无法保存模型架构（bilstm + attention）
Unable to save model architecture (bilstm + attention)
我正在处理多标签文本分类问题。我正在尝试使用 bilstm 模型添加注意力机制。注意力机制代码取自here。我无法保存模型架构并收到下面提到的错误。我的tensorflow版本-2.2.0
from keras import backend as K
def dot_product(x, kernel):
if K.backend() == 'tensorflow':
return K.squeeze(K.dot(x, K.expand_dims(kernel)), axis=-1)
else:
return K.dot(x, kernel)
class AttentionWithContext(tf.keras.layers.Layer):
"""
# Input shape
3D tensor with shape: `(samples, steps, features)`.
# Output shape
2D tensor with shape: `(samples, features)`.
"""
def __init__(self,
W_regularizer=None, u_regularizer=None, b_regularizer=None,
W_constraint=None, u_constraint=None, b_constraint=None,
bias=True, **kwargs):
self.supports_masking = True
self.init = tf.keras.initializers.get('glorot_uniform')
self.W_regularizer = tf.keras.regularizers.get(W_regularizer)
self.u_regularizer = tf.keras.regularizers.get(u_regularizer)
self.b_regularizer = tf.keras.regularizers.get(b_regularizer)
self.W_constraint = tf.keras.constraints.get(W_constraint)
self.u_constraint = tf.keras.constraints.get(u_constraint)
self.b_constraint = tf.keras.constraints.get(b_constraint)
self.bias = bias
super(AttentionWithContext, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape) == 3
self.W = self.add_weight(shape=(input_shape[-1], input_shape[-1],),
initializer=self.init,
name='{}_W'.format(self.name),
regularizer=self.W_regularizer,
constraint=self.W_constraint)
if self.bias:
self.b = self.add_weight(shape=(input_shape[-1],),
initializer='zero',
name='{}_b'.format(self.name),
regularizer=self.b_regularizer,
constraint=self.b_constraint)
self.u = self.add_weight(shape=(input_shape[-1],),
initializer=self.init,
name='{}_u'.format(self.name),
regularizer=self.u_regularizer,
constraint=self.u_constraint)
super(AttentionWithContext, self).build(input_shape)
def compute_mask(self, input, input_mask=None):
# do not pass the mask to the next layers
return None
def call(self, x, mask=None):
uit = dot_product(x, self.W)
if self.bias:
uit += self.b
uit = K.tanh(uit)
ait = dot_product(uit, self.u)
a = K.exp(ait)
# apply mask after the exp. will be re-normalized next
if mask is not None:
# Cast the mask to floatX to avoid float64 upcasting in theano
a *= K.cast(mask, K.floatx())
# in some cases especially in the early stages of training the sum may be almost zero
# and this results in NaN's. A workaround is to add a very small positive number ε to the sum.
# a /= K.cast(K.sum(a, axis=1, keepdims=True), K.floatx())
a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())
a = K.expand_dims(a)
weighted_input = x * a
return K.sum(weighted_input, axis=1)
def compute_output_shape(self, input_shape):
return input_shape[0], input_shape[-1]
def lstm_with_attention(embedding_matrix,
**kwargs):
STAMP = kwargs['STAMP']
max_seq_length = kwargs['max_seq_length']
EMBEDDING_DIM = kwargs['EMBEDDING_DIM']
nb_words = kwargs['nb_words']
inp = tf.keras.Input(shape=(max_seq_length,))
embedded_seq = tf.keras.layers.Embedding(nb_words,
EMBEDDING_DIM,
weights=[embedding_matrix],
trainable=False)(inp)
x_1_bilstm = tf.keras.layers.Bidirectional(tf.compat.v1.keras.layers.CuDNNLSTM(128, return_sequences=True))(embedded_seq)
x_1_bn = tf.keras.layers.BatchNormalization()(x_1_bilstm)
x_2_bilstm = tf.keras.layers.Bidirectional(tf.compat.v1.keras.layers.CuDNNLSTM(64, return_sequences=True))(x_1_bn)
attention = AttentionWithContext()(x_2_bilstm)
x = tf.keras.layers.Dense(64, activation="relu")(attention)
x = tf.keras.layers.Dense(1, activation="sigmoid")(x)
model = tf.keras.Model(inputs=inp, outputs=x)
optimizer = tf.keras.optimizers.Adam()
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
model.summary()
with open(STAMP + ".json", "w") as json_file: json_file.write(model.to_json())
return model, attention
用注意力构建 lstm
embedding_matrix, nb_words = get_embedding('glove',word_index)
model, attention_layer = lstm_with_attention(embedding_matrix,STAMP=STAMP,max_seq_length=max_seq_length,nb_words=nb_words,EMBEDDING_DIM=EMBEDDING_DIM)
错误
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-54-4be6d63890f7> in <module>()
20 # # BiGRU CuDNN
21 embedding_matrix, nb_words = get_embedding('glove',word_index)
---> 22 model, attention_layer = lstm_with_attention(embedding_matrix,STAMP=STAMP,max_seq_length=max_seq_length,nb_words=nb_words,EMBEDDING_DIM=EMBEDDING_DIM)
23 # gru_model = make_cudnn_gru_f(max_seq_length,embedding_matrix,loss_func=macro_soft_f1,eval_metric=macro_f1)
24 # model = gru_model()
7 frames
<ipython-input-51-1ae8a90521d0> in lstm_with_attention(embedding_matrix, **kwargs)
115 model.summary()
116
--> 117 with open(STAMP + ".json", "w") as json_file: json_file.write(model.to_json())
118 return model, attention
119
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in to_json(self, **kwargs)
1296 A JSON string.
1297 """
-> 1298 model_config = self._updated_config()
1299 return json.dumps(
1300 model_config, default=serialization.get_json_type, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in _updated_config(self)
1274 from tensorflow.python.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top
1275
-> 1276 config = self.get_config()
1277 model_config = {
1278 'class_name': self.__class__.__name__,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in get_config(self)
966 if not self._is_graph_network:
967 raise NotImplementedError
--> 968 return copy.deepcopy(get_network_config(self))
969
970 @classmethod
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in get_network_config(network, serialize_layer_fn)
2117 filtered_inbound_nodes.append(node_data)
2118
-> 2119 layer_config = serialize_layer_fn(layer)
2120 layer_config['name'] = layer.name
2121 layer_config['inbound_nodes'] = filtered_inbound_nodes
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py in serialize_keras_object(instance)
273 return serialize_keras_class_and_config(
274 name, {_LAYER_UNDEFINED_CONFIG_KEY: True})
--> 275 raise e
276 serialization_config = {}
277 for key, item in config.items():
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py in serialize_keras_object(instance)
268 name = get_registered_name(instance.__class__)
269 try:
--> 270 config = instance.get_config()
271 except NotImplementedError as e:
272 if _SKIP_FAILED_SERIALIZATION:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in get_config(self)
634 raise NotImplementedError('Layer %s has arguments in `__init__` and '
635 'therefore must override `get_config`.' %
--> 636 self.__class__.__name__)
637 return config
638
NotImplementedError: Layer AttentionWithContext has arguments in `__init__` and therefore must override `get_config`.
这是一个 TensorFlow 功能，用于通知您它不知道如何重建您的层，因为它不知道如何处理您的配置。这是来自 tensorflow 文档的引述：
get_config()
Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).
要解决这个问题，你只需要在你的class中创建一个__init__对应的get_config方法来指示TensorFlow如何重新实例化你的层。
def get_config(self):
config = super().get_config().copy()
config.update({
'W_regularizer': self.W_regularizer,
'u_regularizer': self.u_regularizer,
'b_regularizer': self.b_regularizer,
'W_constraint': self.W_constraint,
'u_constraint': self.u_constraint,
'b_constraint': self.b_constraint,
'bias': self.bias,
})
return config
那么你应该可以保存和加载它了。
我正在处理多标签文本分类问题。我正在尝试使用 bilstm 模型添加注意力机制。注意力机制代码取自here。我无法保存模型架构并收到下面提到的错误。我的tensorflow版本-2.2.0
from keras import backend as K
def dot_product(x, kernel):
if K.backend() == 'tensorflow':
return K.squeeze(K.dot(x, K.expand_dims(kernel)), axis=-1)
else:
return K.dot(x, kernel)
class AttentionWithContext(tf.keras.layers.Layer):
"""
# Input shape
3D tensor with shape: `(samples, steps, features)`.
