In [1]:
# Data
import numpy as np
with open('data/text_data/japan.txt', 'r') as f:
txt = f.read()
X = []
y = []
char_to_idx = {char: i for i, char in enumerate(set(txt))}
idx_to_char = {i: char for i, char in enumerate(set(txt))}
X = np.array([char_to_idx[x] for x in txt])
y = [char_to_idx[x] for x in txt[1:]]
y.append(char_to_idx['.'])
y = np.array(y)
In [2]:
# Model or Network
import impl.layer as l
class GRU:
def __init__(self, D, H, L, char2idx, idx2char, p_dropout):
self.D = D
self.H = H
self.L = L
self.char2idx = char2idx
self.idx2char = idx2char
self.vocab_size = len(char2idx)
self.losses = {'train':[], 'smooth train':[]}
self.p_dropout = p_dropout
# Model parameters weights and biases
Z = H + D
m = dict(
Wz=np.random.randn(Z, H) / np.sqrt(Z / 2.),
Wr=np.random.randn(Z, H) / np.sqrt(Z / 2.),
Wh=np.random.randn(Z, H) / np.sqrt(Z / 2.),
Wy=np.random.randn(H, D) / np.sqrt(H / 2.),
bz=np.zeros((1, H)),
br=np.zeros((1, H)),
bh=np.zeros((1, H)),
by=np.zeros((1, D))
)
self.model = []
for _ in range(self.L):
self.model.append(m)
def initial_state(self):
return np.zeros((1, self.H))
def forward(self, X, h, m, train):
Wz, Wr, Wh, Wy = m['Wz'], m['Wr'], m['Wh'], m['Wy']
bz, br, bh, by = m['bz'], m['br'], m['bh'], m['by']
X_one_hot = X.copy()
h_old = h.copy()
X = np.column_stack((h_old, X_one_hot))
hz, hz_cache = l.fc_forward(X, Wz, bz)
hz, hz_sigm_cache = l.sigmoid_forward(hz)
hr, hr_cache = l.fc_forward(X, Wr, br)
hr, hr_sigm_cache = l.sigmoid_forward(hr)
X_prime = np.column_stack((hr * h_old, X_one_hot))
hh, hh_cache = l.fc_forward(X_prime, Wh, bh)
hh, hh_tanh_cache = l.tanh_forward(hh)
h = (1. - hz) * h_old + hz * hh
y, y_cache = l.fc_forward(h, Wy, by)
if train:
y, do_cache = self.dropout_forward(X=y, p_dropout=self.p_dropout)
cache = (X, X_prime, h_old, hz, hz_cache, hz_sigm_cache, hr, hr_cache, hr_sigm_cache, hh, hh_cache,
hh_tanh_cache, y_cache, do_cache)
else: # not train but test
cache = (X, X_prime, h_old, hz, hz_cache, hz_sigm_cache, hr, hr_cache, hr_sigm_cache, hh, hh_cache,
hh_tanh_cache, y_cache)
return y, h, cache
def backward(self, dy, dh, cache, train):
if train: # include dropout_cache/do_cache
X, X_prime, h_old, hz, hz_cache, hz_sigm_cache, hr, hr_cache, hr_sigm_cache, hh, hh_cache, hh_tanh_cache, y_cache, do_cache = cache
dy = self.dropout_backward(dout=dy, cache=do_cache)
else: # not train but test
X, X_prime, h_old, hz, hz_cache, hz_sigm_cache, hr, hr_cache, hr_sigm_cache, hh, hh_cache, hh_tanh_cache, y_cache = cache
dh_next = dh.copy()
dh, dWy, dby = l.fc_backward(dy, y_cache)
dh += dh_next
dhh = hz * dh
dh_old1 = (1. - hz) * dh
dhz = hh * dh - h_old * dh
dhh = l.tanh_backward(dhh, hh_tanh_cache)
dX_prime, dWh, dbh = l.fc_backward(dhh, hh_cache)
dh_prime = dX_prime[:, :self.H]
dh_old2 = hr * dh_prime
dhr = h_old * dh_prime
dhr = l.sigmoid_backward(dhr, hr_sigm_cache)
dXr, dWr, dbr = l.fc_backward(dhr, hr_cache)
dhz = l.sigmoid_backward(dhz, hz_sigm_cache)
dXz, dWz, dbz = l.fc_backward(dhz, hz_cache)
dX = dXr + dXz
dh_old3 = dX[:, :self.H]
dh = dh_old1 + dh_old2 + dh_old3
dX = dX[:, self.H:] + dX_prime[:, self.H:]
grad = dict(Wz=dWz, Wr=dWr, Wh=dWh, Wy=dWy, bz=dbz, br=dbr, bh=dbh, by=dby)
return dX, dh, grad
def dropout_forward(self, X, p_dropout):
u = np.random.binomial(1, p_dropout, size=X.shape) / p_dropout
# q = 1-p_dropout
# u = np.random.binomial(1, q, size=X.shape)
out = X * u
cache = u
return out, cache
def dropout_backward(self, dout, cache):
dX = dout * cache
return dX
def train_forward(self, X_train, h):
ys, caches = [], []
h_init = h.copy()
h = []
for _ in range(self.L):
h.append(h_init.copy())
caches.append([])
for X in X_train:
X_one_hot = np.zeros(self.D)
X_one_hot[X] = 1.
