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))

CodePudding user response：

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.