在 Keras 中使用 Lambda 层时符号张量值错误
Symbolic tensor value error when using Lambda layer in Keras
ValueError: Layer lambda_47 was called with an input that isn't a symbolic tensor. Received type: <class 'tuple'>. Full input: [(<tf.Tensor 'lambda_45/Slice:0' shape=(110000, 1, 128) dtype=float32>, <tf.Tensor 'lambda_46/Slice:0' shape=(110000, 1, 128) dtype=float32>)]. All inputs to the layer should be tensors
我一直在尝试在模型定义中使用 keras 前端实现 tensorflow 操作。我在创建允许权重更新的 t运行sformation 层时遇到问题。我读过 Keras 的 Lambda 函数是执行此操作的关键，但我 运行 进入此错误。
这是我的代码：
### CONTROL VARIABLES (i.e. user input parameters)
dropout_rate = 0.5
batch_size = 128
nb_epochs = 40
#with tf.device('/gpu:0'):
### MODEL CREATION
X_input = Input(shape=input_shape, name='input_1')
# Input
X_i = Lambda(lambda x: tf.slice(x, [0,0,0], [110000,1,128]))(X_input) # Slicing out inphase column
X_q = Lambda(lambda x: tf.slice(x, [0,1,0], [110000,1,128]))(X_input) # Slicing out quadrature column
X_mag = Lambda(lambda x_i, x_q: tf.math.sqrt(tf.math.add(tf.math.square(x_i), tf.math.square(x_q))))((X_i, X_q)) # Acquiring magnitude of IQ
## THE SOURCE OF THE ERROR IS THE LINE ABOVE ^
## ITS USING TENSORFLOW OPERATORS TO FIND ABSOLUTE VALUE
X_phase = Lambda(lambda x_i, x_q: tf.math.atan2(x_i, x_q))((X_i, X_q)) # Acquiring phase of IQ
X = Concatenate(axis=1)([X_mag, X_phase]) # Combining into two column (magnitude,phase) tensor
X = Conv2D(128, kernel_size=(2,8), padding='same',data_format='channels_last')(X)
X = Activation('relu')(X)
X = Dropout(dropout_rate)(X)
X = Conv2D(64, kernel_size=(1,8), padding='same',data_format='channels_last')(X)
X = Activation('relu')(X)
X = Dropout(dropout_rate)(X)
X = Flatten()(X)
X = Dense(128, kernel_initializer='he_normal', activation='relu')(X)
X = Dropout(dropout_rate)(X)
X = Dense(len(classes), kernel_initializer='he_normal')(X)
X = Activation('softmax', name = 'labels')(X)
model = Model(inputs = X_input, outputs = X)
model.summary()
model.compile(optimizer=Adam(learning_rate), loss='categorical_crossentropy', metrics =['accuracy'])
完整堆栈跟踪错误：
The shape of x is (220000, 2, 128)
(110000, 2, 128) [2, 128]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in assert_input_compatibility(self, inputs)
278 try:
--> 279 K.is_keras_tensor(x)
280 except ValueError:
3 frames
/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in is_keras_tensor(x)
473 raise ValueError('Unexpectedly found an instance of type `' +
--> 474 str(type(x)) + '`. '
475 'Expected a symbolic tensor instance.')
ValueError: Unexpectedly found an instance of type `<class 'tuple'>`. Expected a symbolic tensor instance.
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-22-dba00eef4193> in <module>()
108 X_i = Lambda(lambda x: tf.slice(x, [0,0,0], [110000,1,128]))(X_input) # Slicing out inphase column
109 X_q = Lambda(lambda x: tf.slice(x, [0,1,0], [110000,1,128]))(X_input) # Slicing out quadrature column
--> 110 X_mag = Lambda(lambda x_i, x_q: tf.math.sqrt(tf.math.add(tf.math.square(x_i), tf.math.square(x_q))))((X_i, X_q)) # Acquiring magnitude of IQ
111 X_phase = Lambda(lambda x_i, x_q: tf.math.atan2(x_i, x_q))((X_i, X_q)) # Acquiring phase of IQ
112 X = Concatenate(axis=1)([X_mag, X_phase]) # Combining into two column (magnitude,phase) tensor
/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
412 # Raise exceptions in case the input is not compatible
413 # with the input_spec specified in the layer constructor.
