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In [3]:

    
import numpy as np
import math
import random
import pandas as pd
import os
import matplotlib.pyplot as plt
import cv2
import glob
from tqdm import tqdm
import pickle
import scipy.ndimage.interpolation as inter
from scipy.signal import medfilt 
from scipy.spatial.distance import cdist

from keras.optimizers import *
from keras.models import Model
from keras.layers import *
from keras.layers.core import *
from tensorflow.keras.callbacks import *
from keras.layers.convolutional import *
from keras import backend as K
import tensorflow as tf

import google.colab.files









    



Using TensorFlow backend.

Define configurations



In [0]:

    
random.seed(1234)

class Config():
    def __init__(self):
        self.frame_l = 32 # the length of frames
        self.joint_n = 15 # the number of joints
        self.joint_d = 2 # the dimension of joints
        self.clc_num = 21 # the number of class
        self.feat_d = 105
        self.filters = 64
C = Config()

Define data processing functions



In [0]:

    
# Temple resizing function
def zoom(p,target_l=64,joints_num=25,joints_dim=3):
    l = p.shape[0]
    p_new = np.empty([target_l,joints_num,joints_dim]) 
    for m in range(joints_num):
        for n in range(joints_dim):
            p_new[:,m,n] = medfilt(p_new[:,m,n],3)
            p_new[:,m,n] = inter.zoom(p[:,m,n],target_l/l)[:target_l]         
    return p_new



# Calculate JCD feature
def norm_scale(x):
    return (x-np.mean(x))/np.mean(x)
  
def get_CG(p,C):
    M = []
    iu = np.triu_indices(C.joint_n,1,C.joint_n)
    for f in range(C.frame_l): 
        d_m = cdist(p[f],p[f],'euclidean')       
        d_m = d_m[iu] 
        M.append(d_m)
    M = np.stack(M) 
    M = norm_scale(M)
    return M
  
  
# Genrate dataset  
def data_generator(T,C,le):
    X_0 = []
    X_1 = []
    X_2 = []
    Y = []
    limb_locs = [(0,2),(4,0),(3,0),(8,4),(7,3),(12,8),(11,7),(1,0),(6,1),(5,1),(10,6),(9,5),(14,10),(13,9)]
    print('len_t_pose:',len(T['pose']))
    for i in tqdm(range(len(T['pose']))): 
        p = np.copy(T['pose'][i])
        q = np.copy(p)
        q = np.pad(q,((0,0),(0,0),(0,1)), constant_values=0)
        qc = np.zeros((q.shape[0],q.shape[1]-1,q.shape[2]))
        normal_vec_pos = 0
        for j1,j2 in limb_locs:
            joint1_section = np.copy(q[:,j1,:])
            joint2_section = np.copy(q[:,j2,:])
            limb_mat = joint1_section - joint2_section
            obs_vec_mat = np.copy(joint1_section)
            #print('obs_vec_mat.size:',obs_vec_mat.size())
            #print('opx.size:',self.opx.size())
            obs_vec_mat[:,0] = obs_vec_mat[:,0] - 0.023900129681375948
            obs_vec_mat[:,1] = obs_vec_mat[:,1] - 0.37827336942214096
            obs_vec_mat[:,2] = obs_vec_mat[:,2] - 1
            qc[:,normal_vec_pos,:] = np.cross(limb_mat, obs_vec_mat, axis=1)
            normal_vec_pos += 1
        if i == 0:
            print('p.shape:',p.shape)
            print('q.shape:',q.shape)
        p = zoom(p,target_l=C.frame_l,joints_num=C.joint_n,joints_dim=C.joint_d)
        qcz = zoom(qc,target_l=C.frame_l,joints_num=C.joint_n-1,joints_dim=C.joint_d+1)
        if i == 0:
            print('p1.shape:',p.shape)
            print('q1.shape:',q.shape)
            print('qc.shape:',qc.shape)
            print('qcz.shape:',qcz.shape)
        label = np.zeros(C.clc_num)
        label[le.transform(T['label'])[i]-1] = 1   

        M = get_CG(p,C)
        if i == 0:
            print('M.shape:',M.shape)
        X_0.append(M)
        X_1.append(p)
        X_2.append(qcz)
        Y.append(label)

    X_0 = np.stack(X_0)  
    X_1 = np.stack(X_1) 
    X_2 = np.stack(X_2) 
    Y = np.stack(Y)
    return X_0,X_1,X_2,Y

Define network



In [0]:

    
def poses_diff(x):
    H, W = x.get_shape()[1],x.get_shape()[2]
    x = tf.subtract(x[:,1:,...],x[:,:-1,...])
    x = tf.compat.v1.image.resize_nearest_neighbor(x,size=[H,W],align_corners=False) # should not alignment here
    return x

def pose_motion(P,frame_l):
    #print('pose_motion: P.shape:',P.shape)
    P_diff_slow = Lambda(lambda x: poses_diff(x))(P)
    #print('pose_motion: P_diff_slow.shape:',P_diff_slow.shape)
    P_diff_slow = Reshape((frame_l,-1))(P_diff_slow)
    #print('pose_motion: P_diff_slow1.shape:',P_diff_slow.shape)
    P_fast = Lambda(lambda x: x[:,::2,...])(P)
    #print('pose_motion: P_fast.shape:',P_fast.shape)
    P_diff_fast = Lambda(lambda x: poses_diff(x))(P_fast)
    #print('pose_motion: P_diff_fast.shape:',P_diff_fast.shape)
    P_diff_fast = Reshape((int(frame_l/2),-1))(P_diff_fast)
    #print('pose_motion: P_diff_fast1.shape:',P_diff_fast.shape)
    return P_diff_slow,P_diff_fast
    
def c1D(x,filters,kernel):
    #print('c1D - x.shape:',x.shape)
    x = Conv1D(filters, kernel_size=kernel,padding='same',use_bias=False)(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    return x

def block(x,filters):
    x = c1D(x,filters,3)
    x = c1D(x,filters,3)
    return x
    
def d1D(x,filters):
    x = Dense(filters,use_bias=False)(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(alpha=0.2)(x)
    return x

def build_FM(frame_l=32,joint_n=22,joint_d=2,feat_d=231,filters=16):   
    M = Input(shape=(frame_l,feat_d))
    P = Input(shape=(frame_l,joint_n,joint_d))
    Q = Input(shape=(frame_l,14,3))
    
    diff_slow,diff_fast = pose_motion(P,frame_l)
    #print('M.s:',M.shape)
    
    x = c1D(M,filters*2,1)
    x = SpatialDropout1D(0.1)(x)
    x = c1D(x,filters,3)
    x = SpatialDropout1D(0.1)(x)
    x = c1D(x,filters,1)
    x = MaxPooling1D(2)(x)
    x = SpatialDropout1D(0.1)(x)

    #print('P.s:',P.shape)
    #print('diff_slow.s:',diff_slow.shape)
    
    x_d_slow = c1D(diff_slow,filters*2,1)
    x_d_slow = SpatialDropout1D(0.1)(x_d_slow)
    x_d_slow = c1D(x_d_slow,filters,3)
    x_d_slow = SpatialDropout1D(0.1)(x_d_slow)
    x_d_slow = c1D(x_d_slow,filters,1)
    x_d_slow = MaxPool1D(2)(x_d_slow)
    x_d_slow = SpatialDropout1D(0.1)(x_d_slow)

    #print('diff_fast.s:',diff_fast.shape)    
    x_d_fast = c1D(diff_fast,filters*2,1)
    x_d_fast = SpatialDropout1D(0.1)(x_d_fast)
    x_d_fast = c1D(x_d_fast,filters,3) 
    x_d_fast = SpatialDropout1D(0.1)(x_d_fast)
    x_d_fast = c1D(x_d_fast,filters,1) 
    x_d_fast = SpatialDropout1D(0.1)(x_d_fast)

    Q_reshaped = Reshape((frame_l,-1))(Q)
    x_cp = c1D(Q_reshaped,filters*2,1)
    x_cp = SpatialDropout1D(0.1)(x_cp)
    x_cp = c1D(x_cp,filters,3)
    x_cp = SpatialDropout1D(0.1)(x_cp)
    x_cp = c1D(x_cp,filters,1)
    x_cp = MaxPool1D(2)(x_cp)
    x_cp = SpatialDropout1D(0.1)(x_cp)
   
    x = concatenate([x,x_d_slow,x_d_fast, x_cp])
    #x1 = concatenate([x,x_d_slow,x_d_fast, x_cp])
    #print('mdl: x.shape:',x.shape,',x1.shape:', x1.shape)
    x = block(x,filters*2)
    x = MaxPool1D(2)(x)
    x = SpatialDropout1D(0.1)(x)
    
    x = block(x,filters*4)
    x = MaxPool1D(2)(x)
    x = SpatialDropout1D(0.1)(x)

    x = block(x,filters*8)
    x = SpatialDropout1D(0.1)(x)
    
    return Model(inputs=[M,P,Q],outputs=x)


def build_DD_Net(C):
    M = Input(name='M', shape=(C.frame_l,C.feat_d))  
    P = Input(name='P', shape=(C.frame_l,C.joint_n,C.joint_d)) 
    Q = Input(name='Q', shape=(C.frame_l,14,3))
    FM = build_FM(C.frame_l,C.joint_n,C.joint_d,C.feat_d,C.filters)
    
    x = FM([M,P,Q])

    x = GlobalMaxPool1D()(x)
    
    x = d1D(x,128)
    x = Dropout(0.5)(x)
    x = d1D(x,128)
    x = Dropout(0.5)(x)
    x = Dense(C.clc_num, activation='softmax')(x)
    
    ######################Self-supervised part
    model = Model(inputs=[M,P,Q],outputs=x)
    return model



In [28]:

    
DD_Net = build_DD_Net(C)
DD_Net.summary()









    



Model: "model_8"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
M (InputLayer)                  (None, 32, 105)      0                                            
__________________________________________________________________________________________________
P (InputLayer)                  (None, 32, 15, 2)    0                                            
__________________________________________________________________________________________________
Q (InputLayer)                  (None, 32, 14, 3)    0                                            
__________________________________________________________________________________________________
model_7 (Model)                 (None, 4, 512)       1774464     M[0][0]                          
                                                                 P[0][0]                          
                                                                 Q[0][0]                          
__________________________________________________________________________________________________
global_max_pooling1d_4 (GlobalM (None, 512)          0           model_7[1][0]                    
__________________________________________________________________________________________________
dense_10 (Dense)                (None, 128)          65536       global_max_pooling1d_4[0][0]     
__________________________________________________________________________________________________
batch_normalization_124 (BatchN (None, 128)          512         dense_10[0][0]                   
__________________________________________________________________________________________________
leaky_re_lu_124 (LeakyReLU)     (None, 128)          0           batch_normalization_124[0][0]    
__________________________________________________________________________________________________
dropout_7 (Dropout)             (None, 128)          0           leaky_re_lu_124[0][0]            
__________________________________________________________________________________________________
dense_11 (Dense)                (None, 128)          16384       dropout_7[0][0]                  
__________________________________________________________________________________________________
batch_normalization_125 (BatchN (None, 128)          512         dense_11[0][0]                   
__________________________________________________________________________________________________
leaky_re_lu_125 (LeakyReLU)     (None, 128)          0           batch_normalization_125[0][0]    
__________________________________________________________________________________________________
dropout_8 (Dropout)             (None, 128)          0           leaky_re_lu_125[0][0]            
__________________________________________________________________________________________________
dense_12 (Dense)                (None, 21)           2709        dropout_8[0][0]                  
==================================================================================================
Total params: 1,860,117
Trainable params: 1,853,973
Non-trainable params: 6,144
__________________________________________________________________________________________________

Load dataset (download GT_train_1.pkl and GT_test_1.pkl from github and then upload them )



In [10]:

    
uploaded = google.colab.files.upload()









    





     
     
      Upload widget is only available when the cell has been executed in the
      current browser session. Please rerun this cell to enable.
      
       






    



Saving GT_test_1.pkl to GT_test_1 (1).pkl
Saving GT_test_2.pkl to GT_test_2 (1).pkl
Saving GT_test_3.pkl to GT_test_3 (1).pkl
Saving GT_train_1.pkl to GT_train_1 (1).pkl
Saving GT_train_2.pkl to GT_train_2 (1).pkl






    



---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-10-d36dcde5bd00> in <module>()
----> 1 uploaded = google.colab.files.upload()

/usr/local/lib/python3.6/dist-packages/google/colab/files.py in upload()
     70     result = _output.eval_js(
     71         'google.colab._files._uploadFilesContinue("{output_id}")'.format(
---> 72             output_id=output_id))
     73     if result['action'] != 'append':
     74       # JS side uses a generator of promises to process all of the files- some

/usr/local/lib/python3.6/dist-packages/google/colab/output/_js.py in eval_js(script, ignore_result)
     37   if ignore_result:
     38     return
---> 39   return _message.read_reply_from_input(request_id)
     40 
     41 

/usr/local/lib/python3.6/dist-packages/google/colab/_message.py in read_reply_from_input(message_id, timeout_sec)
     99     reply = _read_next_input_message()
    100     if reply == _NOT_READY or not isinstance(reply, dict):
--> 101       time.sleep(0.025)
    102       continue
    103     if (reply.get('type') == 'colab_reply' and

KeyboardInterrupt:



In [29]:

    
Train = pickle.load(open("GT_train_1.pkl", "rb"))
Test = pickle.load(open("GT_test_1.pkl", "rb"))

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(Train['label'])

X_0,X_1,X_2,Y = data_generator(Train,C,le)
print('X_0.shape:',X_0.shape,', X_1.shape:',X_1.shape)
X_test_0,X_test_1,X_test_2,Y_test = data_generator(Test,C,le)









    



  2%|▏         | 12/660 [00:00<00:05, 110.57it/s]





    



len_t_pose: 660
p.shape: (40, 15, 2)
q.shape: (40, 15, 3)
p1.shape: (32, 15, 2)
q1.shape: (40, 15, 3)
qc.shape: (40, 14, 3)
qcz.shape: (32, 14, 3)
M.shape: (32, 105)






    



100%|██████████| 660/660 [00:06<00:00, 106.58it/s]
  4%|▍         | 11/268 [00:00<00:02, 107.52it/s]





    



X_0.shape: (660, 32, 105) , X_1.shape: (660, 32, 15, 2)
len_t_pose: 268
p.shape: (40, 15, 2)
q.shape: (40, 15, 3)
p1.shape: (32, 15, 2)
q1.shape: (40, 15, 3)
qc.shape: (40, 14, 3)
qcz.shape: (32, 14, 3)
M.shape: (32, 105)






    



100%|██████████| 268/268 [00:02<00:00, 107.00it/s]

Start train on split 1



In [30]:

    
import keras
lr = 1e-3
DD_Net.compile(loss="categorical_crossentropy",optimizer=adam(lr),metrics=['accuracy'])
lrScheduler = keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.5, patience=5, cooldown=5, min_lr=5e-6)
history = DD_Net.fit([X_0,X_1,X_2],Y,
                    batch_size=len(Y),
                    epochs=600,
                    verbose=True,
                    shuffle=True,
                    callbacks=[lrScheduler],
                    validation_data=([X_test_0,X_test_1,X_test_2],Y_test)      
                    )
lr = 1e-4
DD_Net.compile(loss="categorical_crossentropy",optimizer=adam(lr),metrics=['accuracy'])
lrScheduler = keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.5, patience=5, cooldown=5, min_lr=5e-6)
history = DD_Net.fit([X_0,X_1,X_2],Y,
                    batch_size=len(Y),
                    epochs=600,
                    verbose=True,
                    shuffle=True,
                    callbacks=[lrScheduler],
                    validation_data=([X_test_0,X_test_1,X_test_2],Y_test)      
                    )









    



