def basic_accuracy(self, y_true, y_pred, go_backwards=False):
"""训练过程中显示逐帧准确率的函数，排除了mask的影响
此处y_true需要是整数形式（非one hot）
"""
# 导出mask并转换数据类型
mask = K.all(K.greater(y_pred, -1e6), axis=2)
mask = K.cast(mask, K.floatx())
# y_true需要重新明确一下shape和dtype
y_true = K.reshape(y_true, K.shape(y_pred)[:-1])
y_true = K.cast(y_true, 'int32')
# 反转相关
if self.hidden_dim is None:
if go_backwards: # 是否反转序列
y_true, y_pred = self.reverse_sequence([y_true, y_pred], mask)
trans = K.transpose(self.trans)
else:
trans = self.trans
histoty = K.gather(trans, y_true)
else:
if go_backwards: # 是否反转序列
y_true, y_pred = self.reverse_sequence([y_true, y_pred], mask)
r_trans, l_trans = self.l_trans, self.r_trans
else:
l_trans, r_trans = self.l_trans, self.r_trans
histoty = K.gather(l_trans, y_true)
histoty = tf.einsum('bnd,kd->bnk', histoty, r_trans)
# 计算逐标签accuracy
histoty = K.concatenate([y_pred[:, :1], histoty[:, :-1]], 1)
y_pred = (y_pred + histoty) / 2
y_pred = K.cast(K.argmax(y_pred, 2), 'int32')
isequal = K.cast(K.equal(y_true, y_pred), K.floatx())
return K.sum(isequal * mask) / K.sum(mask)
def basic_loss(self, y_true, y_pred, go_backwards=False):
"""y_true需要是整数形式（非one hot）
"""
# 导出mask并转换数据类型
mask = K.all(K.greater(y_pred, -1e6), axis=2)
mask = K.cast(mask, K.floatx())
# y_true需要重新明确一下shape和dtype
y_true = K.reshape(y_true, K.shape(y_pred)[:-1])
y_true = K.cast(y_true, 'int32')
# 反转相关
if self.hidden_dim is None:
if go_backwards: # 是否反转序列
y_true, y_pred = self.reverse_sequence([y_true, y_pred], mask)
trans = K.transpose(self.trans)
else:
trans = self.trans
histoty = K.gather(trans, y_true)
else:
if go_backwards: # 是否反转序列
y_true, y_pred = self.reverse_sequence([y_true, y_pred], mask)
r_trans, l_trans = self.l_trans, self.r_trans
else:
l_trans, r_trans = self.l_trans, self.r_trans
histoty = K.gather(l_trans, y_true)
histoty = tf.einsum('bnd,kd->bnk', histoty, r_trans)
# 计算loss
histoty = K.concatenate([y_pred[:, :1], histoty[:, :-1]], 1)
y_pred = (y_pred + histoty) / 2
loss = K.sparse_categorical_crossentropy(
y_true, y_pred, from_logits=True
)
return K.sum(loss * mask) / K.sum(mask)
def call(self, inputs, mode='embedding'):
"""新增mode参数，可以为embedding或dense。如果为embedding，
则等价于普通Embedding层；如果为dense，则等价于无bias的Dense层。
"""
self._current_mode = mode
if mode == 'embedding':
return super(Embedding, self).call(inputs)
else:
kernel = K.transpose(self.embeddings)
return K.dot(inputs, kernel)
