def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.learning_rate
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay *
K.cast(self.iterations, K.dtype(self.decay))))
t = K.cast(self.iterations, K.floatx()) + 1
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
ms = [
K.zeros(K.int_shape(p), dtype=K.dtype(p), name='m_' + str(i))
for (i, p) in enumerate(params)
]
vs = [
K.zeros(K.int_shape(p), dtype=K.dtype(p), name='v_' + str(i))
for (i, p) in enumerate(params)
]
if self.amsgrad:
vhats = [
K.zeros(K.int_shape(p),
dtype=K.dtype(p),
name='vhat_' + str(i)) for (i, p) in enumerate(params)
]
else:
vhats = [
K.zeros(1, name='vhat_' + str(i)) for i in range(len(params))
]
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g - m_t)
if self.amsgrad:
vhat_t = K.maximum(vhat, v_t)
p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
self.updates.append(K.update(vhat, vhat_t))
else:
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def get_updates(self, loss, params):
# 是否更新
cond = K.equal(self.iterations % self.grad_accum_steps, 0)
cond = K.cast(cond, K.floatx())
# 获取梯度
grads = self.get_gradients(loss, params)
self.accum_grads = [
K.zeros(shape=K.int_shape(p),
dtype=K.dtype(p),
name='accum_grad_{}'.format(i))
for i, p in enumerate(params)
]
old_update = K.update
def new_update(x, new_x):
new_x = cond * new_x + (1 - cond) * x
return old_update(x, new_x)
K.update = new_update
updates = super(NewOptimizer, self).get_updates(loss, params)
K.update = old_update
# 累计更新
with K.control_dependencies(updates):
acc_updates = [
K.update(ag, g + (1 - cond) * ag)
for ag, g in zip(self.accum_grads, grads)
]
return acc_updates
def dense_loss(self, y_true, y_pred):
"""y_true需要是one hot形式
"""
# 导出mask并转换数据类型
mask = K.all(K.greater(y_pred, -1e6), axis=2, keepdims=True)
mask = K.cast(mask, K.floatx())
# 计算目标分数
y_true, y_pred = y_true * mask, y_pred * mask
target_score = self.path_score(y_pred, y_true)
# 递归计算log Z
init_states = [y_pred[:, 0]]
y_pred = K.concatenate([y_pred, mask], axis=2)
input_length = K.int_shape(y_pred[:, 1:])[1]
log_norm, _, _ = K.rnn(self.log_norm_step,
y_pred[:, 1:],
init_states,
input_length=input_length) # 最后一步的log Z向量
log_norm = K.logsumexp(log_norm, 1) # logsumexp得标量
# 计算损失 -log p
return log_norm - target_score
def call(self, x, mask=None):
x0 = x
if mask is not None:
mask = K.cast(mask, K.floatx())
mask = K.expand_dims(mask, 2)
# x = x0 * mask if mask is not None else x0
x0 = Lambda(lambda x_: x_, output_shape=lambda s: s)(x0) # drop mask so do not put mask to conv1d
x = self.conv1d(x0)
x, g = x[:, :, :self.o_dim], x[:, :, self.o_dim:]
if self.dropout_rate is not None:
g = K.in_train_phase(K.dropout(g, self.dropout_rate), g)
g = K.sigmoid(g)
# mask is none
mask = mask if mask is not None else K.ones_like(x)
if self.skip_connection:
if K.int_shape(x0)[-1] != self.o_dim:
x0 = self.conv1d_1x1(x0)
return (x0 * (1 - g) + x * g) * mask
return x * g * mask
train_generator = data_generator(data=train_data, batch_size=batch_size)
valid_generator = data_generator(data=valid_data, batch_size=batch_size)
train_transfer_generator = data_generator(data=train_data,
batch_size=batch_size,
transfer=True,
data_augmentation=True)
# 加载预训练模型（3层）
teacher = build_transformer_model(config_path=config_path,
checkpoint_path=checkpoint_path,
return_keras_model=False,
num_hidden_layers=num_hidden_layers,
model='bert')
# 判别模型
x_in = Input(shape=K.int_shape(teacher.output)[1:])
x = Lambda(lambda x: x[:, 0])(x_in)
x = Dense(units=num_classes, activation='softmax')(x)
classifier = Model(x_in, x)
teacher_model = Model(teacher.inputs, classifier(teacher.output))
teacher_model.compile(