Train on 660 samples, validate on 268 samples
Epoch 1/600
660/660 [==============================] - 9s 13ms/step - loss: 3.7223 - accuracy: 0.0621 - val_loss: 3.0433 - val_accuracy: 0.0448
Epoch 2/600
660/660 [==============================] - 4s 6ms/step - loss: 3.3440 - accuracy: 0.0955 - val_loss: 3.0422 - val_accuracy: 0.0485
Epoch 3/600
660/660 [==============================] - 4s 6ms/step - loss: 3.0942 - accuracy: 0.1015 - val_loss: 3.0402 - val_accuracy: 0.0410
Epoch 4/600
660/660 [==============================] - 4s 6ms/step - loss: 2.8194 - accuracy: 0.1727 - val_loss: 3.0365 - val_accuracy: 0.0933
Epoch 5/600
660/660 [==============================] - 4s 6ms/step - loss: 2.6696 - accuracy: 0.2364 - val_loss: 3.0321 - val_accuracy: 0.0933
Epoch 6/600
660/660 [==============================] - 4s 6ms/step - loss: 2.5008 - accuracy: 0.2712 - val_loss: 3.0277 - val_accuracy: 0.0858
Epoch 7/600
660/660 [==============================] - 4s 6ms/step - loss: 2.3881 - accuracy: 0.2788 - val_loss: 3.0224 - val_accuracy: 0.0709
Epoch 8/600
660/660 [==============================] - 4s 5ms/step - loss: 2.2501 - accuracy: 0.3379 - val_loss: 3.0159 - val_accuracy: 0.0522
Epoch 9/600
660/660 [==============================] - 4s 6ms/step - loss: 2.2361 - accuracy: 0.3121 - val_loss: 3.0080 - val_accuracy: 0.0522
Epoch 10/600
660/660 [==============================] - 4s 6ms/step - loss: 2.0735 - accuracy: 0.3652 - val_loss: 2.9991 - val_accuracy: 0.0634
Epoch 11/600
660/660 [==============================] - 4s 6ms/step - loss: 1.9921 - accuracy: 0.4061 - val_loss: 2.9889 - val_accuracy: 0.0858
Epoch 12/600
660/660 [==============================] - 4s 5ms/step - loss: 1.8647 - accuracy: 0.4364 - val_loss: 2.9786 - val_accuracy: 0.0970
Epoch 13/600
660/660 [==============================] - 4s 6ms/step - loss: 1.8584 - accuracy: 0.4530 - val_loss: 2.9668 - val_accuracy: 0.1082
Epoch 14/600
660/660 [==============================] - 4s 6ms/step - loss: 1.7079 - accuracy: 0.4864 - val_loss: 2.9554 - val_accuracy: 0.1231
Epoch 15/600
660/660 [==============================] - 4s 6ms/step - loss: 1.6728 - accuracy: 0.5152 - val_loss: 2.9448 - val_accuracy: 0.1418
Epoch 16/600
660/660 [==============================] - 4s 6ms/step - loss: 1.6356 - accuracy: 0.5136 - val_loss: 2.9343 - val_accuracy: 0.1455
Epoch 17/600
660/660 [==============================] - 4s 6ms/step - loss: 1.4639 - accuracy: 0.5788 - val_loss: 2.9242 - val_accuracy: 0.1716
Epoch 18/600
660/660 [==============================] - 4s 6ms/step - loss: 1.5051 - accuracy: 0.5424 - val_loss: 2.9154 - val_accuracy: 0.1754
Epoch 19/600
660/660 [==============================] - 4s 6ms/step - loss: 1.3667 - accuracy: 0.6212 - val_loss: 2.9064 - val_accuracy: 0.1754
Epoch 20/600
660/660 [==============================] - 4s 6ms/step - loss: 1.3653 - accuracy: 0.6030 - val_loss: 2.8963 - val_accuracy: 0.1828
Epoch 21/600
660/660 [==============================] - 4s 6ms/step - loss: 1.3182 - accuracy: 0.6152 - val_loss: 2.8878 - val_accuracy: 0.1828
Epoch 22/600
660/660 [==============================] - 4s 5ms/step - loss: 1.2665 - accuracy: 0.6621 - val_loss: 2.8782 - val_accuracy: 0.1828
Epoch 23/600
660/660 [==============================] - 4s 6ms/step - loss: 1.2140 - accuracy: 0.6515 - val_loss: 2.8691 - val_accuracy: 0.1866
Epoch 24/600
660/660 [==============================] - 4s 6ms/step - loss: 1.1614 - accuracy: 0.6909 - val_loss: 2.8599 - val_accuracy: 0.1866
Epoch 25/600
660/660 [==============================] - 4s 6ms/step - loss: 1.1095 - accuracy: 0.6833 - val_loss: 2.8520 - val_accuracy: 0.1866
Epoch 26/600
660/660 [==============================] - 4s 6ms/step - loss: 1.0760 - accuracy: 0.7182 - val_loss: 2.8436 - val_accuracy: 0.1791
Epoch 27/600
660/660 [==============================] - 4s 6ms/step - loss: 1.0202 - accuracy: 0.7106 - val_loss: 2.8359 - val_accuracy: 0.1679
Epoch 28/600
660/660 [==============================] - 4s 6ms/step - loss: 0.9984 - accuracy: 0.7394 - val_loss: 2.8303 - val_accuracy: 0.1828
Epoch 29/600
660/660 [==============================] - 4s 6ms/step - loss: 0.9974 - accuracy: 0.7121 - val_loss: 2.8281 - val_accuracy: 0.1866
Epoch 30/600
660/660 [==============================] - 4s 6ms/step - loss: 0.9446 - accuracy: 0.7500 - val_loss: 2.8253 - val_accuracy: 0.1940
Epoch 31/600
660/660 [==============================] - 4s 6ms/step - loss: 0.9331 - accuracy: 0.7485 - val_loss: 2.8220 - val_accuracy: 0.2015
Epoch 32/600
660/660 [==============================] - 4s 6ms/step - loss: 0.8551 - accuracy: 0.7727 - val_loss: 2.8133 - val_accuracy: 0.1978
Epoch 33/600
660/660 [==============================] - 4s 6ms/step - loss: 0.8812 - accuracy: 0.7652 - val_loss: 2.8065 - val_accuracy: 0.1978
Epoch 34/600
660/660 [==============================] - 4s 6ms/step - loss: 0.7876 - accuracy: 0.7833 - val_loss: 2.8034 - val_accuracy: 0.1940
Epoch 35/600
660/660 [==============================] - 4s 6ms/step - loss: 0.7843 - accuracy: 0.8045 - val_loss: 2.7983 - val_accuracy: 0.1978
Epoch 36/600
660/660 [==============================] - 4s 6ms/step - loss: 0.7204 - accuracy: 0.8273 - val_loss: 2.7935 - val_accuracy: 0.1866
Epoch 37/600
660/660 [==============================] - 4s 6ms/step - loss: 0.7177 - accuracy: 0.8273 - val_loss: 2.7915 - val_accuracy: 0.1903
Epoch 38/600
660/660 [==============================] - 4s 6ms/step - loss: 0.7401 - accuracy: 0.8197 - val_loss: 2.7905 - val_accuracy: 0.1791
Epoch 39/600
660/660 [==============================] - 4s 6ms/step - loss: 0.6601 - accuracy: 0.8439 - val_loss: 2.7893 - val_accuracy: 0.1866
Epoch 40/600
660/660 [==============================] - 4s 6ms/step - loss: 0.6689 - accuracy: 0.8470 - val_loss: 2.7865 - val_accuracy: 0.1903
Epoch 41/600
660/660 [==============================] - 4s 6ms/step - loss: 0.6452 - accuracy: 0.8394 - val_loss: 2.7827 - val_accuracy: 0.2127
Epoch 42/600
660/660 [==============================] - 4s 6ms/step - loss: 0.6449 - accuracy: 0.8470 - val_loss: 2.7863 - val_accuracy: 0.2201
Epoch 43/600
660/660 [==============================] - 4s 6ms/step - loss: 0.5989 - accuracy: 0.8530 - val_loss: 2.7982 - val_accuracy: 0.2201
Epoch 44/600
660/660 [==============================] - 4s 6ms/step - loss: 0.5551 - accuracy: 0.8712 - val_loss: 2.8139 - val_accuracy: 0.2164
Epoch 45/600
660/660 [==============================] - 4s 6ms/step - loss: 0.5772 - accuracy: 0.8742 - val_loss: 2.8270 - val_accuracy: 0.2127
Epoch 46/600
660/660 [==============================] - 4s 6ms/step - loss: 0.5153 - accuracy: 0.8879 - val_loss: 2.8462 - val_accuracy: 0.2015
Epoch 47/600
660/660 [==============================] - 4s 5ms/step - loss: 0.4992 - accuracy: 0.8924 - val_loss: 2.8725 - val_accuracy: 0.1940
Epoch 48/600
660/660 [==============================] - 4s 6ms/step - loss: 0.4818 - accuracy: 0.8955 - val_loss: 2.8982 - val_accuracy: 0.1791
Epoch 49/600
660/660 [==============================] - 4s 6ms/step - loss: 0.5081 - accuracy: 0.8894 - val_loss: 2.9204 - val_accuracy: 0.1754
Epoch 50/600
660/660 [==============================] - 4s 5ms/step - loss: 0.4488 - accuracy: 0.9106 - val_loss: 2.9383 - val_accuracy: 0.1642
Epoch 51/600
660/660 [==============================] - 4s 5ms/step - loss: 0.4399 - accuracy: 0.9106 - val_loss: 2.9490 - val_accuracy: 0.1642
Epoch 52/600
660/660 [==============================] - 4s 6ms/step - loss: 0.4336 - accuracy: 0.9121 - val_loss: 2.9551 - val_accuracy: 0.1642
Epoch 53/600
660/660 [==============================] - 4s 5ms/step - loss: 0.3916 - accuracy: 0.9182 - val_loss: 2.9669 - val_accuracy: 0.1604
Epoch 54/600
660/660 [==============================] - 4s 6ms/step - loss: 0.3705 - accuracy: 0.9242 - val_loss: 2.9869 - val_accuracy: 0.1679
Epoch 55/600
660/660 [==============================] - 4s 6ms/step - loss: 0.3803 - accuracy: 0.9288 - val_loss: 3.0166 - val_accuracy: 0.1716
Epoch 56/600
660/660 [==============================] - 4s 6ms/step - loss: 0.3874 - accuracy: 0.9288 - val_loss: 3.0530 - val_accuracy: 0.1679
Epoch 57/600
660/660 [==============================] - 4s 6ms/step - loss: 0.3563 - accuracy: 0.9379 - val_loss: 3.0871 - val_accuracy: 0.1679
Epoch 58/600
660/660 [==============================] - 4s 6ms/step - loss: 0.3547 - accuracy: 0.9273 - val_loss: 3.1290 - val_accuracy: 0.1642
Epoch 59/600
660/660 [==============================] - 4s 5ms/step - loss: 0.3200 - accuracy: 0.9379 - val_loss: 3.1662 - val_accuracy: 0.1642
Epoch 60/600
660/660 [==============================] - 4s 6ms/step - loss: 0.3118 - accuracy: 0.9530 - val_loss: 3.1977 - val_accuracy: 0.1716
Epoch 61/600
660/660 [==============================] - 4s 6ms/step - loss: 0.3182 - accuracy: 0.9424 - val_loss: 3.2188 - val_accuracy: 0.1754
Epoch 62/600
660/660 [==============================] - 4s 6ms/step - loss: 0.3023 - accuracy: 0.9379 - val_loss: 3.2325 - val_accuracy: 0.1828
Epoch 63/600
660/660 [==============================] - 4s 6ms/step - loss: 0.3006 - accuracy: 0.9515 - val_loss: 3.2579 - val_accuracy: 0.1828
Epoch 64/600
660/660 [==============================] - 4s 6ms/step - loss: 0.3107 - accuracy: 0.9470 - val_loss: 3.2828 - val_accuracy: 0.1791
Epoch 65/600
660/660 [==============================] - 4s 5ms/step - loss: 0.3066 - accuracy: 0.9424 - val_loss: 3.3057 - val_accuracy: 0.1828
Epoch 66/600
660/660 [==============================] - 4s 6ms/step - loss: 0.2627 - accuracy: 0.9515 - val_loss: 3.3386 - val_accuracy: 0.1940
Epoch 67/600
660/660 [==============================] - 4s 5ms/step - loss: 0.2786 - accuracy: 0.9515 - val_loss: 3.3781 - val_accuracy: 0.1903
Epoch 68/600
660/660 [==============================] - 4s 6ms/step - loss: 0.2309 - accuracy: 0.9788 - val_loss: 3.4228 - val_accuracy: 0.1866
Epoch 69/600
660/660 [==============================] - 4s 6ms/step - loss: 0.2724 - accuracy: 0.9515 - val_loss: 3.4600 - val_accuracy: 0.1903
Epoch 70/600
660/660 [==============================] - 4s 6ms/step - loss: 0.2714 - accuracy: 0.9485 - val_loss: 3.5162 - val_accuracy: 0.1791
Epoch 71/600
660/660 [==============================] - 4s 6ms/step - loss: 0.2162 - accuracy: 0.9742 - val_loss: 3.5712 - val_accuracy: 0.1791
Epoch 72/600
660/660 [==============================] - 4s 5ms/step - loss: 0.2309 - accuracy: 0.9636 - val_loss: 3.6066 - val_accuracy: 0.1828
Epoch 73/600
660/660 [==============================] - 4s 5ms/step - loss: 0.2403 - accuracy: 0.9606 - val_loss: 3.6473 - val_accuracy: 0.1828
Epoch 74/600
660/660 [==============================] - 4s 6ms/step - loss: 0.2123 - accuracy: 0.9697 - val_loss: 3.6734 - val_accuracy: 0.1716
Epoch 75/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1977 - accuracy: 0.9621 - val_loss: 3.6808 - val_accuracy: 0.1754
Epoch 76/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1921 - accuracy: 0.9788 - val_loss: 3.6793 - val_accuracy: 0.1828
Epoch 77/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1941 - accuracy: 0.9803 - val_loss: 3.6572 - val_accuracy: 0.1903
Epoch 78/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1730 - accuracy: 0.9848 - val_loss: 3.6463 - val_accuracy: 0.1978
Epoch 79/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1834 - accuracy: 0.9758 - val_loss: 3.6438 - val_accuracy: 0.2052
Epoch 80/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1928 - accuracy: 0.9773 - val_loss: 3.6416 - val_accuracy: 0.2090
Epoch 81/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1712 - accuracy: 0.9788 - val_loss: 3.6356 - val_accuracy: 0.2090
Epoch 82/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1851 - accuracy: 0.9788 - val_loss: 3.6493 - val_accuracy: 0.2164
Epoch 83/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1702 - accuracy: 0.9773 - val_loss: 3.6784 - val_accuracy: 0.2201
Epoch 84/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1497 - accuracy: 0.9848 - val_loss: 3.7167 - val_accuracy: 0.2127
Epoch 85/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1532 - accuracy: 0.9773 - val_loss: 3.7788 - val_accuracy: 0.2164
Epoch 86/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1546 - accuracy: 0.9788 - val_loss: 3.8159 - val_accuracy: 0.2015
Epoch 87/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1379 - accuracy: 0.9848 - val_loss: 3.8696 - val_accuracy: 0.2015
Epoch 88/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1406 - accuracy: 0.9758 - val_loss: 3.9288 - val_accuracy: 0.2015
Epoch 89/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1409 - accuracy: 0.9818 - val_loss: 3.9934 - val_accuracy: 0.1940
Epoch 90/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1384 - accuracy: 0.9833 - val_loss: 4.0310 - val_accuracy: 0.1940
Epoch 91/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1422 - accuracy: 0.9879 - val_loss: 4.0643 - val_accuracy: 0.1978
Epoch 92/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1181 - accuracy: 0.9939 - val_loss: 4.0947 - val_accuracy: 0.1978
Epoch 93/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1328 - accuracy: 0.9894 - val_loss: 4.1274 - val_accuracy: 0.1903
Epoch 94/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1416 - accuracy: 0.9879 - val_loss: 4.1588 - val_accuracy: 0.1903
Epoch 95/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1306 - accuracy: 0.9833 - val_loss: 4.1916 - val_accuracy: 0.1903
Epoch 96/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1179 - accuracy: 0.9848 - val_loss: 4.1963 - val_accuracy: 0.1940
Epoch 97/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1092 - accuracy: 0.9924 - val_loss: 4.1803 - val_accuracy: 0.2015
Epoch 98/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1111 - accuracy: 0.9864 - val_loss: 4.1521 - val_accuracy: 0.2015
Epoch 99/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1254 - accuracy: 0.9864 - val_loss: 4.1063 - val_accuracy: 0.2015
Epoch 100/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1194 - accuracy: 0.9879 - val_loss: 4.0846 - val_accuracy: 0.2127
Epoch 101/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1023 - accuracy: 0.9909 - val_loss: 4.0779 - val_accuracy: 0.2164
Epoch 102/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0935 - accuracy: 0.9909 - val_loss: 4.0807 - val_accuracy: 0.2164
Epoch 103/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1143 - accuracy: 0.9924 - val_loss: 4.1174 - val_accuracy: 0.2127
Epoch 104/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1218 - accuracy: 0.9848 - val_loss: 4.1649 - val_accuracy: 0.2127
Epoch 105/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1122 - accuracy: 0.9833 - val_loss: 4.2151 - val_accuracy: 0.2052
Epoch 106/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1067 - accuracy: 0.9848 - val_loss: 4.2953 - val_accuracy: 0.1978
Epoch 107/600
660/660 [==============================] - 4s 5ms/step - loss: 0.1045 - accuracy: 0.9909 - val_loss: 4.3492 - val_accuracy: 0.1903
Epoch 108/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0828 - accuracy: 0.9939 - val_loss: 4.3812 - val_accuracy: 0.1866
Epoch 109/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0926 - accuracy: 0.9924 - val_loss: 4.4084 - val_accuracy: 0.1866
Epoch 110/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0857 - accuracy: 0.9985 - val_loss: 4.4409 - val_accuracy: 0.1828
Epoch 111/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0881 - accuracy: 0.9924 - val_loss: 4.4742 - val_accuracy: 0.1828
Epoch 112/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0908 - accuracy: 0.9939 - val_loss: 4.5084 - val_accuracy: 0.1828
Epoch 113/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0941 - accuracy: 0.9894 - val_loss: 4.5379 - val_accuracy: 0.1791
Epoch 114/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0896 - accuracy: 0.9970 - val_loss: 4.5647 - val_accuracy: 0.1791
Epoch 115/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0806 - accuracy: 0.9939 - val_loss: 4.6037 - val_accuracy: 0.1754
Epoch 116/600
660/660 [==============================] - 4s 6ms/step - loss: 0.1082 - accuracy: 0.9833 - val_loss: 4.6159 - val_accuracy: 0.1754
Epoch 117/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0814 - accuracy: 0.9939 - val_loss: 4.6168 - val_accuracy: 0.1791
Epoch 118/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0864 - accuracy: 0.9894 - val_loss: 4.6049 - val_accuracy: 0.1828
Epoch 119/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0855 - accuracy: 0.9924 - val_loss: 4.5902 - val_accuracy: 0.1791
Epoch 120/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0866 - accuracy: 0.9955 - val_loss: 4.5740 - val_accuracy: 0.1828
Epoch 121/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0774 - accuracy: 0.9955 - val_loss: 4.5721 - val_accuracy: 0.1828
Epoch 122/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0775 - accuracy: 0.9939 - val_loss: 4.5671 - val_accuracy: 0.1828
Epoch 123/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0808 - accuracy: 0.9879 - val_loss: 4.5626 - val_accuracy: 0.1828
Epoch 124/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0817 - accuracy: 0.9909 - val_loss: 4.5632 - val_accuracy: 0.1903
Epoch 125/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0917 - accuracy: 0.9924 - val_loss: 4.5610 - val_accuracy: 0.1940
Epoch 126/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0743 - accuracy: 0.9985 - val_loss: 4.5600 - val_accuracy: 0.1940
Epoch 127/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0766 - accuracy: 1.0000 - val_loss: 4.5623 - val_accuracy: 0.1940
Epoch 128/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0928 - accuracy: 0.9879 - val_loss: 4.5650 - val_accuracy: 0.1978
Epoch 129/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0811 - accuracy: 0.9924 - val_loss: 4.5764 - val_accuracy: 0.1978
Epoch 130/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0793 - accuracy: 0.9970 - val_loss: 4.5857 - val_accuracy: 0.1978
Epoch 131/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0884 - accuracy: 0.9955 - val_loss: 4.5967 - val_accuracy: 0.1978
Epoch 132/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0823 - accuracy: 0.9970 - val_loss: 4.6044 - val_accuracy: 0.1978
Epoch 133/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0848 - accuracy: 0.9939 - val_loss: 4.6136 - val_accuracy: 0.1978
Epoch 134/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0751 - accuracy: 0.9955 - val_loss: 4.6200 - val_accuracy: 0.1978
Epoch 135/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0780 - accuracy: 0.9939 - val_loss: 4.6261 - val_accuracy: 0.1978
Epoch 136/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0720 - accuracy: 0.9939 - val_loss: 4.6339 - val_accuracy: 0.1978
Epoch 137/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0737 - accuracy: 0.9924 - val_loss: 4.6401 - val_accuracy: 0.1978
Epoch 138/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0735 - accuracy: 0.9970 - val_loss: 4.6474 - val_accuracy: 0.1978
Epoch 139/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0656 - accuracy: 0.9985 - val_loss: 4.6518 - val_accuracy: 0.1978
Epoch 140/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0728 - accuracy: 0.9970 - val_loss: 4.6587 - val_accuracy: 0.2015
Epoch 141/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0793 - accuracy: 0.9970 - val_loss: 4.6614 - val_accuracy: 0.2052
Epoch 142/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0785 - accuracy: 0.9955 - val_loss: 4.6649 - val_accuracy: 0.2052
Epoch 143/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0679 - accuracy: 1.0000 - val_loss: 4.6682 - val_accuracy: 0.2052
Epoch 144/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0737 - accuracy: 1.0000 - val_loss: 4.6706 - val_accuracy: 0.2052
Epoch 145/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0700 - accuracy: 0.9939 - val_loss: 4.6725 - val_accuracy: 0.2052
Epoch 146/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0720 - accuracy: 0.9955 - val_loss: 4.6718 - val_accuracy: 0.2052
Epoch 147/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0766 - accuracy: 0.9970 - val_loss: 4.6722 - val_accuracy: 0.2052
Epoch 148/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0662 - accuracy: 1.0000 - val_loss: 4.6723 - val_accuracy: 0.2052
Epoch 149/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0833 - accuracy: 0.9939 - val_loss: 4.6730 - val_accuracy: 0.2090
Epoch 150/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0707 - accuracy: 0.9955 - val_loss: 4.6718 - val_accuracy: 0.2090
Epoch 151/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0745 - accuracy: 0.9955 - val_loss: 4.6696 - val_accuracy: 0.2127
Epoch 152/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0875 - accuracy: 0.9848 - val_loss: 4.6682 - val_accuracy: 0.2127
Epoch 153/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0692 - accuracy: 0.9955 - val_loss: 4.6659 - val_accuracy: 0.2127
Epoch 154/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0851 - accuracy: 0.9970 - val_loss: 4.6655 - val_accuracy: 0.2127
Epoch 155/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0799 - accuracy: 0.9924 - val_loss: 4.6628 - val_accuracy: 0.2127
Epoch 156/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0706 - accuracy: 0.9970 - val_loss: 4.6614 - val_accuracy: 0.2090
Epoch 157/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0730 - accuracy: 0.9924 - val_loss: 4.6594 - val_accuracy: 0.2052
Epoch 158/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0719 - accuracy: 0.9955 - val_loss: 4.6564 - val_accuracy: 0.2052
Epoch 159/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0887 - accuracy: 0.9894 - val_loss: 4.6537 - val_accuracy: 0.2052
Epoch 160/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0748 - accuracy: 0.9955 - val_loss: 4.6500 - val_accuracy: 0.2052
Epoch 161/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0691 - accuracy: 0.9939 - val_loss: 4.6469 - val_accuracy: 0.2052
Epoch 162/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0737 - accuracy: 0.9955 - val_loss: 4.6450 - val_accuracy: 0.2052
Epoch 163/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0823 - accuracy: 0.9955 - val_loss: 4.6430 - val_accuracy: 0.2052
Epoch 164/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0795 - accuracy: 0.9924 - val_loss: 4.6395 - val_accuracy: 0.2052
