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Iter-95 training loss: 0.6785
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Iter-96 training loss: 0.6935
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Iter-97 training loss: 0.6423
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-98 training loss: 0.6677
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
Iter-99 training loss: 0.6114
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
/Users/arasdar/data/Training_data/DATA_03_TYPE02.mat
Iter-100 training loss: 0.6319






    

In [22]:

    

# # hyper parameters
# n_iter = 100 # epochs: processing speed and how much time it takes.
# n_files = 2 # dataset size: maximum number of files in dataset based on 12 subjects
# print_after = 1 # print loss of train, valid, and test
# time_step = 100 # width of the model or minibatch size
# alpha = 1e-4 # 0.001 - This is set to 1/miniatch_size (mb_size) or num_time_steps
# num_hidden_units = 64 # width of the hidden layers or number of hidden units in hidden layer
# num_hidden_layers = 1 # depth or number of hidden layer
# num_input_units = X_train.shape[1] # number of input features/dimensions
# num_output_units = Y_train.shape[1]
# # X_train.shape, Y_train.shape

# net = GRU(D=num_input_units, H=num_hidden_units, L=num_hidden_layers, C=num_output_units)

# adam_rnn(nn=net, alpha=alpha, mb_size=time_step, n_iter=n_iter, n_files=n_files, print_after=print_after)

# # Display the learning curve and losses for training, validation, and testing
# # % matplotlib inline
# import matplotlib.pyplot as plt

# plt.plot(net.losses['train'], label='Train loss')
# plt.legend()
# plt.show()


    

    




/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-1 training loss: 10.8140
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-2 training loss: 10.5160
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Iter-3 training loss: 10.2500
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Iter-4 training loss: 10.1040
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Iter-5 training loss: 9.9796
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Iter-6 training loss: 9.8500
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Iter-7 training loss: 9.7225
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Iter-8 training loss: 9.6038
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Iter-9 training loss: 9.4975
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Iter-10 training loss: 9.4060
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Iter-11 training loss: 9.3313
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Iter-12 training loss: 9.2688
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Iter-13 training loss: 9.2105
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Iter-14 training loss: 9.1536
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Iter-15 training loss: 9.0971
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Iter-16 training loss: 9.0407
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-17 training loss: 8.9843
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Iter-18 training loss: 8.9278
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Iter-19 training loss: 8.8713
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Iter-20 training loss: 8.8150
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Iter-21 training loss: 8.7588
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Iter-22 training loss: 8.7028
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Iter-23 training loss: 8.6472
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Iter-24 training loss: 8.5921
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Iter-25 training loss: 8.5376
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Iter-26 training loss: 8.4837
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Iter-27 training loss: 8.4305
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Iter-28 training loss: 8.3782
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Iter-29 training loss: 8.3266
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Iter-30 training loss: 8.2754
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Iter-31 training loss: 8.2234
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Iter-32 training loss: 8.1687
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Iter-33 training loss: 8.1096
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Iter-34 training loss: 8.0452
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Iter-35 training loss: 7.9751
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Iter-36 training loss: 7.8997
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Iter-37 training loss: 7.8191
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Iter-38 training loss: 7.7333
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Iter-39 training loss: 7.6425
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Iter-40 training loss: 7.5464
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Iter-41 training loss: 7.4450
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Iter-42 training loss: 7.3383
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Iter-43 training loss: 7.2267
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Iter-44 training loss: 7.1108
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Iter-45 training loss: 6.9915
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Iter-46 training loss: 6.8697
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Iter-47 training loss: 6.7459
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Iter-48 training loss: 6.6203
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Iter-49 training loss: 6.4926
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-50 training loss: 6.3619
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-51 training loss: 6.2278
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-52 training loss: 6.0898
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-53 training loss: 5.9475
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-54 training loss: 5.8012
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-55 training loss: 5.6511
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Iter-56 training loss: 5.4980
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Iter-57 training loss: 5.3428
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Iter-58 training loss: 5.1866
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Iter-59 training loss: 5.0308
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-60 training loss: 4.8770
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-61 training loss: 4.7269
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-62 training loss: 4.5818
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
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Iter-63 training loss: 4.4423
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Iter-64 training loss: 4.3087
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-65 training loss: 4.1805
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-66 training loss: 4.0552
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-67 training loss: 3.9291
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-68 training loss: 3.8008
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-69 training loss: 3.6717
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-70 training loss: 3.5436
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-71 training loss: 3.4201
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-72 training loss: 3.3033
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-73 training loss: 3.1935
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-74 training loss: 3.0895
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-75 training loss: 2.9906
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-76 training loss: 2.8951
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-77 training loss: 2.8008
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-78 training loss: 2.7064
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-79 training loss: 2.6121
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-80 training loss: 2.5239
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-81 training loss: 2.4350
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-82 training loss: 2.3532
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-83 training loss: 2.3603
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-84 training loss: 2.1528
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-85 training loss: 2.8387
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-86 training loss: 2.0274
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-87 training loss: 1.9926
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-88 training loss: 1.9114
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-89 training loss: 1.8416
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-90 training loss: 2.1945
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-91 training loss: 1.6854
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/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-92 training loss: 1.8680
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Iter-93 training loss: 1.5541
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-94 training loss: 1.6198
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-95 training loss: 1.4408
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-96 training loss: 1.5098
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-97 training loss: 1.2900
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-98 training loss: 1.5185
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-99 training loss: 1.2093
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
/Users/arasdar/data/Training_data/DATA_02_TYPE02.mat
Iter-100 training loss: 1.1732