loss='sparse_categorical_crossentropy',
optimizer=Adam(2e-5), # 用足够小的学习率
metrics=['sparse_categorical_accuracy'],
)
teacher_model.summary()
class FastbertClassifierLayer(Layer):
# 加载预训练模型（12层）
predecessor = build_transformer_model(config_path=config_path,
checkpoint_path=checkpoint_path,
return_keras_model=False,
prefix='Predecessor-')
# 加载预训练模型（3层）
successor = build_transformer_model(config_path=config_path,
checkpoint_path=checkpoint_path,
return_keras_model=False,
num_hidden_layers=3,
prefix='Successor-')
# 判别模型
x_in = Input(shape=K.int_shape(predecessor.output)[1:])
x = Dense(num_labels)(x_in)
CRF = ConditionalRandomField(lr_multiplier=2)
x = CRF(x)
classifier = Model(x_in, x)
opt = Adam(learning_rate=lr)
predecessor_model = Model(predecessor.inputs, classifier(predecessor.outputs))
predecessor_model.compile(
loss=predecessor_model.layers[-1].layers[-1].sparse_loss,
optimizer=opt,
metrics=[CRF.sparse_accuracy])
predecessor_model.summary()
def call(self, inputs, mask=None):
if mask is not None:
mask = K.cast(mask, K.floatx())
mask = K.expand_dims(mask, 2)
inputs = inputs - (1.0 - mask) * 1e12
return K.softmax(inputs, 1)
# build model
model = build_transformer_model(
config_path,
checkpoint_path,
)
inputs = [
Input(shape=K.int_shape(model.inputs[0])[1:]),
Input(shape=K.int_shape(model.inputs[1])[1:])
]
output = model(inputs)
output = SinCosPositionEmbedding(K.int_shape(output)[-1])(output)
output = Dropout(0.5)(output)
output = Dense(384, activation='tanh')(output)
att = AttentionPooling1D(name='attention_pooling_1')(output)
output = ConcatSeq2Vec()([output, att])
output = DGCNN(dilation_rate=1, dropout_rate=0.1)(output)
output = DGCNN(dilation_rate=2, dropout_rate=0.1)(output)
output = DGCNN(dilation_rate=5, dropout_rate=0.1)(output)
def compute_output_shape(self, input_shape):
if self._mode == 'embedding':
return super(Embedding, self).compute_output_shape(input_shape)
return input_shape[:2] + (K.int_shape(self.embeddings)[0], )
(f1, precision, recall, self.best_val_f1)
)
f1, precision, recall = evaluate(self.model, test_data)
print(
'test: f1: %.5f, precision: %.5f, recall: %.5f\n' %
(f1, precision, recall)
)
teacher = build_transformer_model(
config_path,
checkpoint_path,
return_keras_model=False
)
x_in = Input(shape=K.int_shape(teacher.output)[1:])
x = Lambda(lambda x: x)(x_in)
# softmax
x = Dense(num_labels, activation='softmax')(x)
teacher_classifier = Model(x_in, x)
teacher_model = Model(teacher.input, teacher_classifier(teacher.output))
teacher_model.summary()
teacher_model.compile(
loss='sparse_categorical_crossentropy',
optimizer=Adam(learning_rate),
metrics=['sparse_categorical_accuracy']
)
student = build_transformer_model(
if f1 >= self.best_val_f1:
self.best_val_f1 = f1
self.model.save_weights(self.model_name)
print(
'valid: f1: %.5f, precision: %.5f, recall: %.5f, best f1: %.5f\n'
% (f1, precision, recall, self.best_val_f1))
f1, precision, recall = evaluate(self.model, test_data)
print('test: f1: %.5f, precision: %.5f, recall: %.5f\n' %
(f1, precision, recall))
bert = build_transformer_model(config_path,
checkpoint_path,
return_keras_model=False)
x_in = Input(shape=K.int_shape(bert.output)[1:])
x = Lambda(lambda x: x)(x_in)
# softmax
x = Dense(num_labels, activation='softmax')(x)
bert_classifier = Model(x_in, x)
teacher_model = Model(bert.input, bert_classifier(bert.output))
teacher_model.summary()
teacher_model.compile(loss='sparse_categorical_crossentropy',
optimizer=Adam(learning_rate),
metrics=['sparse_categorical_accuracy'])
if __name__ == '__main__':
teacher_model_name = './best_teacher_model.weights'
teacher_evaluator = Evaluator(teacher_model_name)