Epoch 165/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0670 - accuracy: 0.9970 - val_loss: 4.6360 - val_accuracy: 0.2052
Epoch 166/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0761 - accuracy: 0.9939 - val_loss: 4.6323 - val_accuracy: 0.2052
Epoch 167/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0861 - accuracy: 0.9924 - val_loss: 4.6288 - val_accuracy: 0.2090
Epoch 168/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0824 - accuracy: 0.9970 - val_loss: 4.6246 - val_accuracy: 0.2090
Epoch 169/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0694 - accuracy: 0.9985 - val_loss: 4.6204 - val_accuracy: 0.2090
Epoch 170/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0790 - accuracy: 0.9939 - val_loss: 4.6170 - val_accuracy: 0.2090
Epoch 171/600
660/660 [==============================] - 3s 5ms/step - loss: 0.0762 - accuracy: 0.9939 - val_loss: 4.6115 - val_accuracy: 0.2127
Epoch 172/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0721 - accuracy: 0.9955 - val_loss: 4.6077 - val_accuracy: 0.2164
Epoch 173/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0746 - accuracy: 0.9955 - val_loss: 4.6038 - val_accuracy: 0.2164
Epoch 174/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0622 - accuracy: 0.9985 - val_loss: 4.6002 - val_accuracy: 0.2164
Epoch 175/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0735 - accuracy: 0.9970 - val_loss: 4.5944 - val_accuracy: 0.2164
Epoch 176/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0695 - accuracy: 0.9924 - val_loss: 4.5896 - val_accuracy: 0.2164
Epoch 177/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0714 - accuracy: 0.9970 - val_loss: 4.5851 - val_accuracy: 0.2164
Epoch 178/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0772 - accuracy: 0.9955 - val_loss: 4.5810 - val_accuracy: 0.2164
Epoch 179/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0734 - accuracy: 0.9894 - val_loss: 4.5750 - val_accuracy: 0.2164
Epoch 180/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0674 - accuracy: 0.9970 - val_loss: 4.5697 - val_accuracy: 0.2164
Epoch 181/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0722 - accuracy: 0.9955 - val_loss: 4.5649 - val_accuracy: 0.2164
Epoch 182/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0621 - accuracy: 1.0000 - val_loss: 4.5594 - val_accuracy: 0.2201
Epoch 183/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0664 - accuracy: 0.9985 - val_loss: 4.5545 - val_accuracy: 0.2201
Epoch 184/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0622 - accuracy: 0.9985 - val_loss: 4.5484 - val_accuracy: 0.2201
Epoch 185/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0744 - accuracy: 0.9939 - val_loss: 4.5424 - val_accuracy: 0.2201
Epoch 186/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0711 - accuracy: 0.9985 - val_loss: 4.5356 - val_accuracy: 0.2201
Epoch 187/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0653 - accuracy: 0.9985 - val_loss: 4.5295 - val_accuracy: 0.2201
Epoch 188/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0795 - accuracy: 0.9939 - val_loss: 4.5240 - val_accuracy: 0.2201
Epoch 189/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0702 - accuracy: 0.9955 - val_loss: 4.5169 - val_accuracy: 0.2201
Epoch 190/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0813 - accuracy: 0.9985 - val_loss: 4.5112 - val_accuracy: 0.2201
Epoch 191/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0761 - accuracy: 0.9970 - val_loss: 4.5045 - val_accuracy: 0.2239
Epoch 192/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0661 - accuracy: 0.9939 - val_loss: 4.4980 - val_accuracy: 0.2239
Epoch 193/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0768 - accuracy: 0.9970 - val_loss: 4.4920 - val_accuracy: 0.2239
Epoch 194/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0730 - accuracy: 0.9924 - val_loss: 4.4860 - val_accuracy: 0.2239
Epoch 195/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0694 - accuracy: 0.9970 - val_loss: 4.4800 - val_accuracy: 0.2239
Epoch 196/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0691 - accuracy: 0.9985 - val_loss: 4.4733 - val_accuracy: 0.2239
Epoch 197/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0685 - accuracy: 0.9970 - val_loss: 4.4673 - val_accuracy: 0.2239
Epoch 198/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0713 - accuracy: 0.9924 - val_loss: 4.4605 - val_accuracy: 0.2239
Epoch 199/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0629 - accuracy: 0.9985 - val_loss: 4.4538 - val_accuracy: 0.2239
Epoch 200/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0699 - accuracy: 0.9939 - val_loss: 4.4467 - val_accuracy: 0.2239
Epoch 201/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0759 - accuracy: 0.9939 - val_loss: 4.4399 - val_accuracy: 0.2239
Epoch 202/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0782 - accuracy: 0.9924 - val_loss: 4.4322 - val_accuracy: 0.2239
Epoch 203/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0752 - accuracy: 0.9924 - val_loss: 4.4244 - val_accuracy: 0.2239
Epoch 204/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0722 - accuracy: 0.9939 - val_loss: 4.4162 - val_accuracy: 0.2239
Epoch 205/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0663 - accuracy: 0.9955 - val_loss: 4.4075 - val_accuracy: 0.2239
Epoch 206/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0682 - accuracy: 0.9985 - val_loss: 4.3996 - val_accuracy: 0.2239
Epoch 207/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0779 - accuracy: 0.9939 - val_loss: 4.3911 - val_accuracy: 0.2239
Epoch 208/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0736 - accuracy: 0.9970 - val_loss: 4.3830 - val_accuracy: 0.2239
Epoch 209/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0778 - accuracy: 0.9955 - val_loss: 4.3750 - val_accuracy: 0.2276
Epoch 210/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0769 - accuracy: 0.9924 - val_loss: 4.3674 - val_accuracy: 0.2276
Epoch 211/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0769 - accuracy: 0.9955 - val_loss: 4.3596 - val_accuracy: 0.2276
Epoch 212/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0797 - accuracy: 0.9924 - val_loss: 4.3515 - val_accuracy: 0.2276
Epoch 213/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0697 - accuracy: 0.9955 - val_loss: 4.3428 - val_accuracy: 0.2276
Epoch 214/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0650 - accuracy: 0.9970 - val_loss: 4.3345 - val_accuracy: 0.2276
Epoch 215/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0742 - accuracy: 0.9970 - val_loss: 4.3247 - val_accuracy: 0.2276
Epoch 216/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0721 - accuracy: 0.9970 - val_loss: 4.3163 - val_accuracy: 0.2276
Epoch 217/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0652 - accuracy: 1.0000 - val_loss: 4.3072 - val_accuracy: 0.2276
Epoch 218/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0767 - accuracy: 0.9970 - val_loss: 4.2979 - val_accuracy: 0.2276
Epoch 219/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0785 - accuracy: 0.9939 - val_loss: 4.2889 - val_accuracy: 0.2276
Epoch 220/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0869 - accuracy: 0.9879 - val_loss: 4.2801 - val_accuracy: 0.2276
Epoch 221/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0650 - accuracy: 0.9985 - val_loss: 4.2708 - val_accuracy: 0.2276
Epoch 222/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0735 - accuracy: 0.9924 - val_loss: 4.2618 - val_accuracy: 0.2276
Epoch 223/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0709 - accuracy: 1.0000 - val_loss: 4.2536 - val_accuracy: 0.2276
Epoch 224/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0682 - accuracy: 0.9985 - val_loss: 4.2434 - val_accuracy: 0.2276
Epoch 225/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0683 - accuracy: 0.9955 - val_loss: 4.2336 - val_accuracy: 0.2276
Epoch 226/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0807 - accuracy: 0.9924 - val_loss: 4.2249 - val_accuracy: 0.2276
Epoch 227/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0658 - accuracy: 0.9970 - val_loss: 4.2151 - val_accuracy: 0.2313
Epoch 228/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0676 - accuracy: 0.9939 - val_loss: 4.2050 - val_accuracy: 0.2351
Epoch 229/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0714 - accuracy: 0.9955 - val_loss: 4.1950 - val_accuracy: 0.2351
Epoch 230/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0706 - accuracy: 0.9985 - val_loss: 4.1845 - val_accuracy: 0.2351
Epoch 231/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0723 - accuracy: 0.9985 - val_loss: 4.1749 - val_accuracy: 0.2351
Epoch 232/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0786 - accuracy: 0.9955 - val_loss: 4.1649 - val_accuracy: 0.2351
Epoch 233/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0720 - accuracy: 0.9970 - val_loss: 4.1541 - val_accuracy: 0.2351
Epoch 234/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0654 - accuracy: 0.9970 - val_loss: 4.1433 - val_accuracy: 0.2351
Epoch 235/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0672 - accuracy: 0.9985 - val_loss: 4.1323 - val_accuracy: 0.2351
Epoch 236/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0671 - accuracy: 0.9970 - val_loss: 4.1222 - val_accuracy: 0.2388
Epoch 237/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0895 - accuracy: 0.9879 - val_loss: 4.1119 - val_accuracy: 0.2388
Epoch 238/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0682 - accuracy: 0.9955 - val_loss: 4.1014 - val_accuracy: 0.2388
Epoch 239/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0767 - accuracy: 0.9924 - val_loss: 4.0897 - val_accuracy: 0.2388
Epoch 240/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0613 - accuracy: 0.9955 - val_loss: 4.0783 - val_accuracy: 0.2388
Epoch 241/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0752 - accuracy: 0.9955 - val_loss: 4.0670 - val_accuracy: 0.2388
Epoch 242/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0844 - accuracy: 0.9848 - val_loss: 4.0555 - val_accuracy: 0.2388
Epoch 243/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0705 - accuracy: 0.9924 - val_loss: 4.0448 - val_accuracy: 0.2388
Epoch 244/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0847 - accuracy: 0.9924 - val_loss: 4.0342 - val_accuracy: 0.2388
Epoch 245/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0665 - accuracy: 0.9970 - val_loss: 4.0235 - val_accuracy: 0.2388
Epoch 246/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0736 - accuracy: 0.9985 - val_loss: 4.0127 - val_accuracy: 0.2425
Epoch 247/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0825 - accuracy: 0.9955 - val_loss: 4.0014 - val_accuracy: 0.2425
Epoch 248/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0728 - accuracy: 0.9970 - val_loss: 3.9899 - val_accuracy: 0.2425
Epoch 249/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0850 - accuracy: 0.9894 - val_loss: 3.9774 - val_accuracy: 0.2425
Epoch 250/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0681 - accuracy: 0.9985 - val_loss: 3.9662 - val_accuracy: 0.2425
Epoch 251/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0674 - accuracy: 0.9939 - val_loss: 3.9536 - val_accuracy: 0.2425
Epoch 252/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0769 - accuracy: 0.9924 - val_loss: 3.9423 - val_accuracy: 0.2425
Epoch 253/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0842 - accuracy: 0.9879 - val_loss: 3.9306 - val_accuracy: 0.2425
Epoch 254/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0746 - accuracy: 0.9985 - val_loss: 3.9183 - val_accuracy: 0.2425
Epoch 255/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0692 - accuracy: 0.9955 - val_loss: 3.9059 - val_accuracy: 0.2425
Epoch 256/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0775 - accuracy: 0.9985 - val_loss: 3.8937 - val_accuracy: 0.2425
Epoch 257/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0701 - accuracy: 0.9970 - val_loss: 3.8822 - val_accuracy: 0.2425
Epoch 258/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0806 - accuracy: 0.9955 - val_loss: 3.8705 - val_accuracy: 0.2425
Epoch 259/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0783 - accuracy: 0.9924 - val_loss: 3.8587 - val_accuracy: 0.2425
Epoch 260/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0692 - accuracy: 0.9955 - val_loss: 3.8465 - val_accuracy: 0.2425
Epoch 261/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0734 - accuracy: 0.9955 - val_loss: 3.8342 - val_accuracy: 0.2425
Epoch 262/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0817 - accuracy: 0.9955 - val_loss: 3.8223 - val_accuracy: 0.2425
Epoch 263/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0733 - accuracy: 0.9970 - val_loss: 3.8104 - val_accuracy: 0.2425
Epoch 264/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0694 - accuracy: 0.9985 - val_loss: 3.7984 - val_accuracy: 0.2500
Epoch 265/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0745 - accuracy: 0.9970 - val_loss: 3.7862 - val_accuracy: 0.2500
Epoch 266/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0837 - accuracy: 0.9939 - val_loss: 3.7747 - val_accuracy: 0.2500
Epoch 267/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0702 - accuracy: 0.9955 - val_loss: 3.7625 - val_accuracy: 0.2500
Epoch 268/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0713 - accuracy: 0.9970 - val_loss: 3.7508 - val_accuracy: 0.2500
Epoch 269/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0699 - accuracy: 0.9970 - val_loss: 3.7393 - val_accuracy: 0.2500
Epoch 270/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0798 - accuracy: 0.9924 - val_loss: 3.7282 - val_accuracy: 0.2500
Epoch 271/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0783 - accuracy: 0.9909 - val_loss: 3.7159 - val_accuracy: 0.2537
Epoch 272/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0763 - accuracy: 0.9924 - val_loss: 3.7044 - val_accuracy: 0.2537
Epoch 273/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0673 - accuracy: 0.9955 - val_loss: 3.6922 - val_accuracy: 0.2575
Epoch 274/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0833 - accuracy: 0.9909 - val_loss: 3.6804 - val_accuracy: 0.2575
Epoch 275/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0706 - accuracy: 0.9955 - val_loss: 3.6679 - val_accuracy: 0.2575
Epoch 276/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0719 - accuracy: 1.0000 - val_loss: 3.6566 - val_accuracy: 0.2575
Epoch 277/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0714 - accuracy: 0.9985 - val_loss: 3.6461 - val_accuracy: 0.2649
Epoch 278/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0802 - accuracy: 0.9879 - val_loss: 3.6342 - val_accuracy: 0.2649
Epoch 279/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0712 - accuracy: 0.9970 - val_loss: 3.6227 - val_accuracy: 0.2649
Epoch 280/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0639 - accuracy: 0.9970 - val_loss: 3.6106 - val_accuracy: 0.2649
Epoch 281/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0696 - accuracy: 0.9955 - val_loss: 3.5984 - val_accuracy: 0.2724
Epoch 282/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0722 - accuracy: 0.9955 - val_loss: 3.5864 - val_accuracy: 0.2724
Epoch 283/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0726 - accuracy: 0.9939 - val_loss: 3.5738 - val_accuracy: 0.2724
Epoch 284/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0765 - accuracy: 0.9939 - val_loss: 3.5619 - val_accuracy: 0.2761
Epoch 285/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0694 - accuracy: 0.9970 - val_loss: 3.5500 - val_accuracy: 0.2799
Epoch 286/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0862 - accuracy: 0.9894 - val_loss: 3.5382 - val_accuracy: 0.2799
Epoch 287/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0697 - accuracy: 0.9924 - val_loss: 3.5260 - val_accuracy: 0.2799
Epoch 288/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0726 - accuracy: 0.9955 - val_loss: 3.5145 - val_accuracy: 0.2836
Epoch 289/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0733 - accuracy: 0.9939 - val_loss: 3.5026 - val_accuracy: 0.2836
Epoch 290/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0666 - accuracy: 0.9970 - val_loss: 3.4903 - val_accuracy: 0.2836
Epoch 291/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0693 - accuracy: 0.9985 - val_loss: 3.4784 - val_accuracy: 0.2836
Epoch 292/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0639 - accuracy: 0.9970 - val_loss: 3.4660 - val_accuracy: 0.2836
Epoch 293/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0822 - accuracy: 0.9939 - val_loss: 3.4530 - val_accuracy: 0.2873
Epoch 294/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0783 - accuracy: 0.9939 - val_loss: 3.4415 - val_accuracy: 0.2873
Epoch 295/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0713 - accuracy: 0.9939 - val_loss: 3.4290 - val_accuracy: 0.2873
Epoch 296/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0744 - accuracy: 0.9970 - val_loss: 3.4161 - val_accuracy: 0.2948
Epoch 297/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0745 - accuracy: 0.9955 - val_loss: 3.4030 - val_accuracy: 0.2985
Epoch 298/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0659 - accuracy: 0.9970 - val_loss: 3.3906 - val_accuracy: 0.2985
Epoch 299/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0668 - accuracy: 0.9970 - val_loss: 3.3782 - val_accuracy: 0.3060
Epoch 300/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0876 - accuracy: 0.9924 - val_loss: 3.3662 - val_accuracy: 0.3060
Epoch 301/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0654 - accuracy: 0.9955 - val_loss: 3.3533 - val_accuracy: 0.3060
Epoch 302/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0630 - accuracy: 0.9970 - val_loss: 3.3412 - val_accuracy: 0.3060
Epoch 303/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0910 - accuracy: 0.9894 - val_loss: 3.3288 - val_accuracy: 0.3097
Epoch 304/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0763 - accuracy: 0.9939 - val_loss: 3.3170 - val_accuracy: 0.3097
Epoch 305/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0742 - accuracy: 0.9955 - val_loss: 3.3047 - val_accuracy: 0.3134
Epoch 306/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0714 - accuracy: 0.9955 - val_loss: 3.2925 - val_accuracy: 0.3172
Epoch 307/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0722 - accuracy: 0.9955 - val_loss: 3.2802 - val_accuracy: 0.3172
Epoch 308/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0714 - accuracy: 0.9924 - val_loss: 3.2684 - val_accuracy: 0.3246
Epoch 309/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0702 - accuracy: 0.9985 - val_loss: 3.2564 - val_accuracy: 0.3246
Epoch 310/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0706 - accuracy: 0.9955 - val_loss: 3.2442 - val_accuracy: 0.3284
Epoch 311/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0782 - accuracy: 0.9955 - val_loss: 3.2324 - val_accuracy: 0.3284
Epoch 312/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0841 - accuracy: 0.9848 - val_loss: 3.2205 - val_accuracy: 0.3321
Epoch 313/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0730 - accuracy: 0.9924 - val_loss: 3.2082 - val_accuracy: 0.3358
Epoch 314/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0639 - accuracy: 0.9970 - val_loss: 3.1962 - val_accuracy: 0.3358
Epoch 315/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0781 - accuracy: 0.9909 - val_loss: 3.1841 - val_accuracy: 0.3358
Epoch 316/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0632 - accuracy: 0.9985 - val_loss: 3.1724 - val_accuracy: 0.3358
Epoch 317/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0628 - accuracy: 0.9985 - val_loss: 3.1605 - val_accuracy: 0.3396
Epoch 318/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0658 - accuracy: 1.0000 - val_loss: 3.1495 - val_accuracy: 0.3396
Epoch 319/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0609 - accuracy: 0.9970 - val_loss: 3.1376 - val_accuracy: 0.3433
Epoch 320/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0722 - accuracy: 0.9924 - val_loss: 3.1259 - val_accuracy: 0.3433
Epoch 321/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0707 - accuracy: 1.0000 - val_loss: 3.1140 - val_accuracy: 0.3433
Epoch 322/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0694 - accuracy: 0.9939 - val_loss: 3.1029 - val_accuracy: 0.3470
Epoch 323/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0701 - accuracy: 0.9955 - val_loss: 3.0919 - val_accuracy: 0.3507
Epoch 324/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0674 - accuracy: 0.9970 - val_loss: 3.0808 - val_accuracy: 0.3545
Epoch 325/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0707 - accuracy: 0.9955 - val_loss: 3.0700 - val_accuracy: 0.3545
Epoch 326/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0795 - accuracy: 0.9955 - val_loss: 3.0590 - val_accuracy: 0.3545
Epoch 327/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0701 - accuracy: 0.9970 - val_loss: 3.0479 - val_accuracy: 0.3619
Epoch 328/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0651 - accuracy: 0.9955 - val_loss: 3.0371 - val_accuracy: 0.3657
Epoch 329/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0745 - accuracy: 0.9909 - val_loss: 3.0263 - val_accuracy: 0.3769
Epoch 330/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0758 - accuracy: 0.9955 - val_loss: 3.0154 - val_accuracy: 0.3806
Epoch 331/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0751 - accuracy: 0.9970 - val_loss: 3.0048 - val_accuracy: 0.3806
Epoch 332/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0726 - accuracy: 0.9939 - val_loss: 2.9937 - val_accuracy: 0.3806
Epoch 333/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0638 - accuracy: 0.9955 - val_loss: 2.9835 - val_accuracy: 0.3806
Epoch 334/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0716 - accuracy: 0.9970 - val_loss: 2.9728 - val_accuracy: 0.3806
Epoch 335/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0688 - accuracy: 0.9955 - val_loss: 2.9619 - val_accuracy: 0.3843
Epoch 336/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0697 - accuracy: 0.9970 - val_loss: 2.9516 - val_accuracy: 0.3881
Epoch 337/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0659 - accuracy: 1.0000 - val_loss: 2.9415 - val_accuracy: 0.3918
