Use keras layer in tensorflow code

Question

Lets say I have a simple neural network with an input layer and a single convolution layer programmed in tensorflow:

  # Input Layer
  input_layer = tf.reshape(f         


        

Answer 1

Yes, this is possible.

Import both TensorFlow and Keras and link your Keras session to the TF one:

import tensorflow as tf
import keras
from keras import backend as K

tf_sess = tf.Session()
K.set_session(tf_sess)

Now, in your model definition, you can mix TF and Keras layers like so:

# Input Layer
input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])

# Convolutional Layer #1
conv1 = tf.layers.conv2d(
    inputs=input_layer,
    filters=32,
    kernel_size=[5, 5],
    padding="same",
    activation=tf.nn.relu)

# Flatten conv output
flat = tf.contrib.layers.flatten(conv1)

# Fully-connected Keras layer
layer2_dense = keras.layers.Dense(128, activation='relu')(flat)

# Fully-connected TF layer (output)
output_preds = tf.layers.dense(layer2_dense, units=10)

This answer is adopted from a Keras blog post by Francois Chollet.