# Output shape
2D tensor with shape: `(samples, features)`.
"""
def __init__(self,
W_regularizer=None, u_regularizer=None, b_regularizer=None,
W_constraint=None, u_constraint=None, b_constraint=None,
bias=True, **kwargs):
self.supports_masking = True
self.init = tf.keras.initializers.get('glorot_uniform')
self.W_regularizer = tf.keras.regularizers.get(W_regularizer)
self.u_regularizer = tf.keras.regularizers.get(u_regularizer)
self.b_regularizer = tf.keras.regularizers.get(b_regularizer)
self.W_constraint = tf.keras.constraints.get(W_constraint)
self.u_constraint = tf.keras.constraints.get(u_constraint)
self.b_constraint = tf.keras.constraints.get(b_constraint)
self.bias = bias
super(AttentionWithContext, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape) == 3
self.W = self.add_weight(shape=(input_shape[-1], input_shape[-1],),
initializer=self.init,
name='{}_W'.format(self.name),
regularizer=self.W_regularizer,
constraint=self.W_constraint)
if self.bias:
self.b = self.add_weight(shape=(input_shape[-1],),
initializer='zero',
name='{}_b'.format(self.name),
regularizer=self.b_regularizer,
constraint=self.b_constraint)
self.u = self.add_weight(shape=(input_shape[-1],),
initializer=self.init,
name='{}_u'.format(self.name),
regularizer=self.u_regularizer,
constraint=self.u_constraint)
super(AttentionWithContext, self).build(input_shape)
def compute_mask(self, input, input_mask=None):
# do not pass the mask to the next layers
return None
def call(self, x, mask=None):
uit = dot_product(x, self.W)
if self.bias:
uit += self.b
uit = K.tanh(uit)
ait = dot_product(uit, self.u)
a = K.exp(ait)
# apply mask after the exp. will be re-normalized next
if mask is not None:
# Cast the mask to floatX to avoid float64 upcasting in theano
a *= K.cast(mask, K.floatx())
# in some cases especially in the early stages of training the sum may be almost zero
# and this results in NaN's. A workaround is to add a very small positive number ε to the sum.
# a /= K.cast(K.sum(a, axis=1, keepdims=True), K.floatx())
a /= K.cast(K.sum(a, axis=1, keepdims=True) + K.epsilon(), K.floatx())
a = K.expand_dims(a)
weighted_input = x * a
return K.sum(weighted_input, axis=1)
def compute_output_shape(self, input_shape):
return input_shape[0], input_shape[-1]
def lstm_with_attention(embedding_matrix,
**kwargs):
STAMP = kwargs['STAMP']
max_seq_length = kwargs['max_seq_length']
EMBEDDING_DIM = kwargs['EMBEDDING_DIM']
nb_words = kwargs['nb_words']
inp = tf.keras.Input(shape=(max_seq_length,))
embedded_seq = tf.keras.layers.Embedding(nb_words,
EMBEDDING_DIM,
weights=[embedding_matrix],
trainable=False)(inp)
x_1_bilstm = tf.keras.layers.Bidirectional(tf.compat.v1.keras.layers.CuDNNLSTM(128, return_sequences=True))(embedded_seq)
x_1_bn = tf.keras.layers.BatchNormalization()(x_1_bilstm)
x_2_bilstm = tf.keras.layers.Bidirectional(tf.compat.v1.keras.layers.CuDNNLSTM(64, return_sequences=True))(x_1_bn)
attention = AttentionWithContext()(x_2_bilstm)
x = tf.keras.layers.Dense(64, activation="relu")(attention)
x = tf.keras.layers.Dense(1, activation="sigmoid")(x)
model = tf.keras.Model(inputs=inp, outputs=x)
optimizer = tf.keras.optimizers.Adam()
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
model.summary()
with open(STAMP + ".json", "w") as json_file: json_file.write(model.to_json())
return model, attention
用注意力构建 lstm
embedding_matrix, nb_words = get_embedding('glove',word_index)