Epoch 338/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0704 - accuracy: 0.9985 - val_loss: 2.9315 - val_accuracy: 0.3955
Epoch 339/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0763 - accuracy: 1.0000 - val_loss: 2.9209 - val_accuracy: 0.4030
Epoch 340/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0656 - accuracy: 0.9985 - val_loss: 2.9106 - val_accuracy: 0.4067
Epoch 341/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0764 - accuracy: 0.9970 - val_loss: 2.8997 - val_accuracy: 0.4067
Epoch 342/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0634 - accuracy: 0.9970 - val_loss: 2.8898 - val_accuracy: 0.4067
Epoch 343/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0754 - accuracy: 0.9924 - val_loss: 2.8790 - val_accuracy: 0.4104
Epoch 344/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0750 - accuracy: 0.9955 - val_loss: 2.8691 - val_accuracy: 0.4142
Epoch 345/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0707 - accuracy: 0.9985 - val_loss: 2.8586 - val_accuracy: 0.4179
Epoch 346/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0767 - accuracy: 0.9985 - val_loss: 2.8478 - val_accuracy: 0.4216
Epoch 347/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0648 - accuracy: 0.9970 - val_loss: 2.8373 - val_accuracy: 0.4328
Epoch 348/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0731 - accuracy: 0.9909 - val_loss: 2.8270 - val_accuracy: 0.4328
Epoch 349/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0786 - accuracy: 0.9879 - val_loss: 2.8166 - val_accuracy: 0.4328
Epoch 350/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0706 - accuracy: 0.9939 - val_loss: 2.8060 - val_accuracy: 0.4366
Epoch 351/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0904 - accuracy: 0.9879 - val_loss: 2.7957 - val_accuracy: 0.4366
Epoch 352/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0794 - accuracy: 0.9939 - val_loss: 2.7857 - val_accuracy: 0.4366
Epoch 353/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0671 - accuracy: 0.9970 - val_loss: 2.7754 - val_accuracy: 0.4366
Epoch 354/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0762 - accuracy: 0.9970 - val_loss: 2.7653 - val_accuracy: 0.4366
Epoch 355/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0844 - accuracy: 0.9909 - val_loss: 2.7556 - val_accuracy: 0.4366
Epoch 356/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0633 - accuracy: 1.0000 - val_loss: 2.7459 - val_accuracy: 0.4366
Epoch 357/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0636 - accuracy: 0.9955 - val_loss: 2.7365 - val_accuracy: 0.4366
Epoch 358/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0674 - accuracy: 0.9970 - val_loss: 2.7264 - val_accuracy: 0.4366
Epoch 359/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0704 - accuracy: 0.9924 - val_loss: 2.7164 - val_accuracy: 0.4366
Epoch 360/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0645 - accuracy: 0.9939 - val_loss: 2.7066 - val_accuracy: 0.4403
Epoch 361/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0824 - accuracy: 0.9879 - val_loss: 2.6968 - val_accuracy: 0.4440
Epoch 362/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0760 - accuracy: 0.9924 - val_loss: 2.6869 - val_accuracy: 0.4552
Epoch 363/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0852 - accuracy: 0.9924 - val_loss: 2.6777 - val_accuracy: 0.4590
Epoch 364/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0758 - accuracy: 0.9955 - val_loss: 2.6679 - val_accuracy: 0.4590
Epoch 365/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0754 - accuracy: 0.9924 - val_loss: 2.6580 - val_accuracy: 0.4664
Epoch 366/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0715 - accuracy: 0.9955 - val_loss: 2.6489 - val_accuracy: 0.4664
Epoch 367/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0613 - accuracy: 0.9985 - val_loss: 2.6392 - val_accuracy: 0.4664
Epoch 368/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0673 - accuracy: 0.9939 - val_loss: 2.6299 - val_accuracy: 0.4701
Epoch 369/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0787 - accuracy: 0.9909 - val_loss: 2.6209 - val_accuracy: 0.4701
Epoch 370/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0653 - accuracy: 0.9985 - val_loss: 2.6115 - val_accuracy: 0.4701
Epoch 371/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0772 - accuracy: 0.9924 - val_loss: 2.6027 - val_accuracy: 0.4739
Epoch 372/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0763 - accuracy: 0.9939 - val_loss: 2.5939 - val_accuracy: 0.4739
Epoch 373/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0672 - accuracy: 0.9939 - val_loss: 2.5848 - val_accuracy: 0.4739
Epoch 374/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0610 - accuracy: 0.9970 - val_loss: 2.5760 - val_accuracy: 0.4739
Epoch 375/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0605 - accuracy: 0.9970 - val_loss: 2.5675 - val_accuracy: 0.4776
Epoch 376/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0724 - accuracy: 0.9939 - val_loss: 2.5587 - val_accuracy: 0.4776
Epoch 377/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0633 - accuracy: 0.9955 - val_loss: 2.5499 - val_accuracy: 0.4776
Epoch 378/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0737 - accuracy: 0.9955 - val_loss: 2.5419 - val_accuracy: 0.4851
Epoch 379/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0695 - accuracy: 0.9955 - val_loss: 2.5334 - val_accuracy: 0.4851
Epoch 380/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0664 - accuracy: 0.9955 - val_loss: 2.5245 - val_accuracy: 0.4851
Epoch 381/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0805 - accuracy: 0.9955 - val_loss: 2.5158 - val_accuracy: 0.4813
Epoch 382/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0598 - accuracy: 1.0000 - val_loss: 2.5074 - val_accuracy: 0.4851
Epoch 383/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0727 - accuracy: 0.9939 - val_loss: 2.4988 - val_accuracy: 0.4888
Epoch 384/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0624 - accuracy: 0.9970 - val_loss: 2.4899 - val_accuracy: 0.4888
Epoch 385/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0675 - accuracy: 0.9985 - val_loss: 2.4815 - val_accuracy: 0.4925
Epoch 386/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0617 - accuracy: 0.9955 - val_loss: 2.4735 - val_accuracy: 0.4925
Epoch 387/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0830 - accuracy: 0.9879 - val_loss: 2.4654 - val_accuracy: 0.4925
Epoch 388/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0636 - accuracy: 0.9909 - val_loss: 2.4570 - val_accuracy: 0.4925
Epoch 389/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0623 - accuracy: 0.9985 - val_loss: 2.4489 - val_accuracy: 0.4925
Epoch 390/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0613 - accuracy: 0.9985 - val_loss: 2.4408 - val_accuracy: 0.4925
Epoch 391/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0640 - accuracy: 0.9970 - val_loss: 2.4327 - val_accuracy: 0.4963
Epoch 392/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0701 - accuracy: 0.9985 - val_loss: 2.4245 - val_accuracy: 0.5037
Epoch 393/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0707 - accuracy: 0.9955 - val_loss: 2.4164 - val_accuracy: 0.5037
Epoch 394/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0643 - accuracy: 0.9970 - val_loss: 2.4084 - val_accuracy: 0.5037
Epoch 395/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0701 - accuracy: 0.9909 - val_loss: 2.4003 - val_accuracy: 0.5075
Epoch 396/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0720 - accuracy: 0.9985 - val_loss: 2.3923 - val_accuracy: 0.5075
Epoch 397/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0775 - accuracy: 0.9955 - val_loss: 2.3841 - val_accuracy: 0.5075
Epoch 398/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0601 - accuracy: 0.9985 - val_loss: 2.3756 - val_accuracy: 0.5112
Epoch 399/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0626 - accuracy: 0.9985 - val_loss: 2.3676 - val_accuracy: 0.5112
Epoch 400/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0782 - accuracy: 0.9955 - val_loss: 2.3593 - val_accuracy: 0.5112
Epoch 401/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0703 - accuracy: 1.0000 - val_loss: 2.3513 - val_accuracy: 0.5112
Epoch 402/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0781 - accuracy: 0.9924 - val_loss: 2.3435 - val_accuracy: 0.5112
Epoch 403/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0668 - accuracy: 0.9970 - val_loss: 2.3359 - val_accuracy: 0.5112
Epoch 404/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0744 - accuracy: 0.9924 - val_loss: 2.3279 - val_accuracy: 0.5112
Epoch 405/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0742 - accuracy: 0.9939 - val_loss: 2.3202 - val_accuracy: 0.5149
Epoch 406/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0663 - accuracy: 0.9955 - val_loss: 2.3126 - val_accuracy: 0.5149
Epoch 407/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0651 - accuracy: 0.9970 - val_loss: 2.3052 - val_accuracy: 0.5149
Epoch 408/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0596 - accuracy: 0.9970 - val_loss: 2.2975 - val_accuracy: 0.5149
Epoch 409/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0666 - accuracy: 0.9955 - val_loss: 2.2899 - val_accuracy: 0.5187
Epoch 410/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0539 - accuracy: 1.0000 - val_loss: 2.2816 - val_accuracy: 0.5224
Epoch 411/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0733 - accuracy: 0.9955 - val_loss: 2.2738 - val_accuracy: 0.5336
Epoch 412/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0650 - accuracy: 0.9970 - val_loss: 2.2664 - val_accuracy: 0.5336
Epoch 413/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0666 - accuracy: 0.9970 - val_loss: 2.2591 - val_accuracy: 0.5336
Epoch 414/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0662 - accuracy: 0.9955 - val_loss: 2.2519 - val_accuracy: 0.5336
Epoch 415/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0724 - accuracy: 0.9955 - val_loss: 2.2446 - val_accuracy: 0.5336
Epoch 416/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0719 - accuracy: 0.9955 - val_loss: 2.2372 - val_accuracy: 0.5336
Epoch 417/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0837 - accuracy: 0.9909 - val_loss: 2.2300 - val_accuracy: 0.5336
Epoch 418/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0701 - accuracy: 0.9970 - val_loss: 2.2227 - val_accuracy: 0.5373
Epoch 419/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0770 - accuracy: 0.9924 - val_loss: 2.2153 - val_accuracy: 0.5373
Epoch 420/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0742 - accuracy: 0.9955 - val_loss: 2.2078 - val_accuracy: 0.5373
Epoch 421/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0668 - accuracy: 0.9955 - val_loss: 2.2010 - val_accuracy: 0.5410
Epoch 422/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0588 - accuracy: 0.9985 - val_loss: 2.1941 - val_accuracy: 0.5410
Epoch 423/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0846 - accuracy: 0.9909 - val_loss: 2.1870 - val_accuracy: 0.5485
Epoch 424/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0741 - accuracy: 0.9924 - val_loss: 2.1797 - val_accuracy: 0.5485
Epoch 425/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0761 - accuracy: 0.9955 - val_loss: 2.1725 - val_accuracy: 0.5485
Epoch 426/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0751 - accuracy: 0.9924 - val_loss: 2.1652 - val_accuracy: 0.5485
Epoch 427/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0685 - accuracy: 0.9985 - val_loss: 2.1575 - val_accuracy: 0.5485
Epoch 428/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0766 - accuracy: 0.9909 - val_loss: 2.1499 - val_accuracy: 0.5485
Epoch 429/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0692 - accuracy: 0.9970 - val_loss: 2.1424 - val_accuracy: 0.5522
Epoch 430/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0668 - accuracy: 1.0000 - val_loss: 2.1352 - val_accuracy: 0.5522
Epoch 431/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0668 - accuracy: 0.9985 - val_loss: 2.1281 - val_accuracy: 0.5522
Epoch 432/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0643 - accuracy: 0.9970 - val_loss: 2.1211 - val_accuracy: 0.5522
Epoch 433/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0760 - accuracy: 0.9985 - val_loss: 2.1140 - val_accuracy: 0.5560
Epoch 434/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0659 - accuracy: 0.9955 - val_loss: 2.1072 - val_accuracy: 0.5560
Epoch 435/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0582 - accuracy: 0.9985 - val_loss: 2.1000 - val_accuracy: 0.5560
Epoch 436/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0806 - accuracy: 0.9955 - val_loss: 2.0931 - val_accuracy: 0.5597
Epoch 437/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0648 - accuracy: 0.9970 - val_loss: 2.0856 - val_accuracy: 0.5597
Epoch 438/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0616 - accuracy: 0.9985 - val_loss: 2.0781 - val_accuracy: 0.5597
Epoch 439/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0698 - accuracy: 0.9939 - val_loss: 2.0712 - val_accuracy: 0.5634
Epoch 440/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0675 - accuracy: 0.9955 - val_loss: 2.0643 - val_accuracy: 0.5634
Epoch 441/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0658 - accuracy: 0.9970 - val_loss: 2.0573 - val_accuracy: 0.5634
Epoch 442/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0611 - accuracy: 0.9985 - val_loss: 2.0504 - val_accuracy: 0.5634
Epoch 443/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0691 - accuracy: 0.9924 - val_loss: 2.0432 - val_accuracy: 0.5634
Epoch 444/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0819 - accuracy: 0.9924 - val_loss: 2.0365 - val_accuracy: 0.5634
Epoch 445/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0664 - accuracy: 0.9939 - val_loss: 2.0300 - val_accuracy: 0.5672
Epoch 446/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0671 - accuracy: 0.9985 - val_loss: 2.0232 - val_accuracy: 0.5672
Epoch 447/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0715 - accuracy: 0.9970 - val_loss: 2.0163 - val_accuracy: 0.5672
Epoch 448/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0747 - accuracy: 0.9924 - val_loss: 2.0093 - val_accuracy: 0.5709
Epoch 449/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0694 - accuracy: 0.9955 - val_loss: 2.0029 - val_accuracy: 0.5746
Epoch 450/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0715 - accuracy: 0.9955 - val_loss: 1.9960 - val_accuracy: 0.5746
Epoch 451/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0657 - accuracy: 0.9985 - val_loss: 1.9891 - val_accuracy: 0.5746
Epoch 452/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0757 - accuracy: 0.9970 - val_loss: 1.9822 - val_accuracy: 0.5746
Epoch 453/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0619 - accuracy: 0.9970 - val_loss: 1.9751 - val_accuracy: 0.5821
Epoch 454/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0788 - accuracy: 0.9970 - val_loss: 1.9679 - val_accuracy: 0.5821
Epoch 455/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0688 - accuracy: 0.9970 - val_loss: 1.9609 - val_accuracy: 0.5821
Epoch 456/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0823 - accuracy: 0.9894 - val_loss: 1.9538 - val_accuracy: 0.5858
Epoch 457/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0744 - accuracy: 0.9955 - val_loss: 1.9471 - val_accuracy: 0.5858
Epoch 458/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0702 - accuracy: 0.9970 - val_loss: 1.9401 - val_accuracy: 0.5858
Epoch 459/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0698 - accuracy: 0.9985 - val_loss: 1.9332 - val_accuracy: 0.5858
Epoch 460/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0674 - accuracy: 0.9955 - val_loss: 1.9262 - val_accuracy: 0.5858
Epoch 461/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0779 - accuracy: 0.9939 - val_loss: 1.9194 - val_accuracy: 0.5858
Epoch 462/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0701 - accuracy: 0.9985 - val_loss: 1.9124 - val_accuracy: 0.5896
Epoch 463/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0674 - accuracy: 0.9970 - val_loss: 1.9056 - val_accuracy: 0.5896
Epoch 464/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0691 - accuracy: 0.9939 - val_loss: 1.8991 - val_accuracy: 0.5896
Epoch 465/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0623 - accuracy: 0.9955 - val_loss: 1.8919 - val_accuracy: 0.5896
Epoch 466/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0705 - accuracy: 0.9955 - val_loss: 1.8854 - val_accuracy: 0.5933
Epoch 467/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0822 - accuracy: 0.9939 - val_loss: 1.8782 - val_accuracy: 0.6007
Epoch 468/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0691 - accuracy: 0.9955 - val_loss: 1.8718 - val_accuracy: 0.6007
Epoch 469/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0812 - accuracy: 0.9909 - val_loss: 1.8649 - val_accuracy: 0.6045
Epoch 470/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0634 - accuracy: 0.9970 - val_loss: 1.8580 - val_accuracy: 0.6045
Epoch 471/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0731 - accuracy: 0.9955 - val_loss: 1.8515 - val_accuracy: 0.6045
Epoch 472/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0614 - accuracy: 0.9970 - val_loss: 1.8449 - val_accuracy: 0.6045
Epoch 473/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0708 - accuracy: 0.9985 - val_loss: 1.8382 - val_accuracy: 0.6082
Epoch 474/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0668 - accuracy: 0.9939 - val_loss: 1.8313 - val_accuracy: 0.6082
Epoch 475/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0614 - accuracy: 0.9955 - val_loss: 1.8247 - val_accuracy: 0.6082
Epoch 476/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0718 - accuracy: 0.9985 - val_loss: 1.8178 - val_accuracy: 0.6082
Epoch 477/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0640 - accuracy: 0.9955 - val_loss: 1.8110 - val_accuracy: 0.6119
Epoch 478/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0684 - accuracy: 0.9955 - val_loss: 1.8046 - val_accuracy: 0.6119
Epoch 479/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0709 - accuracy: 0.9924 - val_loss: 1.7981 - val_accuracy: 0.6119
Epoch 480/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0683 - accuracy: 0.9970 - val_loss: 1.7917 - val_accuracy: 0.6119
Epoch 481/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0736 - accuracy: 0.9970 - val_loss: 1.7853 - val_accuracy: 0.6119
Epoch 482/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0702 - accuracy: 0.9955 - val_loss: 1.7789 - val_accuracy: 0.6157
Epoch 483/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0656 - accuracy: 0.9939 - val_loss: 1.7722 - val_accuracy: 0.6157
Epoch 484/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0745 - accuracy: 0.9955 - val_loss: 1.7658 - val_accuracy: 0.6157
Epoch 485/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0673 - accuracy: 0.9985 - val_loss: 1.7592 - val_accuracy: 0.6231
Epoch 486/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0709 - accuracy: 0.9955 - val_loss: 1.7531 - val_accuracy: 0.6231
Epoch 487/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0704 - accuracy: 0.9970 - val_loss: 1.7467 - val_accuracy: 0.6231
Epoch 488/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0971 - accuracy: 0.9864 - val_loss: 1.7407 - val_accuracy: 0.6269
Epoch 489/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0681 - accuracy: 0.9985 - val_loss: 1.7342 - val_accuracy: 0.6269
Epoch 490/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0678 - accuracy: 0.9985 - val_loss: 1.7279 - val_accuracy: 0.6269
Epoch 491/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0783 - accuracy: 0.9924 - val_loss: 1.7215 - val_accuracy: 0.6269
Epoch 492/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0644 - accuracy: 0.9955 - val_loss: 1.7153 - val_accuracy: 0.6269
Epoch 493/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0629 - accuracy: 0.9955 - val_loss: 1.7091 - val_accuracy: 0.6269
Epoch 494/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0656 - accuracy: 0.9924 - val_loss: 1.7030 - val_accuracy: 0.6306
Epoch 495/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0605 - accuracy: 0.9970 - val_loss: 1.6965 - val_accuracy: 0.6306
Epoch 496/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0703 - accuracy: 0.9924 - val_loss: 1.6903 - val_accuracy: 0.6306
Epoch 497/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0687 - accuracy: 0.9970 - val_loss: 1.6843 - val_accuracy: 0.6306
Epoch 498/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0835 - accuracy: 0.9924 - val_loss: 1.6783 - val_accuracy: 0.6306
Epoch 499/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0615 - accuracy: 0.9939 - val_loss: 1.6723 - val_accuracy: 0.6306
Epoch 500/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0785 - accuracy: 0.9864 - val_loss: 1.6665 - val_accuracy: 0.6343
Epoch 501/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0768 - accuracy: 0.9909 - val_loss: 1.6606 - val_accuracy: 0.6343
Epoch 502/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0675 - accuracy: 0.9939 - val_loss: 1.6547 - val_accuracy: 0.6381
Epoch 503/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0658 - accuracy: 0.9970 - val_loss: 1.6486 - val_accuracy: 0.6381
Epoch 504/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0660 - accuracy: 0.9970 - val_loss: 1.6428 - val_accuracy: 0.6381
Epoch 505/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0565 - accuracy: 1.0000 - val_loss: 1.6369 - val_accuracy: 0.6381
Epoch 506/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0748 - accuracy: 0.9924 - val_loss: 1.6313 - val_accuracy: 0.6381
Epoch 507/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0634 - accuracy: 0.9985 - val_loss: 1.6255 - val_accuracy: 0.6381
Epoch 508/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0650 - accuracy: 0.9985 - val_loss: 1.6194 - val_accuracy: 0.6381
Epoch 509/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0707 - accuracy: 0.9970 - val_loss: 1.6137 - val_accuracy: 0.6381
Epoch 510/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0626 - accuracy: 1.0000 - val_loss: 1.6080 - val_accuracy: 0.6418
