How to get the output shape of a layer in Keras?

Question

I have the following code in Keras (Basically I am modifying this code for my use) and I get this error:

'ValueError: Error when checking target: expected conv3d_3 to have 5 dimensions, but got array with shape (10, 4096)'

Code:

from keras.models import Sequential from keras.layers.convolutional import Conv3D from keras.layers.convolutional_recurrent import ConvLSTM2D from keras.layers.normalization import BatchNormalization import numpy as np import pylab as plt from keras import layers  # We create a layer which take as input movies of shape # (n_frames, width, height, channels) and returns a movie # of identical shape.  model = Sequential() model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),                    input_shape=(None, 64, 64, 1),                    padding='same', return_sequences=True)) model.add(BatchNormalization())  model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),                    padding='same', return_sequences=True)) model.add(BatchNormalization())  model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),                    padding='same', return_sequences=True)) model.add(BatchNormalization())  model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),                    padding='same', return_sequences=True)) model.add(BatchNormalization())  model.add(Conv3D(filters=1, kernel_size=(3, 3, 3),                activation='sigmoid',                padding='same', data_format='channels_last')) model.compile(loss='binary_crossentropy', optimizer='adadelta') 

the data I feed is in the following format: [1, 10, 64, 64, 1]. So I would like to know where I am wrong and also how to see the output_shape of each layer.

Answer 1

You can get the output shape of a layer by layer.output_shape.

for layer in model.layers:     print(layer.output_shape) 

Gives you:

(None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 40) (None, None, 64, 64, 1) 

Alternatively you can pretty print the model using model.summary:

model.summary() 

Gives you the details about the number of parameters and output shapes of each layer and an overall model structure in a pretty format:

_________________________________________________________________ Layer (type)                 Output Shape              Param #    ================================================================= conv_lst_m2d_1 (ConvLSTM2D)  (None, None, 64, 64, 40)  59200      _________________________________________________________________ batch_normalization_1 (Batch (None, None, 64, 64, 40)  160        _________________________________________________________________ conv_lst_m2d_2 (ConvLSTM2D)  (None, None, 64, 64, 40)  115360     _________________________________________________________________ batch_normalization_2 (Batch (None, None, 64, 64, 40)  160        _________________________________________________________________ conv_lst_m2d_3 (ConvLSTM2D)  (None, None, 64, 64, 40)  115360     _________________________________________________________________ batch_normalization_3 (Batch (None, None, 64, 64, 40)  160        _________________________________________________________________ conv_lst_m2d_4 (ConvLSTM2D)  (None, None, 64, 64, 40)  115360     _________________________________________________________________ batch_normalization_4 (Batch (None, None, 64, 64, 40)  160        _________________________________________________________________ conv3d_1 (Conv3D)            (None, None, 64, 64, 1)   1081       ================================================================= Total params: 407,001 Trainable params: 406,681 Non-trainable params: 320 _________________________________________________________________ 

If you want to access information about a specific layer only, you can use name argument when constructing that layer and then call like this:

... model.add(ConvLSTM2D(..., name='conv3d_0')) ...  model.get_layer('conv3d_0') 

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EDIT: For reference sake it will always be same as layer.output_shape and please don't actually use Lambda or custom layers for this. But you can use Lambda layer to echo the shape of a passing tensor.

... def print_tensor_shape(x):     print(x.shape)     return x model.add(Lambda(print_tensor_shape)) ... 

Or write a custom layer and print the shape of the tensor on call().

class echo_layer(Layer): ...     def call(self, x):         print(x.shape)         return x ...  model.add(echo_layer())