Keras: How to get layer shapes in a Sequential model

If you want the output printed in a fancy way:

model.summary()

If you want the sizes in an accessible form

for layer in model.layers:    print(layer.get_output_at(0).get_shape().as_list())

There are probably better ways to access the shapes than this. Thanks to Daniel for the inspiration.

According to official doc for Keras Layer, one can access layer output/input shape via layer.output_shape or layer.input_shape.

from keras.models import Sequentialfrom keras.layers import Conv2D, MaxPool2Dmodel = Sequential(layers=[    Conv2D(32, (3, 3), input_shape=(64, 64, 3)),    MaxPool2D(pool_size=(3, 3), strides=(2, 2))])for layer in model.layers:    print(layer.output_shape)# Output# (None, 62, 62, 32)# (None, 30, 30, 32)

Just use model.summary(), and it will print all layers with their output shapes.

If you need them as arrays, tuples or etc, you can try:

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

For layers that are used more than once, they contain "multiple inbound nodes", and you should get each output shape separately:

if isinstance(layer.outputs, list):    for out in layer.outputs:        print(K.int_shape(out))        for out in layer.outputs:

It will come as a (None, 62, 62, 32) for the first layer. The None is related to the batch_size, and will be defined during training or predicting.