Epoch 511/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0588 - accuracy: 0.9970 - val_loss: 1.6022 - val_accuracy: 0.6418
Epoch 512/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0875 - accuracy: 0.9924 - val_loss: 1.5964 - val_accuracy: 0.6455
Epoch 513/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0685 - accuracy: 0.9955 - val_loss: 1.5905 - val_accuracy: 0.6455
Epoch 514/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0753 - accuracy: 0.9970 - val_loss: 1.5844 - val_accuracy: 0.6455
Epoch 515/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0671 - accuracy: 0.9970 - val_loss: 1.5785 - val_accuracy: 0.6455
Epoch 516/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0721 - accuracy: 0.9939 - val_loss: 1.5728 - val_accuracy: 0.6493
Epoch 517/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0669 - accuracy: 0.9955 - val_loss: 1.5671 - val_accuracy: 0.6493
Epoch 518/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0687 - accuracy: 0.9970 - val_loss: 1.5613 - val_accuracy: 0.6493
Epoch 519/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0694 - accuracy: 0.9955 - val_loss: 1.5554 - val_accuracy: 0.6493
Epoch 520/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0763 - accuracy: 0.9939 - val_loss: 1.5498 - val_accuracy: 0.6493
Epoch 521/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0700 - accuracy: 0.9985 - val_loss: 1.5444 - val_accuracy: 0.6493
Epoch 522/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0780 - accuracy: 0.9939 - val_loss: 1.5393 - val_accuracy: 0.6530
Epoch 523/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0572 - accuracy: 0.9985 - val_loss: 1.5340 - val_accuracy: 0.6530
Epoch 524/600
660/660 [==============================] - 5s 8ms/step - loss: 0.0760 - accuracy: 0.9955 - val_loss: 1.5289 - val_accuracy: 0.6530
Epoch 525/600
660/660 [==============================] - 6s 9ms/step - loss: 0.0775 - accuracy: 0.9924 - val_loss: 1.5236 - val_accuracy: 0.6604
Epoch 526/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0634 - accuracy: 1.0000 - val_loss: 1.5181 - val_accuracy: 0.6604
Epoch 527/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0684 - accuracy: 0.9924 - val_loss: 1.5129 - val_accuracy: 0.6604
Epoch 528/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0691 - accuracy: 0.9985 - val_loss: 1.5073 - val_accuracy: 0.6604
Epoch 529/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0574 - accuracy: 0.9985 - val_loss: 1.5023 - val_accuracy: 0.6604
Epoch 530/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0642 - accuracy: 0.9970 - val_loss: 1.4970 - val_accuracy: 0.6604
Epoch 531/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0707 - accuracy: 0.9939 - val_loss: 1.4918 - val_accuracy: 0.6604
Epoch 532/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0642 - accuracy: 0.9955 - val_loss: 1.4864 - val_accuracy: 0.6604
Epoch 533/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0735 - accuracy: 0.9985 - val_loss: 1.4812 - val_accuracy: 0.6604
Epoch 534/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0712 - accuracy: 0.9939 - val_loss: 1.4760 - val_accuracy: 0.6604
Epoch 535/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0588 - accuracy: 0.9955 - val_loss: 1.4708 - val_accuracy: 0.6604
Epoch 536/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0733 - accuracy: 0.9985 - val_loss: 1.4656 - val_accuracy: 0.6604
Epoch 537/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0685 - accuracy: 0.9955 - val_loss: 1.4607 - val_accuracy: 0.6604
Epoch 538/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0650 - accuracy: 0.9985 - val_loss: 1.4557 - val_accuracy: 0.6604
Epoch 539/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0676 - accuracy: 0.9985 - val_loss: 1.4509 - val_accuracy: 0.6642
Epoch 540/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0655 - accuracy: 0.9955 - val_loss: 1.4458 - val_accuracy: 0.6642
Epoch 541/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0654 - accuracy: 0.9985 - val_loss: 1.4406 - val_accuracy: 0.6642
Epoch 542/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0616 - accuracy: 0.9970 - val_loss: 1.4359 - val_accuracy: 0.6679
Epoch 543/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0626 - accuracy: 0.9970 - val_loss: 1.4309 - val_accuracy: 0.6679
Epoch 544/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0657 - accuracy: 0.9970 - val_loss: 1.4260 - val_accuracy: 0.6679
Epoch 545/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0652 - accuracy: 0.9970 - val_loss: 1.4211 - val_accuracy: 0.6716
Epoch 546/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0698 - accuracy: 0.9939 - val_loss: 1.4160 - val_accuracy: 0.6716
Epoch 547/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0693 - accuracy: 0.9985 - val_loss: 1.4113 - val_accuracy: 0.6716
Epoch 548/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0696 - accuracy: 0.9955 - val_loss: 1.4065 - val_accuracy: 0.6716
Epoch 549/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0729 - accuracy: 0.9985 - val_loss: 1.4021 - val_accuracy: 0.6716
Epoch 550/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0779 - accuracy: 0.9924 - val_loss: 1.3973 - val_accuracy: 0.6754
Epoch 551/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0610 - accuracy: 0.9955 - val_loss: 1.3926 - val_accuracy: 0.6754
Epoch 552/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0615 - accuracy: 0.9970 - val_loss: 1.3881 - val_accuracy: 0.6754
Epoch 553/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0691 - accuracy: 0.9939 - val_loss: 1.3836 - val_accuracy: 0.6754
Epoch 554/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0727 - accuracy: 0.9955 - val_loss: 1.3790 - val_accuracy: 0.6754
Epoch 555/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0641 - accuracy: 0.9985 - val_loss: 1.3746 - val_accuracy: 0.6754
Epoch 556/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0682 - accuracy: 0.9970 - val_loss: 1.3701 - val_accuracy: 0.6754
Epoch 557/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0793 - accuracy: 0.9970 - val_loss: 1.3657 - val_accuracy: 0.6754
Epoch 558/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0712 - accuracy: 0.9939 - val_loss: 1.3613 - val_accuracy: 0.6754
Epoch 559/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0561 - accuracy: 0.9955 - val_loss: 1.3569 - val_accuracy: 0.6754
Epoch 560/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0650 - accuracy: 0.9955 - val_loss: 1.3524 - val_accuracy: 0.6754
Epoch 561/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0594 - accuracy: 0.9970 - val_loss: 1.3479 - val_accuracy: 0.6754
Epoch 562/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0618 - accuracy: 0.9970 - val_loss: 1.3434 - val_accuracy: 0.6754
Epoch 563/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0736 - accuracy: 0.9955 - val_loss: 1.3392 - val_accuracy: 0.6754
Epoch 564/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0685 - accuracy: 0.9939 - val_loss: 1.3349 - val_accuracy: 0.6754
Epoch 565/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0620 - accuracy: 0.9985 - val_loss: 1.3305 - val_accuracy: 0.6754
Epoch 566/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0723 - accuracy: 0.9939 - val_loss: 1.3260 - val_accuracy: 0.6754
Epoch 567/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0699 - accuracy: 0.9924 - val_loss: 1.3217 - val_accuracy: 0.6791
Epoch 568/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0685 - accuracy: 0.9970 - val_loss: 1.3177 - val_accuracy: 0.6791
Epoch 569/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0680 - accuracy: 0.9970 - val_loss: 1.3134 - val_accuracy: 0.6828
Epoch 570/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0589 - accuracy: 0.9985 - val_loss: 1.3093 - val_accuracy: 0.6828
Epoch 571/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0735 - accuracy: 0.9970 - val_loss: 1.3052 - val_accuracy: 0.6866
Epoch 572/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0670 - accuracy: 0.9970 - val_loss: 1.3010 - val_accuracy: 0.6866
Epoch 573/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0666 - accuracy: 0.9955 - val_loss: 1.2970 - val_accuracy: 0.6866
Epoch 574/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0701 - accuracy: 1.0000 - val_loss: 1.2930 - val_accuracy: 0.6940
Epoch 575/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0635 - accuracy: 0.9970 - val_loss: 1.2891 - val_accuracy: 0.6940
Epoch 576/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0642 - accuracy: 1.0000 - val_loss: 1.2852 - val_accuracy: 0.6940
Epoch 577/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0668 - accuracy: 0.9970 - val_loss: 1.2815 - val_accuracy: 0.6940
Epoch 578/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0677 - accuracy: 0.9970 - val_loss: 1.2776 - val_accuracy: 0.6940
Epoch 579/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0782 - accuracy: 0.9939 - val_loss: 1.2738 - val_accuracy: 0.7015
Epoch 580/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0637 - accuracy: 0.9955 - val_loss: 1.2701 - val_accuracy: 0.7052
Epoch 581/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0622 - accuracy: 0.9985 - val_loss: 1.2662 - val_accuracy: 0.7090
Epoch 582/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0741 - accuracy: 0.9970 - val_loss: 1.2627 - val_accuracy: 0.7090
Epoch 583/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0732 - accuracy: 0.9955 - val_loss: 1.2590 - val_accuracy: 0.7090
Epoch 584/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0670 - accuracy: 0.9985 - val_loss: 1.2554 - val_accuracy: 0.7090
Epoch 585/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0706 - accuracy: 0.9970 - val_loss: 1.2515 - val_accuracy: 0.7090
Epoch 586/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0796 - accuracy: 0.9924 - val_loss: 1.2479 - val_accuracy: 0.7090
Epoch 587/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0720 - accuracy: 0.9970 - val_loss: 1.2445 - val_accuracy: 0.7090
Epoch 588/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0633 - accuracy: 0.9955 - val_loss: 1.2409 - val_accuracy: 0.7090
Epoch 589/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0652 - accuracy: 0.9970 - val_loss: 1.2375 - val_accuracy: 0.7090
Epoch 590/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0645 - accuracy: 0.9939 - val_loss: 1.2339 - val_accuracy: 0.7090
Epoch 591/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0684 - accuracy: 0.9909 - val_loss: 1.2304 - val_accuracy: 0.7090
Epoch 592/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0719 - accuracy: 0.9894 - val_loss: 1.2270 - val_accuracy: 0.7090
Epoch 593/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0731 - accuracy: 0.9970 - val_loss: 1.2235 - val_accuracy: 0.7127
Epoch 594/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0601 - accuracy: 0.9985 - val_loss: 1.2202 - val_accuracy: 0.7127
Epoch 595/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0678 - accuracy: 0.9955 - val_loss: 1.2169 - val_accuracy: 0.7127
Epoch 596/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0657 - accuracy: 0.9939 - val_loss: 1.2136 - val_accuracy: 0.7127
Epoch 597/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0752 - accuracy: 0.9939 - val_loss: 1.2104 - val_accuracy: 0.7127
Epoch 598/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0642 - accuracy: 0.9985 - val_loss: 1.2072 - val_accuracy: 0.7127
Epoch 599/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0694 - accuracy: 0.9970 - val_loss: 1.2039 - val_accuracy: 0.7127
Epoch 600/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0711 - accuracy: 0.9985 - val_loss: 1.2009 - val_accuracy: 0.7127
Train on 660 samples, validate on 268 samples
Epoch 1/600
660/660 [==============================] - 8s 12ms/step - loss: 0.0598 - accuracy: 1.0000 - val_loss: 1.1929 - val_accuracy: 0.7201
Epoch 2/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0620 - accuracy: 0.9985 - val_loss: 1.1839 - val_accuracy: 0.7164
Epoch 3/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0580 - accuracy: 0.9970 - val_loss: 1.1764 - val_accuracy: 0.7239
Epoch 4/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0716 - accuracy: 0.9970 - val_loss: 1.1669 - val_accuracy: 0.7276
Epoch 5/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0667 - accuracy: 0.9955 - val_loss: 1.1571 - val_accuracy: 0.7239
Epoch 6/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0704 - accuracy: 0.9955 - val_loss: 1.1483 - val_accuracy: 0.7239
Epoch 7/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0714 - accuracy: 0.9894 - val_loss: 1.1430 - val_accuracy: 0.7239
Epoch 8/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0627 - accuracy: 1.0000 - val_loss: 1.1414 - val_accuracy: 0.7276
Epoch 9/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0594 - accuracy: 0.9970 - val_loss: 1.1421 - val_accuracy: 0.7239
Epoch 10/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0663 - accuracy: 0.9924 - val_loss: 1.1436 - val_accuracy: 0.7201
Epoch 11/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0750 - accuracy: 0.9924 - val_loss: 1.1448 - val_accuracy: 0.7276
Epoch 12/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0684 - accuracy: 0.9970 - val_loss: 1.1456 - val_accuracy: 0.7313
Epoch 13/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0656 - accuracy: 1.0000 - val_loss: 1.1462 - val_accuracy: 0.7276
Epoch 14/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0668 - accuracy: 0.9894 - val_loss: 1.1461 - val_accuracy: 0.7313
Epoch 15/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0591 - accuracy: 1.0000 - val_loss: 1.1459 - val_accuracy: 0.7276
Epoch 16/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0754 - accuracy: 0.9909 - val_loss: 1.1456 - val_accuracy: 0.7313
Epoch 17/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0767 - accuracy: 0.9939 - val_loss: 1.1435 - val_accuracy: 0.7351
Epoch 18/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0716 - accuracy: 0.9924 - val_loss: 1.1413 - val_accuracy: 0.7351
Epoch 19/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0620 - accuracy: 0.9970 - val_loss: 1.1387 - val_accuracy: 0.7388
Epoch 20/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0696 - accuracy: 0.9924 - val_loss: 1.1358 - val_accuracy: 0.7388
Epoch 21/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0547 - accuracy: 0.9985 - val_loss: 1.1325 - val_accuracy: 0.7388
Epoch 22/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0719 - accuracy: 0.9970 - val_loss: 1.1295 - val_accuracy: 0.7388
Epoch 23/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0704 - accuracy: 0.9924 - val_loss: 1.1259 - val_accuracy: 0.7388
Epoch 24/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0572 - accuracy: 0.9970 - val_loss: 1.1227 - val_accuracy: 0.7388
Epoch 25/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0549 - accuracy: 1.0000 - val_loss: 1.1194 - val_accuracy: 0.7388
Epoch 26/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0671 - accuracy: 0.9985 - val_loss: 1.1159 - val_accuracy: 0.7388
Epoch 27/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0664 - accuracy: 0.9955 - val_loss: 1.1134 - val_accuracy: 0.7388
Epoch 28/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0604 - accuracy: 1.0000 - val_loss: 1.1108 - val_accuracy: 0.7425
Epoch 29/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0687 - accuracy: 0.9955 - val_loss: 1.1086 - val_accuracy: 0.7425
Epoch 30/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0708 - accuracy: 0.9970 - val_loss: 1.1065 - val_accuracy: 0.7425
Epoch 31/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0547 - accuracy: 1.0000 - val_loss: 1.1045 - val_accuracy: 0.7425
Epoch 32/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0599 - accuracy: 0.9985 - val_loss: 1.1025 - val_accuracy: 0.7425
Epoch 33/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0671 - accuracy: 0.9985 - val_loss: 1.1007 - val_accuracy: 0.7463
Epoch 34/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0806 - accuracy: 0.9879 - val_loss: 1.0987 - val_accuracy: 0.7463
Epoch 35/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0538 - accuracy: 0.9985 - val_loss: 1.0970 - val_accuracy: 0.7463
Epoch 36/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0604 - accuracy: 0.9955 - val_loss: 1.0953 - val_accuracy: 0.7425
Epoch 37/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0591 - accuracy: 0.9924 - val_loss: 1.0936 - val_accuracy: 0.7425
Epoch 38/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0569 - accuracy: 0.9985 - val_loss: 1.0917 - val_accuracy: 0.7425
Epoch 39/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0645 - accuracy: 0.9985 - val_loss: 1.0902 - val_accuracy: 0.7463
Epoch 40/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0662 - accuracy: 0.9970 - val_loss: 1.0884 - val_accuracy: 0.7463
Epoch 41/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0671 - accuracy: 0.9939 - val_loss: 1.0863 - val_accuracy: 0.7425
Epoch 42/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0586 - accuracy: 0.9955 - val_loss: 1.0844 - val_accuracy: 0.7425
Epoch 43/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0557 - accuracy: 0.9939 - val_loss: 1.0824 - val_accuracy: 0.7500
Epoch 44/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0616 - accuracy: 0.9985 - val_loss: 1.0804 - val_accuracy: 0.7500
Epoch 45/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0600 - accuracy: 0.9985 - val_loss: 1.0783 - val_accuracy: 0.7500
Epoch 46/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0643 - accuracy: 0.9909 - val_loss: 1.0764 - val_accuracy: 0.7500
Epoch 47/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0599 - accuracy: 0.9970 - val_loss: 1.0742 - val_accuracy: 0.7500
Epoch 48/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0602 - accuracy: 0.9970 - val_loss: 1.0722 - val_accuracy: 0.7500
Epoch 49/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0694 - accuracy: 0.9970 - val_loss: 1.0702 - val_accuracy: 0.7537
Epoch 50/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0665 - accuracy: 0.9924 - val_loss: 1.0680 - val_accuracy: 0.7537
Epoch 51/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0604 - accuracy: 0.9939 - val_loss: 1.0660 - val_accuracy: 0.7537
Epoch 52/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0679 - accuracy: 0.9955 - val_loss: 1.0642 - val_accuracy: 0.7537
Epoch 53/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0650 - accuracy: 0.9985 - val_loss: 1.0621 - val_accuracy: 0.7537
Epoch 54/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0609 - accuracy: 0.9924 - val_loss: 1.0601 - val_accuracy: 0.7537
Epoch 55/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0671 - accuracy: 0.9924 - val_loss: 1.0582 - val_accuracy: 0.7537
Epoch 56/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0616 - accuracy: 0.9924 - val_loss: 1.0563 - val_accuracy: 0.7537
Epoch 57/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0648 - accuracy: 0.9939 - val_loss: 1.0547 - val_accuracy: 0.7537
Epoch 58/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0568 - accuracy: 0.9955 - val_loss: 1.0529 - val_accuracy: 0.7537
Epoch 59/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0562 - accuracy: 0.9939 - val_loss: 1.0513 - val_accuracy: 0.7537
Epoch 60/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0726 - accuracy: 0.9909 - val_loss: 1.0493 - val_accuracy: 0.7537
Epoch 61/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0591 - accuracy: 0.9924 - val_loss: 1.0474 - val_accuracy: 0.7575
Epoch 62/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0664 - accuracy: 0.9955 - val_loss: 1.0456 - val_accuracy: 0.7575
Epoch 63/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0698 - accuracy: 0.9924 - val_loss: 1.0438 - val_accuracy: 0.7575
Epoch 64/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0560 - accuracy: 0.9985 - val_loss: 1.0420 - val_accuracy: 0.7575
Epoch 65/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0637 - accuracy: 0.9970 - val_loss: 1.0403 - val_accuracy: 0.7575
Epoch 66/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0617 - accuracy: 0.9939 - val_loss: 1.0385 - val_accuracy: 0.7575
Epoch 67/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0570 - accuracy: 0.9970 - val_loss: 1.0365 - val_accuracy: 0.7575
Epoch 68/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0731 - accuracy: 0.9924 - val_loss: 1.0345 - val_accuracy: 0.7575
Epoch 69/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0663 - accuracy: 0.9970 - val_loss: 1.0328 - val_accuracy: 0.7575
Epoch 70/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0540 - accuracy: 0.9985 - val_loss: 1.0309 - val_accuracy: 0.7575
Epoch 71/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0613 - accuracy: 0.9970 - val_loss: 1.0290 - val_accuracy: 0.7575
Epoch 72/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0511 - accuracy: 1.0000 - val_loss: 1.0272 - val_accuracy: 0.7575
Epoch 73/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0571 - accuracy: 0.9985 - val_loss: 1.0254 - val_accuracy: 0.7575
Epoch 74/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0690 - accuracy: 0.9955 - val_loss: 1.0235 - val_accuracy: 0.7575
Epoch 75/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0619 - accuracy: 0.9955 - val_loss: 1.0218 - val_accuracy: 0.7575
Epoch 76/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0591 - accuracy: 0.9924 - val_loss: 1.0200 - val_accuracy: 0.7575
Epoch 77/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0614 - accuracy: 0.9955 - val_loss: 1.0183 - val_accuracy: 0.7612
Epoch 78/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0717 - accuracy: 0.9924 - val_loss: 1.0164 - val_accuracy: 0.7612
Epoch 79/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0589 - accuracy: 0.9985 - val_loss: 1.0146 - val_accuracy: 0.7612
Epoch 80/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0623 - accuracy: 0.9970 - val_loss: 1.0128 - val_accuracy: 0.7612
Epoch 81/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0638 - accuracy: 0.9924 - val_loss: 1.0112 - val_accuracy: 0.7612
Epoch 82/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0630 - accuracy: 0.9970 - val_loss: 1.0094 - val_accuracy: 0.7612
Epoch 83/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0645 - accuracy: 0.9939 - val_loss: 1.0076 - val_accuracy: 0.7612
