def backward(self, dout=1):
batch_size = self.t.shape[0]
dx = self.y.copy()
dx[np.arange(batch_size), self.t] -= 1
dx *= dout
dx = dx / batch_size
return dx
def backward(self, dout=1):
ts, ys, mask, (N, T, V) = self.cache
dx = ys
dx[np.arange(N * T), ts] -= 1
dx *= dout
dx /= mask.sum()
dx *= mask[:, np.newaxis]
dx = dx.reshape((N, T, V))
return dx
def cross_entropy_error(y, t):
if y.ndim == 1:
t = t.reshape(1, t.size)
y = y.reshape(1, y.size)
# 教師データがone-hot-vectorの場合、正解ラベルのインデックスに変換
if t.size == y.size:
t = t.argmax(axis=1)
batch_size = y.shape[0]
return -np.sum(np.log(y[np.arange(batch_size), t] + 1e-7)) / batch_size
def backward(self, dout=1):
ts, ys, mask, (N, T, V) = self.cache
dx = ys
dx[np.arange(N * T), ts] -= 1
dx *= dout
dx /= mask.sum()
# when data is ignore_label, gradient is 0
dx *= mask[:, np.newaxis]
dx = dx.reshape((N, T, V))
return dx
def cross_entropy_error(y, t):
"""
Cross entropy error.
One of the loss functions.
"""
if y.ndim == 1:
t = t.reshape(1, t.size)
y = y.reshape(1, y.size)
# convert to index of correct label when training data is one-hot-vector
# (教師データが one-hot-vector の場合、正解ラベルのインデックスに変換
if t.size == y.size:
t = t.argmax(axis=1)
batch_size = y.shape[0]
return -np.sum(np.log(y[np.arange(batch_size), t] + 1e-7)) / batch_size
def forward(self, xs, ts):
N, T, V = xs.shape
if ts.ndim == 3:
ts = ts.argmax(asix=2)
mask = (ts != self.ignore_label)
xs = xs.reshape(N * T, V)
ts = ts.reshape(N * T)
mask = mask.reshape(N * T)
ys = softmax(xs)
ls = np.log(ys[np.arange(N * T), ts])
ls *= mask
loss = -np.sum(ls)
loss /= mask.sum()
self.cache = (ts, ys, mask, (N, T, V))
return loss
def forward(self, xs, ts):
N, T, V = xs.shape
if ts.ndim == 3: # 教師ラベルがone-hotベクトルの場合
ts = ts.argmax(axis=2)
mask = (ts != self.ignore_label)
# バッチ分と時系列分をまとめる（reshape）
xs = xs.reshape(N * T, V)
ts = ts.reshape(N * T)
mask = mask.reshape(N * T)
ys = softmax(xs)
ls = np.log(ys[np.arange(N * T), ts])
ls *= mask # ignore_labelに該当するデータは損失を0にする
loss = -np.sum(ls)
loss /= mask.sum()
self.cache = (ts, ys, mask, (N, T, V))
return loss
def forward(self, xs, ts):
N, T, V = xs.shape
# when train-label is one-hot vector
if ts.ndim == 3:
ts = ts.argmax(axis=2)
mask = (ts != self.ignore_label)
# reshape batch with recurrent
xs = xs.reshape(N * T, V)
ts = ts.reshape(N * T)
mask = mask.reshape(N * T)
ys = softmax(xs)
ls = np.log(ys[np.arange(N * T), ts])
# when data is ignore_label, loss is 0
ls *= mask
loss = -np.sum(ls)
loss /= mask.sum()
self.cache = (ts, ys, mask, (N, T, V))
return loss
batch_size=batch_size,
max_grad=max_grad)
correct_num = 0
for i in range(len(x_test)):
question, correct = x_test[[i]], t_test[[i]]
verbose = i < 10
correct_num += eval_seq2seq(model, question, correct, id_to_char,
verbose)
acc = float(correct_num) / len(x_test)
acc_list.append(acc)
print('val acc %.3f%%' % (acc * 100))
return acc_list
# Plot as graph
acc_nomal = train_eval(x_train, x_test, is_peeky=False)
acc_rev = train_eval(x_train_rev, x_test_rev, is_peeky=False)
acc_pk = train_eval(x_train_rev, x_test_rev, is_peeky=True)
x_normal = np.arange(len(acc_nomal))
x_reverse = np.arange(len(acc_rev))
x_pk = np.arange(len(acc_pk))
plt.plot(x_normal, acc_nomal, marker='o', label="normal")
plt.plot(x_reverse, acc_rev, marker='v', label="reverse")
plt.plot(x_pk, acc_pk, marker='^', label="peeky+rev")
plt.xlabel('epochs')
plt.ylabel('accuracy')
plt.ylim(0, 1.0)
plt.legend()
plt.savefig('train_seq2seq.png')
