I have implemented a custom Layer
in tf.keras
, using TensorFlow 2.1.0.
In the past, when using the stand-alone Keras, it was important to define the compute_output_shape(input_shape)
method in any custom layer so that the computational graph could be created.
Now, having moved to TF2, I found out that even if I remove that method from my custom implementation the layer still works as expected. Apparently, it works both in eager and graph mode. This is an example of what I mean:
from tensorflow.keras.layers import Layer, Input
from tensorflow.keras.models import Sequential
import numpy as np
class MyLayer(Layer):
def call(self, inputs):
return inputs[:, :-1] # Do something that changes the shape
m = Sequential([MyLayer(), MyLayer()])
m.predict(np.ones((10, 3))) # This would not have worked in the past
Is it safe to say that compute_output_shape()
is not necessary anymore? Am I missing something important?
In the documentation there's no explicit mention of removing compute_output_shape()
, although none of the examples implements it explicitly.
Thanks
Defining models and layers in TensorFlow. Most models are made of layers. Layers are functions with a known mathematical structure that can be reused and have trainable variables. In TensorFlow, most high-level implementations of layers and models, such as Keras or Sonnet, are built on the same foundational class: tf.
It is not mentioned in the Tensorflow Documentation but in Chapter 12, Custom Models and Training with TensorFlow of the book, Hands-on Machine Learning using Scikit-Learn and Tensorflow (2nd Edition Updated for Tensorflow 2) of O'REILLY Publications, written by Aurelien Geron, it is mentioned as shown in the screenshot below:
To answer your question, yes, it is safe to say compute_output_shape
is not needed unless the Layer is Dynamic.
This is evident from this Tensorflow Tutorial on Custom Layer where compute_output_shape
is not used.
Hope this helps. Happy Learning!
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