--> 414 self.assert_input_compatibility(inputs)
415
416 # Collect input shapes to build layer.
/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in assert_input_compatibility(self, inputs)
283 'Received type: ' +
284 str(type(x)) + '. Full input: ' +
--> 285 str(inputs) + '. All inputs to the layer '
286 'should be tensors.')
287
ValueError: Layer lambda_47 was called with an input that isn't a symbolic tensor. Received type: <class 'tuple'>. Full input: [(<tf.Tensor 'lambda_45/Slice:0' shape=(110000, 1, 128) dtype=float32>, <tf.Tensor 'lambda_46/Slice:0' shape=(110000, 1, 128) dtype=float32>)]. All inputs to the layer should be tensors.
所以错误发生在 "X_mag = Lambda" 行。我搜索了所有相关的堆栈溢出帖子，none 似乎说明了此处嵌入的 tf 操作。请帮我解决这个问题！
这两天真是难倒我了。
您不能将 元组 传递给层作为它们的输入。相反，您应该使用 lists。此外，因此，Lambda 层中的 lambda 函数仅接受 一个 输入参数，即一个列表，您可以使用索引访问其元素：
X_mag = Lambda(lambda x: tf.math.sqrt(
tf.math.add(tf.math.square(x[0]), tf.math.square(x[1]))))([X_i, X_q]) # Acquiring magnitude of IQ
X_phase = Lambda(lambda x: tf.math.atan2(x[0], x[1]))([X_i, X_q]) # Acquiring phase of IQ
ValueError: Layer lambda_47 was called with an input that isn't a symbolic tensor. Received type: <class 'tuple'>. Full input: [(<tf.Tensor 'lambda_45/Slice:0' shape=(110000, 1, 128) dtype=float32>, <tf.Tensor 'lambda_46/Slice:0' shape=(110000, 1, 128) dtype=float32>)]. All inputs to the layer should be tensors
我一直在尝试在模型定义中使用 keras 前端实现 tensorflow 操作。我在创建允许权重更新的 t运行sformation 层时遇到问题。我读过 Keras 的 Lambda 函数是执行此操作的关键，但我 运行 进入此错误。
这是我的代码：
### CONTROL VARIABLES (i.e. user input parameters)
dropout_rate = 0.5
batch_size = 128
nb_epochs = 40
#with tf.device('/gpu:0'):
### MODEL CREATION
X_input = Input(shape=input_shape, name='input_1')
# Input
X_i = Lambda(lambda x: tf.slice(x, [0,0,0], [110000,1,128]))(X_input) # Slicing out inphase column
X_q = Lambda(lambda x: tf.slice(x, [0,1,0], [110000,1,128]))(X_input) # Slicing out quadrature column
X_mag = Lambda(lambda x_i, x_q: tf.math.sqrt(tf.math.add(tf.math.square(x_i), tf.math.square(x_q))))((X_i, X_q)) # Acquiring magnitude of IQ
## THE SOURCE OF THE ERROR IS THE LINE ABOVE ^
## ITS USING TENSORFLOW OPERATORS TO FIND ABSOLUTE VALUE
X_phase = Lambda(lambda x_i, x_q: tf.math.atan2(x_i, x_q))((X_i, X_q)) # Acquiring phase of IQ
X = Concatenate(axis=1)([X_mag, X_phase]) # Combining into two column (magnitude,phase) tensor
X = Conv2D(128, kernel_size=(2,8), padding='same',data_format='channels_last')(X)
X = Activation('relu')(X)
X = Dropout(dropout_rate)(X)
X = Conv2D(64, kernel_size=(1,8), padding='same',data_format='channels_last')(X)
X = Activation('relu')(X)
X = Dropout(dropout_rate)(X)
X = Flatten()(X)
X = Dense(128, kernel_initializer='he_normal', activation='relu')(X)
X = Dropout(dropout_rate)(X)
