Can i get the all output keras layers

Question

I just started with deep learning and i want to get the input/output of each layer in real-time. I am using google colab with tensorflow 2 and python 3. I tried to get the layers like this but for some reason that i don't understand is not working. Any help will be appreciated.

# Here are imports 

from __future__ import absolute_import, division, print_function, unicode_literals

try:
  # %tensorflow_version only exists in Colab.
  %tensorflow_version 2.x
except Exception:
  pass
import tensorflow as tf

from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt


from tensorflow.keras import backend as K



# I am using CIFAR10 dataset

(train_images, train_labels), (test_images, test_labels) = 
datasets.cifar10.load_data()

Normalize pixel values to be between 0 and 1
train_images, test_images = train_images / 255.0, test_images / 255.0

# Here is the model

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))

# Compilation of the model 

model.compile(optimizer='adam',
          loss='sparse_categorical_crossentropy',
          metrics=['accuracy'])

history = model.fit(train_images, train_labels, epochs=10, 
                validation_data=(test_images, test_labels))
# Based on 
https://stackoverflow.com/questions/41711190/keras-how-to-get-the-output-of-each-layer

# I tried this 

tf.compat.v1.disable_eager_execution()
inp = model.input                                    # input placeholder
outputs = [layer.output for layer in model.layers]     # all layer outputs
functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs]    # evaluation functions

Testing
test = np.random.random(input_shape)[np.newaxis,...]
layer_outs = [func([test, 1.]) for func in functors]
print(layer_outs)


#The error appear at line 
functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs] 


#I got this error message
Tensor Tensor("conv2d/Identity:0", shape=(None, 30, 30, 32), dtype=float32) is not an element of this graph.

Answer 1

This error basically tells you that you want to change the graph after compiling it. When you call compile, TF will statically define all operations. You have to move the code snippet where you define functors above the compile method. Just swap the last lines with these ones:

tf.compat.v1.disable_eager_execution()
inp = model.input                                    # input placeholder
outputs = [layer.output for layer in model.layers]     # all layer outputs
functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs]    # evaluation functions



model.compile(optimizer='adam',
          loss='sparse_categorical_crossentropy',
          metrics=['accuracy'])

history = model.fit(train_images, train_labels, epochs=1, 
                validation_data=(test_images, test_labels))

#Testing
input_shape = [1] + list(model.input_shape[1:])
test = np.random.random(input_shape)
layer_outs = [func([test, 1.]) for func in functors]
print(layer_outs)