In Keras, we can define the network as follows. Are there any way to output the shape after each layer. For instance, I want to print out the shape of inputs after the line defining inputs, then print out the shape of conv1 after the line defining conv1, etc.
inputs = Input((1, img_rows, img_cols))
conv1 = Convolution2D(64, 3, 3, activation='relu', init='lecun_uniform', W_constraint=maxnorm(3), border_mode='same')(inputs)
conv1 = Convolution2D(64, 3, 3, activation='relu', init='lecun_uniform', W_constraint=maxnorm(3), border_mode='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Convolution2D(128, 3, 3, activation='relu', init='lecun_uniform', W_constraint=maxnorm(3), border_mode='same')(pool1)
conv2 = Convolution2D(128, 3, 3, activation='relu', init='lecun_uniform', W_constraint=maxnorm(3), border_mode='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
If a layer has a single node (i.e. if it isn't a shared layer), you can get its input tensor, output tensor, input shape and output shape via: layer.input_shape
from keras.utils.layer_utils import layer_from_config
config = layer.get_config()
layer = layer_from_config(config)
Source: https://keras.io/layers/about-keras-layers/
May be this the easiest way to do:
model.layers[layer_of_interest_index].output_shape