X = Dense(len(classes), kernel_initializer='he_normal')(X)
X = Activation('softmax', name = 'labels')(X)
model = Model(inputs = X_input, outputs = X)
model.summary()
model.compile(optimizer=Adam(learning_rate), loss='categorical_crossentropy', metrics =['accuracy'])
完整堆栈跟踪错误：
The shape of x is (220000, 2, 128)
(110000, 2, 128) [2, 128]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in assert_input_compatibility(self, inputs)
278 try:
--> 279 K.is_keras_tensor(x)
280 except ValueError:
3 frames
/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py in is_keras_tensor(x)
473 raise ValueError('Unexpectedly found an instance of type `' +
--> 474 str(type(x)) + '`. '
475 'Expected a symbolic tensor instance.')
ValueError: Unexpectedly found an instance of type `<class 'tuple'>`. Expected a symbolic tensor instance.
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
<ipython-input-22-dba00eef4193> in <module>()
108 X_i = Lambda(lambda x: tf.slice(x, [0,0,0], [110000,1,128]))(X_input) # Slicing out inphase column
109 X_q = Lambda(lambda x: tf.slice(x, [0,1,0], [110000,1,128]))(X_input) # Slicing out quadrature column
--> 110 X_mag = Lambda(lambda x_i, x_q: tf.math.sqrt(tf.math.add(tf.math.square(x_i), tf.math.square(x_q))))((X_i, X_q)) # Acquiring magnitude of IQ
111 X_phase = Lambda(lambda x_i, x_q: tf.math.atan2(x_i, x_q))((X_i, X_q)) # Acquiring phase of IQ
112 X = Concatenate(axis=1)([X_mag, X_phase]) # Combining into two column (magnitude,phase) tensor
/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in __call__(self, inputs, **kwargs)
412 # Raise exceptions in case the input is not compatible
413 # with the input_spec specified in the layer constructor.
--> 414 self.assert_input_compatibility(inputs)
415
416 # Collect input shapes to build layer.
/usr/local/lib/python3.6/dist-packages/keras/engine/base_layer.py in assert_input_compatibility(self, inputs)
283 'Received type: ' +
284 str(type(x)) + '. Full input: ' +
--> 285 str(inputs) + '. All inputs to the layer '
286 'should be tensors.')
287
ValueError: Layer lambda_47 was called with an input that isn't a symbolic tensor. Received type: <class 'tuple'>. Full input: [(<tf.Tensor 'lambda_45/Slice:0' shape=(110000, 1, 128) dtype=float32>, <tf.Tensor 'lambda_46/Slice:0' shape=(110000, 1, 128) dtype=float32>)]. All inputs to the layer should be tensors.
所以错误发生在 "X_mag = Lambda" 行。我搜索了所有相关的堆栈溢出帖子，none 似乎说明了此处嵌入的 tf 操作。请帮我解决这个问题！
这两天真是难倒我了。
您不能将 元组 传递给层作为它们的输入。相反，您应该使用 lists。此外，因此，Lambda 层中的 lambda 函数仅接受 一个 输入参数，即一个列表，您可以使用索引访问其元素：
X_mag = Lambda(lambda x: tf.math.sqrt(
tf.math.add(tf.math.square(x[0]), tf.math.square(x[1]))))([X_i, X_q]) # Acquiring magnitude of IQ
X_phase = Lambda(lambda x: tf.math.atan2(x[0], x[1]))([X_i, X_q]) # Acquiring phase of IQ