y = X_one_hot.reshape(1, -1)
for layer in range(self.L):
y, h[layer], cache = self.forward(y, h[layer], self.model[layer], train=True)
caches[layer].append(cache)
ys.append(y)
return ys, caches
def cross_entropy(self, y_pred, y_train):
m = y_pred.shape[0]
prob = l.softmax(y_pred)
log_like = -np.log(prob[range(m), y_train])
data_loss = np.sum(log_like) / m
return data_loss
def dcross_entropy(self, y_pred, y_train):
m = y_pred.shape[0]
grad_y = l.softmax(y_pred)
grad_y[range(m), y_train] -= 1.0
grad_y /= m
return grad_y
def loss_function(self, y_train, ys):
loss, dys = 0.0, []
for y_pred, y in zip(ys, y_train):
loss += self.cross_entropy(y_pred, y)
dy = self.dcross_entropy(y_pred, y)
dys.append(dy)
return loss, dys
def train_backward(self, dys, caches):
dh, grad, grads = [], [], []
for layer in range(self.L):
dh.append(np.zeros((1, self.H)))
grad.append({key: np.zeros_like(val) for key, val in self.model[layer].items()})
grads.append({key: np.zeros_like(val) for key, val in self.model[layer].items()})
for t in reversed(range(len(dys))):
dX = dys[t]
for layer in reversed(range(self.L)):
dX, dh[layer], grad[layer] = self.backward(dX, dh[layer], caches[layer][t], train=True)
for key in grad[layer].keys():
grads[layer][key] += grad[layer][key]
return dX, grads
def test(self, X_seed, h, size):
chars = [self.idx2char[X_seed]]
idx_list = list(range(self.vocab_size))
X = X_seed
h_init = h.copy()
h = []
for _ in range(self.L):
h.append(h_init.copy())
for _ in range(size):
X_one_hot = np.zeros(self.D)
X_one_hot[X] = 1.
y = X_one_hot.reshape(1, -1)
for layer in range(self.L):
y, h[layer], _ = self.forward(y, h[layer], self.model[layer], train=False)
prob = l.softmax(y)
idx = np.random.choice(idx_list, p=prob.ravel())
chars.append(self.idx2char[idx])
X = idx
return ''.join(chars)
In [3]:
# Backprop
from sklearn.utils import shuffle as skshuffle
def get_minibatch(X, y, minibatch_size, shuffle):
minibatches = []
if shuffle:
X, y = skshuffle(X, y)
for i in range(0, X.shape[0], minibatch_size):
# for i in range(0, X.shape[0] - minibatch_size +1, 1):
X_mini = X[i:i + minibatch_size]
y_mini = y[i:i + minibatch_size]
minibatches.append((X_mini, y_mini))
return minibatches
def adam_rnn(nn, X_train, y_train, alpha, mb_size, n_iter, print_after):
M, R = [], []
for layer in range(nn.L):
M.append({key: np.zeros_like(val) for key, val in nn.model[layer].items()})
R.append({key: np.zeros_like(val) for key, val in nn.model[layer].items()})
beta1 = .99
beta2 = .999
eps = 1e-8
state = nn.initial_state()
smooth_loss = 1.0
minibatches = get_minibatch(X_train, y_train, mb_size, shuffle=False)
# Epochs
for iter in range(1, n_iter + 1):
# No batches/ full batches/ batch files
# Minibacthes
for idx in range(len(minibatches)):
X_mini, y_mini = minibatches[idx]
ys, caches = nn.train_forward(X_mini, state)
loss, dys = nn.loss_function(y_mini, ys)
_, grads = nn.train_backward(dys, caches)
nn.losses['train'].append(loss)
smooth_loss = (0.999 * smooth_loss) + (0.001 * loss)
nn.losses['smooth train'].append(smooth_loss)
for layer in range(nn.L):
for key in grads[layer].keys(): #key, value: items
M[layer][key] = l.exp_running_avg(M[layer][key], grads[layer][key], beta1)
R[layer][key] = l.exp_running_avg(R[layer][key], grads[layer][key]**2, beta2)
m_k_hat = M[layer][key] / (1. - (beta1**(iter)))
r_k_hat = R[layer][key] / (1. - (beta2**(iter)))
nn.model[layer][key] -= alpha * m_k_hat / (np.sqrt(r_k_hat) + eps)
# Print loss and test sample
if iter % print_after == 0:
print('Iter-{} loss: {:.4f}'.format(iter, loss))
sample = nn.test(X_mini[0], state, size=100)
print(sample)
return nn
In [4]:
# Hyper-parameters
time_step = 100 # width, minibatch size and test sample size as well
num_layers = 1 # depth
n_iter = 10000 # epochs
alpha = 1e-4 # learning_rate
p_dropout = 0.95 # q=1-p, q=keep_prob and p=dropout.
print_after = 10 # nn_iter//10 # print training loss, valid, and test
num_hidden_units = 64 # num_hidden_units in hidden layer
num_input_units = len(char_to_idx) # vocab_size = len(char_to_idx)
# Build the network and learning it or optimizing it using SGD
net = GRU(D=num_input_units, H=num_hidden_units, L=num_layers, char2idx=char_to_idx, idx2char=idx_to_char,
p_dropout=p_dropout)
# Start learning using BP-SGD-ADAM
adam_rnn(nn=net, X_train=X, y_train=y, alpha=alpha, mb_size=time_step, n_iter=n_iter, print_after=print_after)
# # Display the learning curve and losses for training, validation, and testing
# %matplotlib inline
# %config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
plt.plot(net.losses['train'], label='Train loss')
plt.plot(net.losses['smooth train'], label='Train smooth loss')
plt.legend()
plt.show()
In [ ]: