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如何在Keras 2.0中“合并”序列模型?

keras python

twhale · 技术社区 · 8 年前

merged_model.add(Merge([model1, model2], mode='concat'))

这仍然可以正常工作,但给出了一个警告:

"The `Merge` layer is deprecated and will be removed after 08/2017. Use
instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc."

然而,研究Keras文档并尝试添加add(),并没有产生任何效果。我读过一些有同样问题的人的帖子,但在我下面的案例中没有找到有效的解决方案。有什么建议吗?

model = Sequential()
model1 = Sequential()
model1.add(Dense(300, input_dim=40, activation='relu', name='layer_1'))
model2 = Sequential()
model2.add(Dense(300, input_dim=40, activation='relu', name='layer_2'))
merged_model = Sequential()

merged_model.add(Merge([model1, model2], mode='concat'))

merged_model.add(Dense(1, activation='softmax', name='output_layer'))
merged_model.compile(loss='binary_crossentropy', optimizer='adam', 
metrics=['accuracy'])

checkpoint = ModelCheckpoint('weights.h5', monitor='val_acc',
save_best_only=True, verbose=2)
early_stopping = EarlyStopping(monitor="val_loss", patience=5)

merged_model.fit([x1, x2], y=y, batch_size=384, epochs=200,
             verbose=1, validation_split=0.1, shuffle=True, 
callbacks=[early_stopping, checkpoint])

from keras.layers.merge import concatenate
merged_model.add(concatenate([model1, model2]))

这是错误消息:

Traceback (most recent call last):
  File "/anaconda/lib/python3.6/site- packages/keras/engine/topology.py", line 425, 
in assert_input_compatibility
    K.is_keras_tensor(x)
  File "/anaconda/lib/python3.6/site-
packages/keras/backend/tensorflow_backend.py", line 403, in     is_keras_tensor
    raise ValueError('Unexpectedly found an instance of type `' +
 str(type(x)) + '`. '
ValueError: Unexpectedly found an instance of type 
`<class'keras.models.Sequential'>`. Expected a symbolic tensor instance.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "quoradeeptest_simple1.py", line 78, in <module>
    merged_model.add(concatenate([model1, model2]))
  File "/anaconda/lib/python3.6/site-packages/keras/layers/merge.py",
 line 600, in concatenate return Concatenate(axis=axis, **kwargs)(inputs)
  File "/anaconda/lib/python3.6/site-   packages/keras/engine/topology.py", 
line 558, in __call__self.assert_input_compatibility(inputs)
  File "/anaconda/lib/python3.6/site-packages/keras/engine/topology.py", line 431, 
 in assert_input_compatibility str(inputs) + '.All inputs to the layer '
ValueError: Layer concatenate_1 was called with an input that isn't a
symbolic tensor. Received type: <class 'keras.models.Sequential'>. 
Full input: [<keras.models.Sequential object at 0x140fa7ba8>,
<keras.models.Sequential object at 0x140fabdd8>]. All inputs to the
layer should be tensors.

3 回复 | 直到 8 年前

Kent Sommer 8 年前

该警告的意思是,不同的模式现在已被拆分为各自的层,而不是使用具有特定模式的合并层。

所以 Merge(mode='concat') 现在是 concatenate(axis=-1)

但是,由于您希望合并模型而不是层,因此这在您的情况下不起作用。您需要做的是使用功能模型,因为基本的顺序模型类型不再支持这种行为。

在您的情况下,这意味着代码应更改为以下内容:

from keras.layers.merge import concatenate
from keras.models import Model, Sequential
from keras.layers import Dense, Input

model1_in = Input(shape=(27, 27, 1))
model1_out = Dense(300, input_dim=40, activation='relu', name='layer_1')(model1_in)
model1 = Model(model1_in, model1_out)

model2_in = Input(shape=(27, 27, 1))
model2_out = Dense(300, input_dim=40, activation='relu', name='layer_2')(model2_in)
model2 = Model(model2_in, model2_out)


concatenated = concatenate([model1_out, model2_out])
out = Dense(1, activation='softmax', name='output_layer')(concatenated)

merged_model = Model([model1_in, model2_in], out)
merged_model.compile(loss='binary_crossentropy', optimizer='adam', 
metrics=['accuracy'])

checkpoint = ModelCheckpoint('weights.h5', monitor='val_acc',
save_best_only=True, verbose=2)
early_stopping = EarlyStopping(monitor="val_loss", patience=5)

merged_model.fit([x1, x2], y=y, batch_size=384, epochs=200,
             verbose=1, validation_split=0.1, shuffle=True, 
callbacks=[early_stopping, checkpoint])

James 5 年前

from keras import Sequential, Model
from keras.layers import Embedding, GlobalAveragePooling1D, Dense, concatenate
import numpy as np

model1 = Sequential()
model1.add(Embedding(20, 10, trainable=True))
model1.add(GlobalAveragePooling1D())
model1.add(Dense(1, activation='sigmoid'))
model2 = Sequential()
model2.add(Embedding(20, 10, trainable=True))
model2.add(GlobalAveragePooling1D())
model2.add(Dense(1, activation='sigmoid'))

model_concat = concatenate([model1.output, model2.output], axis=-1)
model_concat = Dense(1, activation='softmax')(model_concat)
model = Model(inputs=[model1.input, model2.input], outputs=model_concat)

model.compile(loss='binary_crossentropy', optimizer='adam')

X_train_1 = np.random.randint(0, 20, (10000, 256))
X_train_2 = np.random.randint(0, 20, (10000, 256))
Y_train = np.random.randint(0, 2, 10000)

model.fit([X_train_1, X_train_2], Y_train, batch_size=1000, epochs=200,
              verbose=True)

ilan 8 年前

除非有充分的理由将模型分开,否则可以(而且应该)在单个模型中具有相同的拓扑。类似于:

input1 = Input(shape=(27, 27, 1))
dense1 = Dense(300, activation='relu', name='layer_1')(input1)
input2 = Input(shape=(27, 27, 1))
dense2 = Dense(300, activation='relu', name='layer_2')(input2)
merged = concatenate([dense1, dense2])
out = Dense(1, activation='softmax', name='output_layer')(merged)
model = Model(inputs = [input1, input2], outputs = [out])