Re-write TensorFlow into Keras with tf.keras

Question

I wan to re-write TensorFlow code into Keras. I just wonder if you can use for this purpose the tf.keras.layers to just replace the tf.layers ?

Like

tf.layers.max_pooling2d() 

to:

tf.keras.layers.max_pooling2d() 

Can I re-write TensorFlow to Keras in this way?

Does this define a proper Keras model where you can use the model.fit method?

Answer 1

First of all, I think you meant tf.keras.layers.MaxPool2D , which is a class, not a function. If I got your point, it shouldn't be an issue. There are some minor difference in syntax, but nothing serious. Besides, tf.keras.layers is a direct substitute for tf.layers . As per official docs, tf.layers are wrappers around tf.keras.layers. For example , convolutional layers in Layers API inherit from tf.keras.layers .

@tf_export('layers.Conv1D')
class Conv1D(keras_layers.Conv1D, base.Layer):
  """1D convolution layer (e.g. temporal convolution). 

Even more so, Layers API is deprecated and will be removed from TF 2.0.