Epoch 84/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0695 - accuracy: 0.9955 - val_loss: 1.0057 - val_accuracy: 0.7649
Epoch 85/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0662 - accuracy: 0.9970 - val_loss: 1.0040 - val_accuracy: 0.7649
Epoch 86/600
660/660 [==============================] - 6s 9ms/step - loss: 0.0514 - accuracy: 0.9985 - val_loss: 1.0023 - val_accuracy: 0.7649
Epoch 87/600
660/660 [==============================] - 6s 10ms/step - loss: 0.0751 - accuracy: 0.9909 - val_loss: 1.0006 - val_accuracy: 0.7687
Epoch 88/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0637 - accuracy: 0.9955 - val_loss: 0.9991 - val_accuracy: 0.7687
Epoch 89/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0630 - accuracy: 0.9909 - val_loss: 0.9975 - val_accuracy: 0.7687
Epoch 90/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0522 - accuracy: 0.9970 - val_loss: 0.9960 - val_accuracy: 0.7687
Epoch 91/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0628 - accuracy: 0.9970 - val_loss: 0.9944 - val_accuracy: 0.7687
Epoch 92/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0572 - accuracy: 0.9985 - val_loss: 0.9929 - val_accuracy: 0.7687
Epoch 93/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0639 - accuracy: 0.9939 - val_loss: 0.9914 - val_accuracy: 0.7687
Epoch 94/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0659 - accuracy: 0.9955 - val_loss: 0.9898 - val_accuracy: 0.7687
Epoch 95/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0598 - accuracy: 0.9970 - val_loss: 0.9882 - val_accuracy: 0.7687
Epoch 96/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0585 - accuracy: 1.0000 - val_loss: 0.9866 - val_accuracy: 0.7687
Epoch 97/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0570 - accuracy: 0.9955 - val_loss: 0.9852 - val_accuracy: 0.7687
Epoch 98/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0571 - accuracy: 0.9970 - val_loss: 0.9838 - val_accuracy: 0.7724
Epoch 99/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0680 - accuracy: 0.9985 - val_loss: 0.9825 - val_accuracy: 0.7724
Epoch 100/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0643 - accuracy: 0.9939 - val_loss: 0.9811 - val_accuracy: 0.7724
Epoch 101/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0599 - accuracy: 0.9955 - val_loss: 0.9798 - val_accuracy: 0.7724
Epoch 102/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0603 - accuracy: 0.9970 - val_loss: 0.9783 - val_accuracy: 0.7724
Epoch 103/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0560 - accuracy: 0.9985 - val_loss: 0.9771 - val_accuracy: 0.7761
Epoch 104/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0646 - accuracy: 0.9970 - val_loss: 0.9757 - val_accuracy: 0.7761
Epoch 105/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0609 - accuracy: 0.9970 - val_loss: 0.9743 - val_accuracy: 0.7761
Epoch 106/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0671 - accuracy: 0.9924 - val_loss: 0.9730 - val_accuracy: 0.7761
Epoch 107/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0654 - accuracy: 0.9970 - val_loss: 0.9717 - val_accuracy: 0.7761
Epoch 108/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0561 - accuracy: 1.0000 - val_loss: 0.9704 - val_accuracy: 0.7761
Epoch 109/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0608 - accuracy: 0.9924 - val_loss: 0.9691 - val_accuracy: 0.7761
Epoch 110/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0573 - accuracy: 0.9970 - val_loss: 0.9676 - val_accuracy: 0.7761
Epoch 111/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0668 - accuracy: 0.9939 - val_loss: 0.9663 - val_accuracy: 0.7761
Epoch 112/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0621 - accuracy: 0.9985 - val_loss: 0.9649 - val_accuracy: 0.7761
Epoch 113/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0686 - accuracy: 0.9970 - val_loss: 0.9637 - val_accuracy: 0.7761
Epoch 114/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0689 - accuracy: 0.9955 - val_loss: 0.9624 - val_accuracy: 0.7761
Epoch 115/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0704 - accuracy: 0.9909 - val_loss: 0.9612 - val_accuracy: 0.7761
Epoch 116/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0559 - accuracy: 0.9985 - val_loss: 0.9599 - val_accuracy: 0.7761
Epoch 117/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0572 - accuracy: 0.9955 - val_loss: 0.9587 - val_accuracy: 0.7761
Epoch 118/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0560 - accuracy: 0.9985 - val_loss: 0.9574 - val_accuracy: 0.7761
Epoch 119/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0614 - accuracy: 0.9955 - val_loss: 0.9560 - val_accuracy: 0.7724
Epoch 120/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0627 - accuracy: 0.9955 - val_loss: 0.9546 - val_accuracy: 0.7724
Epoch 121/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0528 - accuracy: 0.9985 - val_loss: 0.9536 - val_accuracy: 0.7724
Epoch 122/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0582 - accuracy: 0.9955 - val_loss: 0.9525 - val_accuracy: 0.7724
Epoch 123/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0653 - accuracy: 0.9955 - val_loss: 0.9513 - val_accuracy: 0.7687
Epoch 124/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0608 - accuracy: 0.9985 - val_loss: 0.9501 - val_accuracy: 0.7687
Epoch 125/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0712 - accuracy: 0.9939 - val_loss: 0.9490 - val_accuracy: 0.7687
Epoch 126/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0666 - accuracy: 0.9955 - val_loss: 0.9478 - val_accuracy: 0.7687
Epoch 127/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0620 - accuracy: 0.9924 - val_loss: 0.9467 - val_accuracy: 0.7687
Epoch 128/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0604 - accuracy: 0.9970 - val_loss: 0.9458 - val_accuracy: 0.7687
Epoch 129/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0635 - accuracy: 0.9955 - val_loss: 0.9448 - val_accuracy: 0.7687
Epoch 130/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0794 - accuracy: 0.9894 - val_loss: 0.9439 - val_accuracy: 0.7687
Epoch 131/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0696 - accuracy: 0.9970 - val_loss: 0.9430 - val_accuracy: 0.7687
Epoch 132/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0797 - accuracy: 0.9939 - val_loss: 0.9419 - val_accuracy: 0.7724
Epoch 133/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0668 - accuracy: 0.9939 - val_loss: 0.9410 - val_accuracy: 0.7724
Epoch 134/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0708 - accuracy: 0.9924 - val_loss: 0.9399 - val_accuracy: 0.7724
Epoch 135/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0645 - accuracy: 0.9970 - val_loss: 0.9389 - val_accuracy: 0.7724
Epoch 136/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0563 - accuracy: 1.0000 - val_loss: 0.9379 - val_accuracy: 0.7724
Epoch 137/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0648 - accuracy: 0.9939 - val_loss: 0.9370 - val_accuracy: 0.7724
Epoch 138/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0656 - accuracy: 0.9970 - val_loss: 0.9362 - val_accuracy: 0.7724
Epoch 139/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0619 - accuracy: 0.9955 - val_loss: 0.9354 - val_accuracy: 0.7724
Epoch 140/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0600 - accuracy: 0.9939 - val_loss: 0.9344 - val_accuracy: 0.7724
Epoch 141/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0543 - accuracy: 0.9985 - val_loss: 0.9337 - val_accuracy: 0.7724
Epoch 142/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0582 - accuracy: 0.9985 - val_loss: 0.9329 - val_accuracy: 0.7724
Epoch 143/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0687 - accuracy: 0.9939 - val_loss: 0.9320 - val_accuracy: 0.7761
Epoch 144/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0724 - accuracy: 0.9939 - val_loss: 0.9310 - val_accuracy: 0.7761
Epoch 145/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0663 - accuracy: 0.9970 - val_loss: 0.9302 - val_accuracy: 0.7761
Epoch 146/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0603 - accuracy: 0.9970 - val_loss: 0.9292 - val_accuracy: 0.7761
Epoch 147/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0543 - accuracy: 0.9970 - val_loss: 0.9283 - val_accuracy: 0.7761
Epoch 148/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0500 - accuracy: 1.0000 - val_loss: 0.9274 - val_accuracy: 0.7761
Epoch 149/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0671 - accuracy: 0.9985 - val_loss: 0.9264 - val_accuracy: 0.7761
Epoch 150/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0575 - accuracy: 1.0000 - val_loss: 0.9255 - val_accuracy: 0.7761
Epoch 151/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0597 - accuracy: 0.9985 - val_loss: 0.9248 - val_accuracy: 0.7799
Epoch 152/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0501 - accuracy: 0.9955 - val_loss: 0.9240 - val_accuracy: 0.7799
Epoch 153/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0694 - accuracy: 0.9985 - val_loss: 0.9232 - val_accuracy: 0.7799
Epoch 154/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0560 - accuracy: 1.0000 - val_loss: 0.9226 - val_accuracy: 0.7799
Epoch 155/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0610 - accuracy: 0.9970 - val_loss: 0.9221 - val_accuracy: 0.7799
Epoch 156/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0619 - accuracy: 0.9939 - val_loss: 0.9216 - val_accuracy: 0.7761
Epoch 157/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0574 - accuracy: 0.9970 - val_loss: 0.9211 - val_accuracy: 0.7761
Epoch 158/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0642 - accuracy: 0.9924 - val_loss: 0.9204 - val_accuracy: 0.7761
Epoch 159/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0561 - accuracy: 0.9985 - val_loss: 0.9199 - val_accuracy: 0.7761
Epoch 160/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0646 - accuracy: 0.9924 - val_loss: 0.9193 - val_accuracy: 0.7761
Epoch 161/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0555 - accuracy: 0.9985 - val_loss: 0.9187 - val_accuracy: 0.7761
Epoch 162/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0628 - accuracy: 0.9939 - val_loss: 0.9181 - val_accuracy: 0.7761
Epoch 163/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0649 - accuracy: 0.9985 - val_loss: 0.9176 - val_accuracy: 0.7761
Epoch 164/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0642 - accuracy: 1.0000 - val_loss: 0.9170 - val_accuracy: 0.7761
Epoch 165/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0648 - accuracy: 0.9955 - val_loss: 0.9166 - val_accuracy: 0.7761
Epoch 166/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0565 - accuracy: 1.0000 - val_loss: 0.9160 - val_accuracy: 0.7761
Epoch 167/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0623 - accuracy: 0.9985 - val_loss: 0.9156 - val_accuracy: 0.7799
Epoch 168/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0732 - accuracy: 0.9939 - val_loss: 0.9151 - val_accuracy: 0.7799
Epoch 169/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0550 - accuracy: 1.0000 - val_loss: 0.9146 - val_accuracy: 0.7799
Epoch 170/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0714 - accuracy: 0.9970 - val_loss: 0.9141 - val_accuracy: 0.7799
Epoch 171/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0551 - accuracy: 0.9985 - val_loss: 0.9135 - val_accuracy: 0.7836
Epoch 172/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0540 - accuracy: 0.9985 - val_loss: 0.9130 - val_accuracy: 0.7836
Epoch 173/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0707 - accuracy: 0.9970 - val_loss: 0.9124 - val_accuracy: 0.7836
Epoch 174/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0627 - accuracy: 0.9985 - val_loss: 0.9118 - val_accuracy: 0.7836
Epoch 175/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0696 - accuracy: 0.9939 - val_loss: 0.9114 - val_accuracy: 0.7836
Epoch 176/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0577 - accuracy: 0.9924 - val_loss: 0.9108 - val_accuracy: 0.7836
Epoch 177/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0612 - accuracy: 0.9955 - val_loss: 0.9102 - val_accuracy: 0.7836
Epoch 178/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0595 - accuracy: 0.9970 - val_loss: 0.9098 - val_accuracy: 0.7836
Epoch 179/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0553 - accuracy: 0.9985 - val_loss: 0.9094 - val_accuracy: 0.7873
Epoch 180/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0574 - accuracy: 0.9955 - val_loss: 0.9088 - val_accuracy: 0.7873
Epoch 181/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0662 - accuracy: 0.9924 - val_loss: 0.9083 - val_accuracy: 0.7873
Epoch 182/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0700 - accuracy: 0.9955 - val_loss: 0.9077 - val_accuracy: 0.7873
Epoch 183/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0670 - accuracy: 0.9939 - val_loss: 0.9072 - val_accuracy: 0.7873
Epoch 184/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0605 - accuracy: 0.9955 - val_loss: 0.9067 - val_accuracy: 0.7873
Epoch 185/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0619 - accuracy: 0.9985 - val_loss: 0.9063 - val_accuracy: 0.7873
Epoch 186/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0609 - accuracy: 0.9939 - val_loss: 0.9058 - val_accuracy: 0.7836
Epoch 187/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0593 - accuracy: 0.9939 - val_loss: 0.9053 - val_accuracy: 0.7836
Epoch 188/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0648 - accuracy: 0.9939 - val_loss: 0.9050 - val_accuracy: 0.7836
Epoch 189/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0617 - accuracy: 0.9970 - val_loss: 0.9046 - val_accuracy: 0.7873
Epoch 190/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0623 - accuracy: 0.9970 - val_loss: 0.9040 - val_accuracy: 0.7873
Epoch 191/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0548 - accuracy: 0.9955 - val_loss: 0.9036 - val_accuracy: 0.7873
Epoch 192/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0629 - accuracy: 0.9970 - val_loss: 0.9033 - val_accuracy: 0.7873
Epoch 193/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0646 - accuracy: 0.9955 - val_loss: 0.9028 - val_accuracy: 0.7873
Epoch 194/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0535 - accuracy: 0.9970 - val_loss: 0.9025 - val_accuracy: 0.7873
Epoch 195/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0580 - accuracy: 1.0000 - val_loss: 0.9019 - val_accuracy: 0.7873
Epoch 196/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0640 - accuracy: 0.9955 - val_loss: 0.9015 - val_accuracy: 0.7873
Epoch 197/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0637 - accuracy: 0.9985 - val_loss: 0.9010 - val_accuracy: 0.7873
Epoch 198/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0645 - accuracy: 0.9924 - val_loss: 0.9005 - val_accuracy: 0.7873
Epoch 199/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0533 - accuracy: 1.0000 - val_loss: 0.9001 - val_accuracy: 0.7873
Epoch 200/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0556 - accuracy: 1.0000 - val_loss: 0.8996 - val_accuracy: 0.7873
Epoch 201/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0575 - accuracy: 1.0000 - val_loss: 0.8991 - val_accuracy: 0.7873
Epoch 202/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0603 - accuracy: 0.9939 - val_loss: 0.8986 - val_accuracy: 0.7873
Epoch 203/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0707 - accuracy: 0.9939 - val_loss: 0.8982 - val_accuracy: 0.7873
Epoch 204/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0600 - accuracy: 0.9955 - val_loss: 0.8978 - val_accuracy: 0.7873
Epoch 205/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0639 - accuracy: 0.9970 - val_loss: 0.8974 - val_accuracy: 0.7873
Epoch 206/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0606 - accuracy: 0.9955 - val_loss: 0.8970 - val_accuracy: 0.7873
Epoch 207/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0642 - accuracy: 0.9970 - val_loss: 0.8967 - val_accuracy: 0.7873
Epoch 208/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0554 - accuracy: 0.9970 - val_loss: 0.8963 - val_accuracy: 0.7910
Epoch 209/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0662 - accuracy: 0.9970 - val_loss: 0.8959 - val_accuracy: 0.7910
Epoch 210/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0582 - accuracy: 0.9985 - val_loss: 0.8956 - val_accuracy: 0.7910
Epoch 211/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0514 - accuracy: 0.9985 - val_loss: 0.8953 - val_accuracy: 0.7910
Epoch 212/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0534 - accuracy: 1.0000 - val_loss: 0.8949 - val_accuracy: 0.7910
Epoch 213/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0564 - accuracy: 0.9985 - val_loss: 0.8947 - val_accuracy: 0.7910
Epoch 214/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0615 - accuracy: 0.9955 - val_loss: 0.8943 - val_accuracy: 0.7873
Epoch 215/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0508 - accuracy: 1.0000 - val_loss: 0.8939 - val_accuracy: 0.7873
Epoch 216/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0744 - accuracy: 0.9924 - val_loss: 0.8936 - val_accuracy: 0.7873
Epoch 217/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0625 - accuracy: 0.9970 - val_loss: 0.8933 - val_accuracy: 0.7873
Epoch 218/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0619 - accuracy: 0.9955 - val_loss: 0.8930 - val_accuracy: 0.7873
Epoch 219/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0688 - accuracy: 0.9924 - val_loss: 0.8927 - val_accuracy: 0.7873
Epoch 220/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0585 - accuracy: 0.9970 - val_loss: 0.8924 - val_accuracy: 0.7836
Epoch 221/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0524 - accuracy: 0.9985 - val_loss: 0.8921 - val_accuracy: 0.7836
Epoch 222/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0579 - accuracy: 0.9924 - val_loss: 0.8918 - val_accuracy: 0.7836
Epoch 223/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0618 - accuracy: 0.9970 - val_loss: 0.8914 - val_accuracy: 0.7836
Epoch 224/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0615 - accuracy: 0.9955 - val_loss: 0.8912 - val_accuracy: 0.7836
Epoch 225/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0564 - accuracy: 0.9970 - val_loss: 0.8909 - val_accuracy: 0.7836
Epoch 226/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0618 - accuracy: 0.9985 - val_loss: 0.8908 - val_accuracy: 0.7836
Epoch 227/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0622 - accuracy: 0.9955 - val_loss: 0.8906 - val_accuracy: 0.7873
Epoch 228/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0585 - accuracy: 0.9939 - val_loss: 0.8905 - val_accuracy: 0.7873
Epoch 229/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0565 - accuracy: 1.0000 - val_loss: 0.8902 - val_accuracy: 0.7873
Epoch 230/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0603 - accuracy: 0.9909 - val_loss: 0.8900 - val_accuracy: 0.7873
Epoch 231/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0599 - accuracy: 0.9970 - val_loss: 0.8895 - val_accuracy: 0.7873
Epoch 232/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0592 - accuracy: 0.9939 - val_loss: 0.8892 - val_accuracy: 0.7873
Epoch 233/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0603 - accuracy: 0.9985 - val_loss: 0.8889 - val_accuracy: 0.7873
Epoch 234/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0538 - accuracy: 0.9985 - val_loss: 0.8886 - val_accuracy: 0.7873
Epoch 235/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0527 - accuracy: 0.9985 - val_loss: 0.8882 - val_accuracy: 0.7873
Epoch 236/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0644 - accuracy: 0.9970 - val_loss: 0.8880 - val_accuracy: 0.7873
Epoch 237/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0589 - accuracy: 0.9970 - val_loss: 0.8877 - val_accuracy: 0.7873
Epoch 238/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0672 - accuracy: 0.9909 - val_loss: 0.8874 - val_accuracy: 0.7873
Epoch 239/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0668 - accuracy: 0.9955 - val_loss: 0.8872 - val_accuracy: 0.7873
Epoch 240/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0534 - accuracy: 0.9970 - val_loss: 0.8870 - val_accuracy: 0.7873
Epoch 241/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0674 - accuracy: 0.9970 - val_loss: 0.8867 - val_accuracy: 0.7873
Epoch 242/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0518 - accuracy: 0.9985 - val_loss: 0.8865 - val_accuracy: 0.7873
Epoch 243/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0559 - accuracy: 0.9985 - val_loss: 0.8862 - val_accuracy: 0.7873
Epoch 244/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0608 - accuracy: 0.9985 - val_loss: 0.8860 - val_accuracy: 0.7873
Epoch 245/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0558 - accuracy: 0.9970 - val_loss: 0.8857 - val_accuracy: 0.7873
Epoch 246/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0571 - accuracy: 0.9985 - val_loss: 0.8856 - val_accuracy: 0.7873
Epoch 247/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0646 - accuracy: 0.9970 - val_loss: 0.8853 - val_accuracy: 0.7873
Epoch 248/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0597 - accuracy: 0.9970 - val_loss: 0.8851 - val_accuracy: 0.7873
Epoch 249/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0692 - accuracy: 0.9909 - val_loss: 0.8849 - val_accuracy: 0.7873
Epoch 250/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0527 - accuracy: 1.0000 - val_loss: 0.8845 - val_accuracy: 0.7873
Epoch 251/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0526 - accuracy: 0.9955 - val_loss: 0.8841 - val_accuracy: 0.7873
Epoch 252/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0611 - accuracy: 0.9970 - val_loss: 0.8837 - val_accuracy: 0.7873
Epoch 253/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0527 - accuracy: 0.9985 - val_loss: 0.8833 - val_accuracy: 0.7873
Epoch 254/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0703 - accuracy: 0.9909 - val_loss: 0.8830 - val_accuracy: 0.7873
Epoch 255/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0635 - accuracy: 0.9985 - val_loss: 0.8827 - val_accuracy: 0.7873