    

In [21]:

    

# # hyper parameters
# n_iter = 100 # epochs: processing speed and how much time it takes.
# n_files = 1 # dataset size: maximum number of files in dataset based on 12 subjects
# print_after = 1 # print loss of train, valid, and test
# time_step = 100 # width of the model or minibatch size
# alpha = 1e-4 # 0.001 - This is set to 1/miniatch_size (mb_size) or num_time_steps
# num_hidden_units = 64 # width of the hidden layers or number of hidden units in hidden layer
# num_hidden_layers = 1 # depth or number of hidden layer
# num_input_units = X_train.shape[1] # number of input features/dimensions
# num_output_units = Y_train.shape[1]
# # X_train.shape, Y_train.shape

# net = GRU(D=num_input_units, H=num_hidden_units, L=num_hidden_layers, C=num_output_units)

# adam_rnn(nn=net, alpha=alpha, mb_size=time_step, n_iter=n_iter, n_files=n_files, print_after=print_after)

# # Display the learning curve and losses for training, validation, and testing
# # % matplotlib inline
# import matplotlib.pyplot as plt

# plt.plot(net.losses['train'], label='Train loss')
# plt.legend()
# plt.show()


    

    




/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-1 training loss: 7.3380
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-2 training loss: 3.5217
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-3 training loss: 1.7416
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-4 training loss: 1.0132
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-5 training loss: 0.7681
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-6 training loss: 0.6669
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-7 training loss: 0.6093
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-8 training loss: 0.5717
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-9 training loss: 0.5470
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-10 training loss: 0.5315
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-11 training loss: 0.5225
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-12 training loss: 0.5180
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-13 training loss: 0.5168
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-14 training loss: 0.5179
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-15 training loss: 0.5206
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-16 training loss: 0.5246
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-17 training loss: 0.5294
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-18 training loss: 0.5349
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-19 training loss: 0.5409
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-20 training loss: 0.5473
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-21 training loss: 0.5540
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-22 training loss: 0.5610
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-23 training loss: 0.5682
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-24 training loss: 0.5756
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-25 training loss: 0.5834
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-26 training loss: 0.5915
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-27 training loss: 0.6001
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-28 training loss: 0.6091
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-29 training loss: 0.6186
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-30 training loss: 0.6287
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-31 training loss: 0.6392
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-32 training loss: 0.6502
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-33 training loss: 0.6615
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-34 training loss: 0.6732
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-35 training loss: 0.6852
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-36 training loss: 0.6974
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-37 training loss: 0.7097
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-38 training loss: 0.7222
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-39 training loss: 0.7348
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-40 training loss: 0.7476
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-41 training loss: 0.7605
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-42 training loss: 0.7738
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-43 training loss: 0.7875
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-44 training loss: 0.8018
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-45 training loss: 0.8168
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-46 training loss: 0.8325
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-47 training loss: 0.8490
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-48 training loss: 0.8662
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-49 training loss: 0.8841
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-50 training loss: 0.9024
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-51 training loss: 0.9213
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-52 training loss: 0.9405
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-53 training loss: 0.9602
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Iter-54 training loss: 0.9802
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-55 training loss: 1.0007
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Iter-56 training loss: 1.0215
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Iter-57 training loss: 1.0427
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Iter-58 training loss: 1.0642
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Iter-59 training loss: 1.0860
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Iter-60 training loss: 1.1079
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Iter-61 training loss: 1.1298
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-62 training loss: 1.1515
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Iter-63 training loss: 1.1725
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Iter-64 training loss: 1.1923
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Iter-65 training loss: 1.2105
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Iter-66 training loss: 1.2264
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Iter-67 training loss: 1.2393
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Iter-68 training loss: 1.2486
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Iter-69 training loss: 1.2540
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Iter-70 training loss: 1.2554
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-71 training loss: 1.2528
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-72 training loss: 1.2467
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-73 training loss: 1.2377
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Iter-74 training loss: 1.2260
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Iter-75 training loss: 1.2120
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-76 training loss: 1.1957
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-77 training loss: 1.1772
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-78 training loss: 1.1568
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-79 training loss: 1.1348
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-80 training loss: 1.1113
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-81 training loss: 1.0876
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-82 training loss: 1.0672
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-83 training loss: 1.0503
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-84 training loss: 1.0377
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-85 training loss: 1.0309
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-86 training loss: 1.0261
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-87 training loss: 1.0237
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-88 training loss: 1.0245
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-89 training loss: 1.0262
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-90 training loss: 1.0230
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-91 training loss: 1.0036
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-92 training loss: 0.9765
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-93 training loss: 0.9650
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-94 training loss: 0.9698
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-95 training loss: 0.9696
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-96 training loss: 0.9695
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-97 training loss: 0.9648
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-98 training loss: 0.9533
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-99 training loss: 0.9508
/Users/arasdar/data/Training_data/DATA_01_TYPE01.mat
Iter-100 training loss: 0.9249






    

In [ ]: