What is the difference between tf.keras.layers.Input() and tf.keras.layers.Flatten()

All we need is an easy explanation of the problem, so here it is.

I have seen multiple uses of both tf.keras.layers.Flatten() (ex. here) and tf.keras.layers.Input() (ex. here). After reading the documentation, it is not clear to me

1. whether either of them uses the other
2. whether both can be used interchangeably when introducing to a model an input layer (let’s say with dimensions (64, 64))

How to solve :

I know you bored from this bug, So we are here to help you! Take a deep breath and look at the explanation of your problem. We have many solutions to this problem, But we recommend you to use the first method because it is tested & true method that will 100% work for you.

Method 1

I think the confusion comes from using a tf.keras.Sequential model, which does not need an explicit Input layer. Consider the following two models, which are equivalent:

import tensorflow as tf

model1 = tf.keras.Sequential([
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(5, activation='relu'),
    ])

model1.build((1, 28, 28, 1))

model2 = tf.keras.Sequential([
      tf.keras.layers.Input((28, 28, 1)),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(5, activation='relu'),
    ])

The difference is that I explicitly set the input shape of model2 using an Input layer. In model1, the input shape will be inferred when you pass real data to it or call model.build.

Now regarding the Flatten layer, this layer simply converts a n-dimensional tensor (for example (28, 28, 1)) into a 1D tensor (28 x 28 x 1). The Flatten layer and Input layer can coexist in a Sequential model but do not depend on each other.

Note: Use and implement method 1 because this method fully tested our system.
Thank you 🙂

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