Keras load_model is causing 'TypeError: Keyword argument not understood:' when using custom layer in model
Keras load_model is causing 'TypeError: Keyword argument not understood:' when using custom layer in model
我正在构建一个带有自定义注意力层的模型，如 Tensorflow's nmt tutorial 中所实现的那样。为了解决我的问题，我使用了相同的层代码并做了一些我发现的建议。
问题是当我有这个自定义层时，我无法在保存模型后从文件加载模型。这是图层 class:
class BahdanauAttention(layers.Layer):
def __init__(self, output_dim=30, **kwargs):
super(BahdanauAttention, self).__init__(**kwargs)
self.W1 = tf.keras.layers.Dense(output_dim)
self.W2 = tf.keras.layers.Dense(output_dim)
self.V = tf.keras.layers.Dense(1)
def call(self, inputs, **kwargs):
query = inputs[0]
values = inputs[1]
query_with_time_axis = tf.expand_dims(query, 1)
score = self.V(tf.nn.tanh(
self.W1(query_with_time_axis) + self.W2(values)))
attention_weights = tf.nn.softmax(score, axis=1)
context_vector = attention_weights * values
context_vector = tf.reduce_sum(context_vector, axis=1)
return context_vector, attention_weights
def get_config(self):
config = super(BahdanauAttention, self).get_config()
config.update({
'W1': self.W1,
'W2': self.W2,
'V': self.V,
})
return config
我正在使用 keras 的 ModelCheckpoint 回调保存模型：
path = os.path.join(self.dir, 'model_{}'.format(self.timestamp))
callbacks.append(ModelCheckpoint(path, save_best_only=True, monitor='val_loss', mode='min'))
稍后，我像这样加载模型：
self.model = load_model(path, custom_objects={'BahdanauAttention': BahdanauAttention, 'custom_loss': self.custom_loss})
这是我收到的错误消息：
raise TypeError(error_message, kwarg)
TypeError: ('Keyword argument not understood:', 'W1')
和完整的追溯：
Traceback (most recent call last):
File "models/lstm.py", line 49, in load_model
'dollar_mape_loss': self.dollar_mape_loss})
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/save.py", line 187, in load_model
return saved_model_load.load(filepath, compile, options)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 121, in load
path, options=options, loader_cls=KerasObjectLoader)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 633, in load_internal
ckpt_options)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 194, in __init__
super(KerasObjectLoader, self).__init__(*args, **kwargs)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 130, in __init__
self._load_all()
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 215, in _load_all
self._layer_nodes = self._load_layers()
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 315, in _load_layers
layers[node_id] = self._load_layer(proto.user_object, node_id)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 341, in _load_layer
obj, setter = self._revive_from_config(proto.identifier, metadata, node_id)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 359, in _revive_from_config
self._revive_layer_from_config(metadata, node_id))
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 417, in _revive_layer_from_config
generic_utils.serialize_keras_class_and_config(class_name, config))
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/layers/serialization.py", line 175, in deserialize
printable_module_name='layer')
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 360, in deserialize_keras_object
return cls.from_config(cls_config)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 697, in from_config
return cls(**config)
File "models/lstm.py", line 310, in __init__
super(BahdanauAttention, self).__init__(**kwargs)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 318, in __init__
generic_utils.validate_kwargs(kwargs, allowed_kwargs)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 778, in validate_kwargs
raise TypeError(error_message, kwarg)
TypeError: ('Keyword argument not understood:', 'W1')
类似的问题表明代码使用了不同版本的 Keras 和 TensorFlow。我只使用 TensorFlow 的 Keras。这些是进口商品
from tensorflow.keras.models import load_model
from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint
from tensorflow.keras import layers
@user7331538 尝试用 path='anymodel_name.h5'
替换 path=f os.path.join(self.dir, 'model_{}'.format(self.timestamp))
在 keras' documentation on custom layers 之后，他们建议不应在 __init__() 中初始化任何权重，而应在 build() 中初始化。这样就不需要在配置中添加权重，错误也会得到解决。
这是更新后的自定义图层class:
class BahdanauAttention(tf.keras.layers.Layer):
def __init__(self, units=30, **kwargs):
super(BahdanauAttention, self).__init__(**kwargs)
self.units = units
def build(self, input_shape):
self.W1 = tf.keras.layers.Dense(self.units)
self.W2 = tf.keras.layers.Dense(self.units)
self.V = tf.keras.layers.Dense(1)
def call(self, inputs, **kwargs):
query = inputs[0]
values = inputs[1]
query_with_time_axis = tf.expand_dims(query, 1)
score = self.V(tf.nn.tanh(
self.W1(query_with_time_axis) + self.W2(values)))
attention_weights = tf.nn.softmax(score, axis=1)
context_vector = attention_weights * values
context_vector = tf.reduce_sum(context_vector, axis=1)
return context_vector, attention_weights
def get_config(self):
config = super(BahdanauAttention, self).get_config()
config.update({
'units': self.units,
})
return config
我也有这个问题。
试了很多方法，发现可以用这个方法。
首先，建立模型
model = TextAttBiRNN(maxlen, max_features, embedding_dims).get_model()
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
二、负重：
我用这个解决了问题：
model_file = "/content/drive/My Drive/dga/output_data/model_lstm_att_test_v6.h5"
model.load_weights(model_file)
然后，我们会发现该模型可以使用。
这样就避免了前面的问题
我正在构建一个带有自定义注意力层的模型，如 Tensorflow's nmt tutorial 中所实现的那样。为了解决我的问题，我使用了相同的层代码并做了一些我发现的建议。
问题是当我有这个自定义层时，我无法在保存模型后从文件加载模型。这是图层 class:
class BahdanauAttention(layers.Layer):
def __init__(self, output_dim=30, **kwargs):
super(BahdanauAttention, self).__init__(**kwargs)
self.W1 = tf.keras.layers.Dense(output_dim)
self.W2 = tf.keras.layers.Dense(output_dim)
self.V = tf.keras.layers.Dense(1)
def call(self, inputs, **kwargs):
query = inputs[0]
values = inputs[1]
query_with_time_axis = tf.expand_dims(query, 1)
score = self.V(tf.nn.tanh(
self.W1(query_with_time_axis) + self.W2(values)))
attention_weights = tf.nn.softmax(score, axis=1)
context_vector = attention_weights * values
context_vector = tf.reduce_sum(context_vector, axis=1)
return context_vector, attention_weights
def get_config(self):
config = super(BahdanauAttention, self).get_config()
config.update({
'W1': self.W1,
'W2': self.W2,
'V': self.V,
})
return config
我正在使用 keras 的 ModelCheckpoint 回调保存模型：
path = os.path.join(self.dir, 'model_{}'.format(self.timestamp))
callbacks.append(ModelCheckpoint(path, save_best_only=True, monitor='val_loss', mode='min'))
稍后，我像这样加载模型：
self.model = load_model(path, custom_objects={'BahdanauAttention': BahdanauAttention, 'custom_loss': self.custom_loss})
这是我收到的错误消息：
raise TypeError(error_message, kwarg)
TypeError: ('Keyword argument not understood:', 'W1')
和完整的追溯：
Traceback (most recent call last):
File "models/lstm.py", line 49, in load_model
'dollar_mape_loss': self.dollar_mape_loss})
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/save.py", line 187, in load_model
return saved_model_load.load(filepath, compile, options)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 121, in load
path, options=options, loader_cls=KerasObjectLoader)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 633, in load_internal
ckpt_options)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 194, in __init__
super(KerasObjectLoader, self).__init__(*args, **kwargs)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/saved_model/load.py", line 130, in __init__
self._load_all()
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 215, in _load_all
self._layer_nodes = self._load_layers()
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 315, in _load_layers
layers[node_id] = self._load_layer(proto.user_object, node_id)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 341, in _load_layer
obj, setter = self._revive_from_config(proto.identifier, metadata, node_id)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 359, in _revive_from_config
self._revive_layer_from_config(metadata, node_id))
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/saving/saved_model/load.py", line 417, in _revive_layer_from_config
generic_utils.serialize_keras_class_and_config(class_name, config))
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/layers/serialization.py", line 175, in deserialize
printable_module_name='layer')
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 360, in deserialize_keras_object
return cls.from_config(cls_config)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 697, in from_config
return cls(**config)
File "models/lstm.py", line 310, in __init__
super(BahdanauAttention, self).__init__(**kwargs)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/training/tracking/base.py", line 457, in _method_wrapper
result = method(self, *args, **kwargs)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py", line 318, in __init__
generic_utils.validate_kwargs(kwargs, allowed_kwargs)
File "venv/m/lib/python3.7/site-packages/tensorflow/python/keras/utils/generic_utils.py", line 778, in validate_kwargs
raise TypeError(error_message, kwarg)
TypeError: ('Keyword argument not understood:', 'W1')
类似的问题表明代码使用了不同版本的 Keras 和 TensorFlow。我只使用 TensorFlow 的 Keras。这些是进口商品
from tensorflow.keras.models import load_model
from tensorflow.keras.callbacks import EarlyStopping, CSVLogger, ModelCheckpoint
from tensorflow.keras import layers
@user7331538 尝试用 path='anymodel_name.h5'
path=f os.path.join(self.dir, 'model_{}'.format(self.timestamp))
在 keras' documentation on custom layers 之后，他们建议不应在 __init__() 中初始化任何权重，而应在 build() 中初始化。这样就不需要在配置中添加权重，错误也会得到解决。
这是更新后的自定义图层class:
class BahdanauAttention(tf.keras.layers.Layer):
def __init__(self, units=30, **kwargs):
super(BahdanauAttention, self).__init__(**kwargs)
self.units = units
def build(self, input_shape):
self.W1 = tf.keras.layers.Dense(self.units)
self.W2 = tf.keras.layers.Dense(self.units)
self.V = tf.keras.layers.Dense(1)
def call(self, inputs, **kwargs):
query = inputs[0]
values = inputs[1]
query_with_time_axis = tf.expand_dims(query, 1)
score = self.V(tf.nn.tanh(
self.W1(query_with_time_axis) + self.W2(values)))
attention_weights = tf.nn.softmax(score, axis=1)
context_vector = attention_weights * values
context_vector = tf.reduce_sum(context_vector, axis=1)
return context_vector, attention_weights
def get_config(self):
config = super(BahdanauAttention, self).get_config()
config.update({
'units': self.units,
})
return config
我也有这个问题。 试了很多方法，发现可以用这个方法。 首先，建立模型
model = TextAttBiRNN(maxlen, max_features, embedding_dims).get_model()
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
二、负重： 我用这个解决了问题：
model_file = "/content/drive/My Drive/dga/output_data/model_lstm_att_test_v6.h5"
model.load_weights(model_file)
然后，我们会发现该模型可以使用。
这样就避免了前面的问题