model, attention_layer = lstm_with_attention(embedding_matrix,STAMP=STAMP,max_seq_length=max_seq_length,nb_words=nb_words,EMBEDDING_DIM=EMBEDDING_DIM)
错误
---------------------------------------------------------------------------
NotImplementedError Traceback (most recent call last)
<ipython-input-54-4be6d63890f7> in <module>()
20 # # BiGRU CuDNN
21 embedding_matrix, nb_words = get_embedding('glove',word_index)
---> 22 model, attention_layer = lstm_with_attention(embedding_matrix,STAMP=STAMP,max_seq_length=max_seq_length,nb_words=nb_words,EMBEDDING_DIM=EMBEDDING_DIM)
23 # gru_model = make_cudnn_gru_f(max_seq_length,embedding_matrix,loss_func=macro_soft_f1,eval_metric=macro_f1)
24 # model = gru_model()
7 frames
<ipython-input-51-1ae8a90521d0> in lstm_with_attention(embedding_matrix, **kwargs)
115 model.summary()
116
--> 117 with open(STAMP + ".json", "w") as json_file: json_file.write(model.to_json())
118 return model, attention
119
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in to_json(self, **kwargs)
1296 A JSON string.
1297 """
-> 1298 model_config = self._updated_config()
1299 return json.dumps(
1300 model_config, default=serialization.get_json_type, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in _updated_config(self)
1274 from tensorflow.python.keras import __version__ as keras_version # pylint: disable=g-import-not-at-top
1275
-> 1276 config = self.get_config()
1277 model_config = {
1278 'class_name': self.__class__.__name__,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in get_config(self)
966 if not self._is_graph_network:
967 raise NotImplementedError
--> 968 return copy.deepcopy(get_network_config(self))
969
970 @classmethod
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/network.py in get_network_config(network, serialize_layer_fn)
2117 filtered_inbound_nodes.append(node_data)
2118
-> 2119 layer_config = serialize_layer_fn(layer)
2120 layer_config['name'] = layer.name
2121 layer_config['inbound_nodes'] = filtered_inbound_nodes
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py in serialize_keras_object(instance)
273 return serialize_keras_class_and_config(
274 name, {_LAYER_UNDEFINED_CONFIG_KEY: True})
--> 275 raise e
276 serialization_config = {}
277 for key, item in config.items():
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py in serialize_keras_object(instance)
268 name = get_registered_name(instance.__class__)
269 try:
--> 270 config = instance.get_config()
271 except NotImplementedError as e:
272 if _SKIP_FAILED_SERIALIZATION:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py in get_config(self)
634 raise NotImplementedError('Layer %s has arguments in `__init__` and '
635 'therefore must override `get_config`.' %
--> 636 self.__class__.__name__)
637 return config
638
NotImplementedError: Layer AttentionWithContext has arguments in `__init__` and therefore must override `get_config`.
这是一个 TensorFlow 功能，用于通知您它不知道如何重建您的层，因为它不知道如何处理您的配置。这是来自 tensorflow 文档的引述：
get_config()
Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).
要解决这个问题，你只需要在你的class中创建一个__init__对应的get_config方法来指示TensorFlow如何重新实例化你的层。
def get_config(self):
config = super().get_config().copy()
config.update({
'W_regularizer': self.W_regularizer,
'u_regularizer': self.u_regularizer,
'b_regularizer': self.b_regularizer,
'W_constraint': self.W_constraint,
'u_constraint': self.u_constraint,
'b_constraint': self.b_constraint,
'bias': self.bias,
})
return config
那么你应该可以保存和加载它了。