Epoch 256/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0574 - accuracy: 0.9970 - val_loss: 0.8824 - val_accuracy: 0.7873
Epoch 257/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0526 - accuracy: 0.9985 - val_loss: 0.8822 - val_accuracy: 0.7873
Epoch 258/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0580 - accuracy: 0.9985 - val_loss: 0.8820 - val_accuracy: 0.7873
Epoch 259/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0614 - accuracy: 0.9955 - val_loss: 0.8815 - val_accuracy: 0.7836
Epoch 260/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0552 - accuracy: 0.9955 - val_loss: 0.8811 - val_accuracy: 0.7836
Epoch 261/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0508 - accuracy: 0.9970 - val_loss: 0.8807 - val_accuracy: 0.7836
Epoch 262/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0600 - accuracy: 0.9985 - val_loss: 0.8804 - val_accuracy: 0.7836
Epoch 263/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0551 - accuracy: 0.9970 - val_loss: 0.8801 - val_accuracy: 0.7836
Epoch 264/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0493 - accuracy: 1.0000 - val_loss: 0.8798 - val_accuracy: 0.7836
Epoch 265/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0625 - accuracy: 0.9985 - val_loss: 0.8795 - val_accuracy: 0.7836
Epoch 266/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0543 - accuracy: 0.9985 - val_loss: 0.8792 - val_accuracy: 0.7836
Epoch 267/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0689 - accuracy: 0.9939 - val_loss: 0.8789 - val_accuracy: 0.7836
Epoch 268/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0596 - accuracy: 0.9955 - val_loss: 0.8787 - val_accuracy: 0.7873
Epoch 269/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0605 - accuracy: 0.9985 - val_loss: 0.8784 - val_accuracy: 0.7873
Epoch 270/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0703 - accuracy: 0.9924 - val_loss: 0.8781 - val_accuracy: 0.7873
Epoch 271/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0576 - accuracy: 0.9955 - val_loss: 0.8778 - val_accuracy: 0.7873
Epoch 272/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0550 - accuracy: 1.0000 - val_loss: 0.8775 - val_accuracy: 0.7873
Epoch 273/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0543 - accuracy: 0.9970 - val_loss: 0.8772 - val_accuracy: 0.7873
Epoch 274/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0541 - accuracy: 0.9970 - val_loss: 0.8770 - val_accuracy: 0.7873
Epoch 275/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0603 - accuracy: 0.9985 - val_loss: 0.8767 - val_accuracy: 0.7873
Epoch 276/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0589 - accuracy: 0.9970 - val_loss: 0.8766 - val_accuracy: 0.7873
Epoch 277/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0615 - accuracy: 0.9955 - val_loss: 0.8764 - val_accuracy: 0.7873
Epoch 278/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0655 - accuracy: 0.9909 - val_loss: 0.8762 - val_accuracy: 0.7873
Epoch 279/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0634 - accuracy: 0.9939 - val_loss: 0.8759 - val_accuracy: 0.7873
Epoch 280/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0596 - accuracy: 0.9985 - val_loss: 0.8756 - val_accuracy: 0.7873
Epoch 281/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0540 - accuracy: 0.9970 - val_loss: 0.8753 - val_accuracy: 0.7873
Epoch 282/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0561 - accuracy: 1.0000 - val_loss: 0.8751 - val_accuracy: 0.7873
Epoch 283/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0623 - accuracy: 0.9985 - val_loss: 0.8749 - val_accuracy: 0.7873
Epoch 284/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0633 - accuracy: 0.9985 - val_loss: 0.8747 - val_accuracy: 0.7873
Epoch 285/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0666 - accuracy: 0.9970 - val_loss: 0.8746 - val_accuracy: 0.7873
Epoch 286/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0642 - accuracy: 0.9955 - val_loss: 0.8745 - val_accuracy: 0.7873
Epoch 287/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0598 - accuracy: 0.9955 - val_loss: 0.8742 - val_accuracy: 0.7873
Epoch 288/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0593 - accuracy: 0.9924 - val_loss: 0.8739 - val_accuracy: 0.7873
Epoch 289/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0527 - accuracy: 0.9970 - val_loss: 0.8735 - val_accuracy: 0.7873
Epoch 290/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0548 - accuracy: 0.9970 - val_loss: 0.8733 - val_accuracy: 0.7873
Epoch 291/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0489 - accuracy: 1.0000 - val_loss: 0.8731 - val_accuracy: 0.7873
Epoch 292/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0495 - accuracy: 1.0000 - val_loss: 0.8729 - val_accuracy: 0.7873
Epoch 293/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0656 - accuracy: 0.9985 - val_loss: 0.8727 - val_accuracy: 0.7873
Epoch 294/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0589 - accuracy: 0.9985 - val_loss: 0.8725 - val_accuracy: 0.7873
Epoch 295/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0540 - accuracy: 0.9955 - val_loss: 0.8724 - val_accuracy: 0.7910
Epoch 296/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0530 - accuracy: 0.9970 - val_loss: 0.8722 - val_accuracy: 0.7910
Epoch 297/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0583 - accuracy: 0.9970 - val_loss: 0.8721 - val_accuracy: 0.7948
Epoch 298/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0535 - accuracy: 1.0000 - val_loss: 0.8719 - val_accuracy: 0.7948
Epoch 299/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0568 - accuracy: 0.9985 - val_loss: 0.8718 - val_accuracy: 0.7948
Epoch 300/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0578 - accuracy: 0.9970 - val_loss: 0.8718 - val_accuracy: 0.7948
Epoch 301/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0560 - accuracy: 0.9970 - val_loss: 0.8717 - val_accuracy: 0.7985
Epoch 302/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0569 - accuracy: 0.9955 - val_loss: 0.8717 - val_accuracy: 0.7985
Epoch 303/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0654 - accuracy: 0.9924 - val_loss: 0.8717 - val_accuracy: 0.7985
Epoch 304/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0613 - accuracy: 0.9924 - val_loss: 0.8717 - val_accuracy: 0.7985
Epoch 305/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0566 - accuracy: 0.9970 - val_loss: 0.8717 - val_accuracy: 0.7985
Epoch 306/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0547 - accuracy: 0.9970 - val_loss: 0.8717 - val_accuracy: 0.7985
Epoch 307/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0592 - accuracy: 0.9924 - val_loss: 0.8716 - val_accuracy: 0.7985
Epoch 308/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0549 - accuracy: 0.9985 - val_loss: 0.8714 - val_accuracy: 0.7985
Epoch 309/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0591 - accuracy: 0.9955 - val_loss: 0.8712 - val_accuracy: 0.7985
Epoch 310/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0579 - accuracy: 0.9985 - val_loss: 0.8710 - val_accuracy: 0.7985
Epoch 311/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0583 - accuracy: 0.9924 - val_loss: 0.8709 - val_accuracy: 0.7985
Epoch 312/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0622 - accuracy: 0.9970 - val_loss: 0.8707 - val_accuracy: 0.7985
Epoch 313/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0704 - accuracy: 0.9985 - val_loss: 0.8705 - val_accuracy: 0.7985
Epoch 314/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0640 - accuracy: 0.9955 - val_loss: 0.8704 - val_accuracy: 0.7985
Epoch 315/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0578 - accuracy: 0.9970 - val_loss: 0.8702 - val_accuracy: 0.7985
Epoch 316/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0527 - accuracy: 1.0000 - val_loss: 0.8702 - val_accuracy: 0.7985
Epoch 317/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0620 - accuracy: 0.9970 - val_loss: 0.8701 - val_accuracy: 0.7985
Epoch 318/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0648 - accuracy: 0.9909 - val_loss: 0.8701 - val_accuracy: 0.7985
Epoch 319/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0682 - accuracy: 0.9939 - val_loss: 0.8698 - val_accuracy: 0.7985
Epoch 320/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0596 - accuracy: 0.9970 - val_loss: 0.8697 - val_accuracy: 0.7985
Epoch 321/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0579 - accuracy: 0.9955 - val_loss: 0.8695 - val_accuracy: 0.7985
Epoch 322/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0499 - accuracy: 0.9985 - val_loss: 0.8692 - val_accuracy: 0.7985
Epoch 323/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0627 - accuracy: 0.9970 - val_loss: 0.8690 - val_accuracy: 0.7985
Epoch 324/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0576 - accuracy: 1.0000 - val_loss: 0.8687 - val_accuracy: 0.7985
Epoch 325/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0671 - accuracy: 0.9955 - val_loss: 0.8687 - val_accuracy: 0.7985
Epoch 326/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0558 - accuracy: 0.9985 - val_loss: 0.8685 - val_accuracy: 0.7985
Epoch 327/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0592 - accuracy: 0.9970 - val_loss: 0.8683 - val_accuracy: 0.7985
Epoch 328/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0664 - accuracy: 0.9955 - val_loss: 0.8681 - val_accuracy: 0.7985
Epoch 329/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0621 - accuracy: 0.9970 - val_loss: 0.8680 - val_accuracy: 0.7985
Epoch 330/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0546 - accuracy: 0.9985 - val_loss: 0.8678 - val_accuracy: 0.7985
Epoch 331/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0564 - accuracy: 0.9970 - val_loss: 0.8677 - val_accuracy: 0.7985
Epoch 332/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0657 - accuracy: 0.9955 - val_loss: 0.8676 - val_accuracy: 0.7985
Epoch 333/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0518 - accuracy: 0.9970 - val_loss: 0.8675 - val_accuracy: 0.7985
Epoch 334/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0508 - accuracy: 0.9985 - val_loss: 0.8675 - val_accuracy: 0.7985
Epoch 335/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0602 - accuracy: 0.9970 - val_loss: 0.8673 - val_accuracy: 0.7985
Epoch 336/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0638 - accuracy: 0.9985 - val_loss: 0.8671 - val_accuracy: 0.7985
Epoch 337/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0606 - accuracy: 0.9924 - val_loss: 0.8669 - val_accuracy: 0.7985
Epoch 338/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0640 - accuracy: 0.9955 - val_loss: 0.8668 - val_accuracy: 0.7985
Epoch 339/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0706 - accuracy: 0.9894 - val_loss: 0.8667 - val_accuracy: 0.7985
Epoch 340/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0494 - accuracy: 0.9985 - val_loss: 0.8665 - val_accuracy: 0.7985
Epoch 341/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0602 - accuracy: 0.9955 - val_loss: 0.8663 - val_accuracy: 0.7985
Epoch 342/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0594 - accuracy: 0.9985 - val_loss: 0.8664 - val_accuracy: 0.7948
Epoch 343/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0513 - accuracy: 0.9985 - val_loss: 0.8662 - val_accuracy: 0.7948
Epoch 344/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0547 - accuracy: 0.9985 - val_loss: 0.8661 - val_accuracy: 0.7948
Epoch 345/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0525 - accuracy: 0.9970 - val_loss: 0.8659 - val_accuracy: 0.7948
Epoch 346/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0646 - accuracy: 0.9924 - val_loss: 0.8657 - val_accuracy: 0.7948
Epoch 347/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0647 - accuracy: 0.9955 - val_loss: 0.8656 - val_accuracy: 0.7948
Epoch 348/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0562 - accuracy: 0.9955 - val_loss: 0.8656 - val_accuracy: 0.7948
Epoch 349/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0551 - accuracy: 0.9985 - val_loss: 0.8653 - val_accuracy: 0.7948
Epoch 350/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0676 - accuracy: 0.9955 - val_loss: 0.8652 - val_accuracy: 0.7948
Epoch 351/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0623 - accuracy: 0.9970 - val_loss: 0.8650 - val_accuracy: 0.7948
Epoch 352/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0505 - accuracy: 0.9985 - val_loss: 0.8649 - val_accuracy: 0.7948
Epoch 353/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0517 - accuracy: 0.9970 - val_loss: 0.8647 - val_accuracy: 0.7948
Epoch 354/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0513 - accuracy: 0.9970 - val_loss: 0.8644 - val_accuracy: 0.7948
Epoch 355/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0614 - accuracy: 0.9955 - val_loss: 0.8643 - val_accuracy: 0.7948
Epoch 356/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0519 - accuracy: 0.9955 - val_loss: 0.8641 - val_accuracy: 0.7948
Epoch 357/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0507 - accuracy: 0.9985 - val_loss: 0.8638 - val_accuracy: 0.7948
Epoch 358/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0502 - accuracy: 0.9985 - val_loss: 0.8637 - val_accuracy: 0.7948
Epoch 359/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0585 - accuracy: 0.9985 - val_loss: 0.8634 - val_accuracy: 0.7948
Epoch 360/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0593 - accuracy: 0.9970 - val_loss: 0.8633 - val_accuracy: 0.7948
Epoch 361/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0575 - accuracy: 0.9924 - val_loss: 0.8631 - val_accuracy: 0.7948
Epoch 362/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0669 - accuracy: 0.9955 - val_loss: 0.8630 - val_accuracy: 0.7910
Epoch 363/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0581 - accuracy: 0.9985 - val_loss: 0.8629 - val_accuracy: 0.7910
Epoch 364/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0589 - accuracy: 0.9939 - val_loss: 0.8628 - val_accuracy: 0.7910
Epoch 365/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0511 - accuracy: 0.9985 - val_loss: 0.8628 - val_accuracy: 0.7910
Epoch 366/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0720 - accuracy: 0.9939 - val_loss: 0.8627 - val_accuracy: 0.7910
Epoch 367/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0570 - accuracy: 0.9970 - val_loss: 0.8627 - val_accuracy: 0.7910
Epoch 368/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0570 - accuracy: 0.9970 - val_loss: 0.8627 - val_accuracy: 0.7910
Epoch 369/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0616 - accuracy: 0.9985 - val_loss: 0.8627 - val_accuracy: 0.7910
Epoch 370/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0628 - accuracy: 0.9970 - val_loss: 0.8625 - val_accuracy: 0.7910
Epoch 371/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0505 - accuracy: 0.9985 - val_loss: 0.8625 - val_accuracy: 0.7910
Epoch 372/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0607 - accuracy: 0.9985 - val_loss: 0.8624 - val_accuracy: 0.7910
Epoch 373/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0619 - accuracy: 0.9939 - val_loss: 0.8625 - val_accuracy: 0.7910
Epoch 374/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0483 - accuracy: 1.0000 - val_loss: 0.8625 - val_accuracy: 0.7910
Epoch 375/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0587 - accuracy: 0.9985 - val_loss: 0.8625 - val_accuracy: 0.7910
Epoch 376/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0608 - accuracy: 1.0000 - val_loss: 0.8624 - val_accuracy: 0.7910
Epoch 377/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0624 - accuracy: 0.9939 - val_loss: 0.8624 - val_accuracy: 0.7910
Epoch 378/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0538 - accuracy: 0.9955 - val_loss: 0.8623 - val_accuracy: 0.7910
Epoch 379/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0612 - accuracy: 0.9939 - val_loss: 0.8622 - val_accuracy: 0.7910
Epoch 380/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0571 - accuracy: 0.9985 - val_loss: 0.8620 - val_accuracy: 0.7948
Epoch 381/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0601 - accuracy: 0.9955 - val_loss: 0.8619 - val_accuracy: 0.7948
Epoch 382/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0510 - accuracy: 0.9955 - val_loss: 0.8617 - val_accuracy: 0.7948
Epoch 383/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0588 - accuracy: 0.9970 - val_loss: 0.8616 - val_accuracy: 0.7948
Epoch 384/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0498 - accuracy: 0.9985 - val_loss: 0.8615 - val_accuracy: 0.7948
Epoch 385/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0611 - accuracy: 0.9955 - val_loss: 0.8612 - val_accuracy: 0.7948
Epoch 386/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0603 - accuracy: 0.9939 - val_loss: 0.8610 - val_accuracy: 0.7985
Epoch 387/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0534 - accuracy: 0.9985 - val_loss: 0.8607 - val_accuracy: 0.7985
Epoch 388/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0570 - accuracy: 0.9955 - val_loss: 0.8606 - val_accuracy: 0.7985
Epoch 389/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0618 - accuracy: 1.0000 - val_loss: 0.8604 - val_accuracy: 0.7985
Epoch 390/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0634 - accuracy: 0.9985 - val_loss: 0.8602 - val_accuracy: 0.7985
Epoch 391/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0596 - accuracy: 0.9970 - val_loss: 0.8601 - val_accuracy: 0.7985
Epoch 392/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0640 - accuracy: 0.9939 - val_loss: 0.8600 - val_accuracy: 0.7985
Epoch 393/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0611 - accuracy: 0.9985 - val_loss: 0.8599 - val_accuracy: 0.7985
Epoch 394/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0468 - accuracy: 0.9985 - val_loss: 0.8597 - val_accuracy: 0.7985
Epoch 395/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0581 - accuracy: 0.9970 - val_loss: 0.8595 - val_accuracy: 0.7985
Epoch 396/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0635 - accuracy: 0.9955 - val_loss: 0.8594 - val_accuracy: 0.7985
Epoch 397/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0610 - accuracy: 0.9955 - val_loss: 0.8596 - val_accuracy: 0.7985
Epoch 398/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0538 - accuracy: 0.9985 - val_loss: 0.8596 - val_accuracy: 0.8022
Epoch 399/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0556 - accuracy: 0.9970 - val_loss: 0.8595 - val_accuracy: 0.8022
Epoch 400/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0471 - accuracy: 0.9985 - val_loss: 0.8596 - val_accuracy: 0.8022
Epoch 401/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0594 - accuracy: 0.9970 - val_loss: 0.8597 - val_accuracy: 0.8022
Epoch 402/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0533 - accuracy: 0.9985 - val_loss: 0.8597 - val_accuracy: 0.8022
Epoch 403/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0496 - accuracy: 0.9985 - val_loss: 0.8598 - val_accuracy: 0.8022
Epoch 404/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0580 - accuracy: 1.0000 - val_loss: 0.8599 - val_accuracy: 0.8022
Epoch 405/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0581 - accuracy: 0.9985 - val_loss: 0.8600 - val_accuracy: 0.8022
Epoch 406/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0561 - accuracy: 0.9970 - val_loss: 0.8599 - val_accuracy: 0.8022
Epoch 407/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0597 - accuracy: 0.9939 - val_loss: 0.8600 - val_accuracy: 0.8022
Epoch 408/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0528 - accuracy: 0.9985 - val_loss: 0.8601 - val_accuracy: 0.8022
Epoch 409/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0593 - accuracy: 0.9970 - val_loss: 0.8600 - val_accuracy: 0.8022
Epoch 410/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0521 - accuracy: 0.9985 - val_loss: 0.8600 - val_accuracy: 0.8022
Epoch 411/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0537 - accuracy: 1.0000 - val_loss: 0.8599 - val_accuracy: 0.8022
Epoch 412/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0600 - accuracy: 0.9939 - val_loss: 0.8596 - val_accuracy: 0.8097
Epoch 413/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0520 - accuracy: 0.9985 - val_loss: 0.8596 - val_accuracy: 0.8097
Epoch 414/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0565 - accuracy: 0.9985 - val_loss: 0.8594 - val_accuracy: 0.8097
Epoch 415/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0528 - accuracy: 0.9985 - val_loss: 0.8592 - val_accuracy: 0.8097
Epoch 416/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0552 - accuracy: 0.9970 - val_loss: 0.8590 - val_accuracy: 0.8097
Epoch 417/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0604 - accuracy: 0.9985 - val_loss: 0.8591 - val_accuracy: 0.8097
Epoch 418/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0615 - accuracy: 0.9970 - val_loss: 0.8590 - val_accuracy: 0.8097
Epoch 419/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0582 - accuracy: 0.9970 - val_loss: 0.8589 - val_accuracy: 0.8097
Epoch 420/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0528 - accuracy: 0.9970 - val_loss: 0.8587 - val_accuracy: 0.8097
Epoch 421/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0533 - accuracy: 0.9985 - val_loss: 0.8588 - val_accuracy: 0.8097
Epoch 422/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0547 - accuracy: 0.9970 - val_loss: 0.8587 - val_accuracy: 0.8097
Epoch 423/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0630 - accuracy: 0.9939 - val_loss: 0.8587 - val_accuracy: 0.8097
Epoch 424/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0574 - accuracy: 0.9970 - val_loss: 0.8587 - val_accuracy: 0.8097
Epoch 425/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0680 - accuracy: 0.9939 - val_loss: 0.8588 - val_accuracy: 0.8097
Epoch 426/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0664 - accuracy: 0.9939 - val_loss: 0.8588 - val_accuracy: 0.8097
Epoch 427/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0497 - accuracy: 0.9970 - val_loss: 0.8587 - val_accuracy: 0.8097
Epoch 428/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0571 - accuracy: 0.9970 - val_loss: 0.8586 - val_accuracy: 0.8097
Epoch 429/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0563 - accuracy: 0.9970 - val_loss: 0.8586 - val_accuracy: 0.8097
Epoch 430/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0542 - accuracy: 1.0000 - val_loss: 0.8584 - val_accuracy: 0.8097
Epoch 431/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0536 - accuracy: 0.9970 - val_loss: 0.8583 - val_accuracy: 0.8097
Epoch 432/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0564 - accuracy: 0.9970 - val_loss: 0.8584 - val_accuracy: 0.8097
Epoch 433/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0514 - accuracy: 0.9985 - val_loss: 0.8583 - val_accuracy: 0.8097
Epoch 434/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0479 - accuracy: 1.0000 - val_loss: 0.8583 - val_accuracy: 0.8097
Epoch 435/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0585 - accuracy: 0.9955 - val_loss: 0.8585 - val_accuracy: 0.8097
Epoch 436/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0559 - accuracy: 0.9985 - val_loss: 0.8584 - val_accuracy: 0.8097
Epoch 437/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0507 - accuracy: 0.9985 - val_loss: 0.8583 - val_accuracy: 0.8097
Epoch 438/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0566 - accuracy: 0.9985 - val_loss: 0.8581 - val_accuracy: 0.8097
Epoch 439/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0588 - accuracy: 0.9970 - val_loss: 0.8583 - val_accuracy: 0.8097
Epoch 440/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0534 - accuracy: 0.9955 - val_loss: 0.8583 - val_accuracy: 0.8097
Epoch 441/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0525 - accuracy: 0.9985 - val_loss: 0.8584 - val_accuracy: 0.8097
Epoch 442/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0490 - accuracy: 0.9970 - val_loss: 0.8584 - val_accuracy: 0.8097
Epoch 443/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0532 - accuracy: 0.9985 - val_loss: 0.8583 - val_accuracy: 0.8097
Epoch 444/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0497 - accuracy: 1.0000 - val_loss: 0.8585 - val_accuracy: 0.8097
Epoch 445/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0550 - accuracy: 0.9970 - val_loss: 0.8587 - val_accuracy: 0.8097
Epoch 446/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0521 - accuracy: 0.9970 - val_loss: 0.8588 - val_accuracy: 0.8097
Epoch 447/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0569 - accuracy: 0.9970 - val_loss: 0.8589 - val_accuracy: 0.8060
Epoch 448/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0604 - accuracy: 0.9955 - val_loss: 0.8591 - val_accuracy: 0.8060
Epoch 449/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0479 - accuracy: 0.9970 - val_loss: 0.8593 - val_accuracy: 0.8060
Epoch 450/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0576 - accuracy: 0.9955 - val_loss: 0.8595 - val_accuracy: 0.8060
Epoch 451/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0522 - accuracy: 0.9985 - val_loss: 0.8597 - val_accuracy: 0.8060
Epoch 452/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0568 - accuracy: 0.9985 - val_loss: 0.8597 - val_accuracy: 0.8060
Epoch 453/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0517 - accuracy: 1.0000 - val_loss: 0.8598 - val_accuracy: 0.8060
Epoch 454/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0509 - accuracy: 1.0000 - val_loss: 0.8600 - val_accuracy: 0.8060
Epoch 455/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0529 - accuracy: 0.9955 - val_loss: 0.8601 - val_accuracy: 0.8060
Epoch 456/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0540 - accuracy: 0.9970 - val_loss: 0.8602 - val_accuracy: 0.8060
Epoch 457/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0573 - accuracy: 0.9985 - val_loss: 0.8603 - val_accuracy: 0.8060
Epoch 458/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0525 - accuracy: 0.9970 - val_loss: 0.8603 - val_accuracy: 0.8060
Epoch 459/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0596 - accuracy: 0.9985 - val_loss: 0.8603 - val_accuracy: 0.8060
Epoch 460/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0572 - accuracy: 0.9985 - val_loss: 0.8603 - val_accuracy: 0.8022
Epoch 461/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0563 - accuracy: 1.0000 - val_loss: 0.8602 - val_accuracy: 0.8022
Epoch 462/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0519 - accuracy: 0.9985 - val_loss: 0.8602 - val_accuracy: 0.8022
Epoch 463/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0598 - accuracy: 0.9955 - val_loss: 0.8604 - val_accuracy: 0.8022
Epoch 464/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0658 - accuracy: 0.9939 - val_loss: 0.8604 - val_accuracy: 0.8022
Epoch 465/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0535 - accuracy: 0.9985 - val_loss: 0.8605 - val_accuracy: 0.8022
Epoch 466/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0538 - accuracy: 0.9985 - val_loss: 0.8605 - val_accuracy: 0.8022
Epoch 467/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0644 - accuracy: 0.9970 - val_loss: 0.8605 - val_accuracy: 0.8022
Epoch 468/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0583 - accuracy: 0.9939 - val_loss: 0.8607 - val_accuracy: 0.8022
Epoch 469/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0572 - accuracy: 0.9939 - val_loss: 0.8608 - val_accuracy: 0.8022
Epoch 470/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0573 - accuracy: 0.9985 - val_loss: 0.8609 - val_accuracy: 0.8022
Epoch 471/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0583 - accuracy: 0.9985 - val_loss: 0.8611 - val_accuracy: 0.8022
Epoch 472/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0550 - accuracy: 0.9955 - val_loss: 0.8613 - val_accuracy: 0.8022
Epoch 473/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0524 - accuracy: 0.9955 - val_loss: 0.8614 - val_accuracy: 0.8022
Epoch 474/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0521 - accuracy: 0.9970 - val_loss: 0.8614 - val_accuracy: 0.8022
Epoch 475/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0642 - accuracy: 0.9939 - val_loss: 0.8615 - val_accuracy: 0.8022
Epoch 476/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0553 - accuracy: 0.9970 - val_loss: 0.8615 - val_accuracy: 0.8022
Epoch 477/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0527 - accuracy: 0.9985 - val_loss: 0.8616 - val_accuracy: 0.8022
Epoch 478/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0538 - accuracy: 0.9985 - val_loss: 0.8615 - val_accuracy: 0.8022
Epoch 479/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0633 - accuracy: 0.9939 - val_loss: 0.8616 - val_accuracy: 0.8022
Epoch 480/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0585 - accuracy: 0.9985 - val_loss: 0.8615 - val_accuracy: 0.8022
Epoch 481/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0536 - accuracy: 0.9970 - val_loss: 0.8614 - val_accuracy: 0.8022
Epoch 482/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0550 - accuracy: 0.9970 - val_loss: 0.8612 - val_accuracy: 0.8022
Epoch 483/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0561 - accuracy: 1.0000 - val_loss: 0.8613 - val_accuracy: 0.8022
Epoch 484/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0518 - accuracy: 0.9970 - val_loss: 0.8614 - val_accuracy: 0.8022
Epoch 485/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0581 - accuracy: 0.9955 - val_loss: 0.8613 - val_accuracy: 0.8022
Epoch 486/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0560 - accuracy: 0.9939 - val_loss: 0.8613 - val_accuracy: 0.8022
Epoch 487/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0526 - accuracy: 0.9970 - val_loss: 0.8614 - val_accuracy: 0.8022
Epoch 488/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0504 - accuracy: 1.0000 - val_loss: 0.8615 - val_accuracy: 0.8022
Epoch 489/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0615 - accuracy: 0.9955 - val_loss: 0.8615 - val_accuracy: 0.8022
Epoch 490/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0639 - accuracy: 0.9924 - val_loss: 0.8616 - val_accuracy: 0.8022
Epoch 491/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0567 - accuracy: 0.9955 - val_loss: 0.8616 - val_accuracy: 0.8022
Epoch 492/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0651 - accuracy: 0.9985 - val_loss: 0.8618 - val_accuracy: 0.8022
Epoch 493/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0554 - accuracy: 0.9955 - val_loss: 0.8618 - val_accuracy: 0.8022
Epoch 494/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0546 - accuracy: 1.0000 - val_loss: 0.8618 - val_accuracy: 0.8022
Epoch 495/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0543 - accuracy: 0.9955 - val_loss: 0.8619 - val_accuracy: 0.8022
Epoch 496/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0556 - accuracy: 0.9985 - val_loss: 0.8619 - val_accuracy: 0.8060
Epoch 497/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0414 - accuracy: 1.0000 - val_loss: 0.8620 - val_accuracy: 0.8060
Epoch 498/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0585 - accuracy: 0.9955 - val_loss: 0.8619 - val_accuracy: 0.8060
Epoch 499/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0578 - accuracy: 0.9955 - val_loss: 0.8618 - val_accuracy: 0.8060
Epoch 500/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0499 - accuracy: 0.9970 - val_loss: 0.8619 - val_accuracy: 0.8060
Epoch 501/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0578 - accuracy: 0.9955 - val_loss: 0.8617 - val_accuracy: 0.8060
Epoch 502/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0557 - accuracy: 0.9955 - val_loss: 0.8617 - val_accuracy: 0.8097
Epoch 503/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0594 - accuracy: 0.9955 - val_loss: 0.8616 - val_accuracy: 0.8097
Epoch 504/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0518 - accuracy: 0.9955 - val_loss: 0.8615 - val_accuracy: 0.8097
Epoch 505/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0593 - accuracy: 0.9985 - val_loss: 0.8614 - val_accuracy: 0.8097
Epoch 506/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0543 - accuracy: 0.9985 - val_loss: 0.8613 - val_accuracy: 0.8097
Epoch 507/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0543 - accuracy: 0.9939 - val_loss: 0.8611 - val_accuracy: 0.8097
Epoch 508/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0518 - accuracy: 0.9985 - val_loss: 0.8608 - val_accuracy: 0.8097
Epoch 509/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0469 - accuracy: 0.9985 - val_loss: 0.8606 - val_accuracy: 0.8097
Epoch 510/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0573 - accuracy: 0.9970 - val_loss: 0.8606 - val_accuracy: 0.8097
Epoch 511/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0563 - accuracy: 0.9894 - val_loss: 0.8605 - val_accuracy: 0.8097
Epoch 512/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0593 - accuracy: 0.9955 - val_loss: 0.8604 - val_accuracy: 0.8097
Epoch 513/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0534 - accuracy: 0.9970 - val_loss: 0.8603 - val_accuracy: 0.8097
Epoch 514/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0597 - accuracy: 0.9955 - val_loss: 0.8602 - val_accuracy: 0.8097
Epoch 515/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0551 - accuracy: 0.9985 - val_loss: 0.8602 - val_accuracy: 0.8097
Epoch 516/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0507 - accuracy: 0.9985 - val_loss: 0.8602 - val_accuracy: 0.8097
Epoch 517/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0516 - accuracy: 1.0000 - val_loss: 0.8602 - val_accuracy: 0.8097
Epoch 518/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0661 - accuracy: 0.9939 - val_loss: 0.8601 - val_accuracy: 0.8097
Epoch 519/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0523 - accuracy: 0.9970 - val_loss: 0.8600 - val_accuracy: 0.8097
Epoch 520/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0527 - accuracy: 0.9985 - val_loss: 0.8599 - val_accuracy: 0.8097
Epoch 521/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0619 - accuracy: 0.9970 - val_loss: 0.8600 - val_accuracy: 0.8097
Epoch 522/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0525 - accuracy: 1.0000 - val_loss: 0.8599 - val_accuracy: 0.8097
Epoch 523/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0478 - accuracy: 1.0000 - val_loss: 0.8598 - val_accuracy: 0.8097
Epoch 524/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0577 - accuracy: 0.9970 - val_loss: 0.8597 - val_accuracy: 0.8097
Epoch 525/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0542 - accuracy: 0.9985 - val_loss: 0.8597 - val_accuracy: 0.8097
Epoch 526/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0633 - accuracy: 0.9955 - val_loss: 0.8596 - val_accuracy: 0.8097
Epoch 527/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0718 - accuracy: 0.9939 - val_loss: 0.8595 - val_accuracy: 0.8097
Epoch 528/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0525 - accuracy: 0.9985 - val_loss: 0.8594 - val_accuracy: 0.8097
Epoch 529/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0515 - accuracy: 1.0000 - val_loss: 0.8593 - val_accuracy: 0.8097
Epoch 530/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0555 - accuracy: 0.9955 - val_loss: 0.8592 - val_accuracy: 0.8097
Epoch 531/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0520 - accuracy: 1.0000 - val_loss: 0.8591 - val_accuracy: 0.8097
Epoch 532/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0554 - accuracy: 0.9970 - val_loss: 0.8591 - val_accuracy: 0.8097
Epoch 533/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0507 - accuracy: 0.9985 - val_loss: 0.8591 - val_accuracy: 0.8097
Epoch 534/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0468 - accuracy: 0.9985 - val_loss: 0.8590 - val_accuracy: 0.8097
Epoch 535/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0544 - accuracy: 0.9955 - val_loss: 0.8590 - val_accuracy: 0.8097
Epoch 536/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0565 - accuracy: 0.9985 - val_loss: 0.8589 - val_accuracy: 0.8097
Epoch 537/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0625 - accuracy: 0.9955 - val_loss: 0.8590 - val_accuracy: 0.8097
Epoch 538/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0568 - accuracy: 0.9939 - val_loss: 0.8589 - val_accuracy: 0.8097
Epoch 539/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0469 - accuracy: 0.9985 - val_loss: 0.8589 - val_accuracy: 0.8097
Epoch 540/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0480 - accuracy: 0.9985 - val_loss: 0.8589 - val_accuracy: 0.8097
Epoch 541/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0535 - accuracy: 0.9955 - val_loss: 0.8590 - val_accuracy: 0.8097
Epoch 542/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0610 - accuracy: 0.9939 - val_loss: 0.8591 - val_accuracy: 0.8097
Epoch 543/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0528 - accuracy: 0.9985 - val_loss: 0.8591 - val_accuracy: 0.8097
Epoch 544/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0465 - accuracy: 0.9985 - val_loss: 0.8591 - val_accuracy: 0.8097
Epoch 545/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0548 - accuracy: 0.9970 - val_loss: 0.8591 - val_accuracy: 0.8097
Epoch 546/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0678 - accuracy: 0.9955 - val_loss: 0.8591 - val_accuracy: 0.8097
Epoch 547/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0461 - accuracy: 0.9985 - val_loss: 0.8591 - val_accuracy: 0.8097
Epoch 548/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0490 - accuracy: 1.0000 - val_loss: 0.8591 - val_accuracy: 0.8097
Epoch 549/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0547 - accuracy: 1.0000 - val_loss: 0.8592 - val_accuracy: 0.8097
Epoch 550/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0523 - accuracy: 0.9970 - val_loss: 0.8592 - val_accuracy: 0.8097
Epoch 551/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0565 - accuracy: 0.9985 - val_loss: 0.8592 - val_accuracy: 0.8097
Epoch 552/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0600 - accuracy: 0.9970 - val_loss: 0.8592 - val_accuracy: 0.8097
Epoch 553/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0534 - accuracy: 0.9970 - val_loss: 0.8593 - val_accuracy: 0.8097
Epoch 554/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0498 - accuracy: 0.9985 - val_loss: 0.8594 - val_accuracy: 0.8097
Epoch 555/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0484 - accuracy: 0.9985 - val_loss: 0.8594 - val_accuracy: 0.8097
Epoch 556/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0505 - accuracy: 0.9970 - val_loss: 0.8596 - val_accuracy: 0.8097
Epoch 557/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0539 - accuracy: 0.9985 - val_loss: 0.8597 - val_accuracy: 0.8097
Epoch 558/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0575 - accuracy: 0.9955 - val_loss: 0.8597 - val_accuracy: 0.8097
Epoch 559/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0496 - accuracy: 0.9970 - val_loss: 0.8597 - val_accuracy: 0.8097
Epoch 560/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0561 - accuracy: 0.9955 - val_loss: 0.8597 - val_accuracy: 0.8097
Epoch 561/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0498 - accuracy: 0.9985 - val_loss: 0.8598 - val_accuracy: 0.8097
Epoch 562/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0514 - accuracy: 0.9985 - val_loss: 0.8598 - val_accuracy: 0.8097
Epoch 563/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0599 - accuracy: 0.9985 - val_loss: 0.8600 - val_accuracy: 0.8097
Epoch 564/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0548 - accuracy: 0.9985 - val_loss: 0.8599 - val_accuracy: 0.8097
Epoch 565/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0576 - accuracy: 0.9970 - val_loss: 0.8599 - val_accuracy: 0.8097
Epoch 566/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0520 - accuracy: 0.9985 - val_loss: 0.8598 - val_accuracy: 0.8097
Epoch 567/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0446 - accuracy: 0.9985 - val_loss: 0.8598 - val_accuracy: 0.8097
Epoch 568/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0498 - accuracy: 0.9985 - val_loss: 0.8597 - val_accuracy: 0.8097
Epoch 569/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0539 - accuracy: 0.9985 - val_loss: 0.8598 - val_accuracy: 0.8097
Epoch 570/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0475 - accuracy: 1.0000 - val_loss: 0.8598 - val_accuracy: 0.8097
Epoch 571/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0554 - accuracy: 0.9985 - val_loss: 0.8599 - val_accuracy: 0.8097
Epoch 572/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0639 - accuracy: 0.9939 - val_loss: 0.8599 - val_accuracy: 0.8097
Epoch 573/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0414 - accuracy: 1.0000 - val_loss: 0.8599 - val_accuracy: 0.8097
Epoch 574/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0541 - accuracy: 0.9970 - val_loss: 0.8599 - val_accuracy: 0.8097
Epoch 575/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0603 - accuracy: 0.9985 - val_loss: 0.8601 - val_accuracy: 0.8097
Epoch 576/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0493 - accuracy: 0.9970 - val_loss: 0.8603 - val_accuracy: 0.8097
Epoch 577/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0524 - accuracy: 0.9970 - val_loss: 0.8603 - val_accuracy: 0.8097
Epoch 578/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0495 - accuracy: 0.9985 - val_loss: 0.8603 - val_accuracy: 0.8097
Epoch 579/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0490 - accuracy: 1.0000 - val_loss: 0.8603 - val_accuracy: 0.8097
Epoch 580/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0576 - accuracy: 0.9955 - val_loss: 0.8603 - val_accuracy: 0.8097
Epoch 581/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0592 - accuracy: 0.9985 - val_loss: 0.8603 - val_accuracy: 0.8097
Epoch 582/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0607 - accuracy: 0.9970 - val_loss: 0.8602 - val_accuracy: 0.8097
Epoch 583/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0506 - accuracy: 0.9955 - val_loss: 0.8600 - val_accuracy: 0.8097
Epoch 584/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0449 - accuracy: 0.9985 - val_loss: 0.8600 - val_accuracy: 0.8097
Epoch 585/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0566 - accuracy: 0.9985 - val_loss: 0.8599 - val_accuracy: 0.8097
Epoch 586/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0531 - accuracy: 0.9970 - val_loss: 0.8599 - val_accuracy: 0.8097
Epoch 587/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0522 - accuracy: 1.0000 - val_loss: 0.8598 - val_accuracy: 0.8097
Epoch 588/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0517 - accuracy: 0.9955 - val_loss: 0.8597 - val_accuracy: 0.8097
Epoch 589/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0513 - accuracy: 0.9985 - val_loss: 0.8596 - val_accuracy: 0.8097
Epoch 590/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0564 - accuracy: 0.9970 - val_loss: 0.8597 - val_accuracy: 0.8097
Epoch 591/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0580 - accuracy: 1.0000 - val_loss: 0.8595 - val_accuracy: 0.8097
Epoch 592/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0533 - accuracy: 0.9985 - val_loss: 0.8595 - val_accuracy: 0.8097
Epoch 593/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0468 - accuracy: 1.0000 - val_loss: 0.8595 - val_accuracy: 0.8097
Epoch 594/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0470 - accuracy: 0.9985 - val_loss: 0.8595 - val_accuracy: 0.8097
Epoch 595/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0557 - accuracy: 1.0000 - val_loss: 0.8596 - val_accuracy: 0.8097
Epoch 596/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0475 - accuracy: 0.9985 - val_loss: 0.8597 - val_accuracy: 0.8097
Epoch 597/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0535 - accuracy: 0.9939 - val_loss: 0.8598 - val_accuracy: 0.8097
Epoch 598/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0466 - accuracy: 0.9970 - val_loss: 0.8598 - val_accuracy: 0.8097
Epoch 599/600
660/660 [==============================] - 4s 6ms/step - loss: 0.0508 - accuracy: 0.9955 - val_loss: 0.8599 - val_accuracy: 0.8097
Epoch 600/600
660/660 [==============================] - 4s 5ms/step - loss: 0.0550 - accuracy: 0.9955 - val_loss: 0.8600 - val_accuracy: 0.8097



In [31]:

    
# Plot training & validation accuracy values
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()



In [0]: