Why Does the Keras Dense Layer Input Reshape Unexpectedly?

Unexpected Reshaping in Keras Dense Layer Input: Unraveling the Mystery

In Keras, the Dense layer is a commonly used building block for neural networks. However, users may encounter an unexpected behavior where the input is not flattened prior to applying the layer's operations.

In the provided code snippet:

input1 = layers.Input((2,3))
output = layers.Dense(4)(input1)

Instead of flattening the input tensor input1 with dimensions (2,3), we surprisingly observe an output tensor output with dimensions (?, 2, 4). This contradicts the documentation's claim that input with rank greater than 2 should be flattened.

Examining the current Keras implementation, however, reveals a different behavior: the Dense layer is actually applied to the last axis of the input tensor. This means that in the given example, each 2D row of input1 is independently passed through the densely connected layer. Consequently, the output retains the first dimension and adds the specified number of units (4) to the last dimension.

This departure from the documentation has significant implications:

• The equivalent operations of TimeDistributed(Dense(...)) and Dense(...) over multidimensional inputs.
• Shared weight matrices across units in the Dense layer.

Example:

model = Sequential()
model.add(Dense(10, input_shape=(20, 5)))

model.summary()

The resulting model summary shows only 60 trainable parameters, despite the densely connected layer having 10 units. This is because each unit connects to the 5 elements of each row with identical weights.

Visual Illustration:

[Image: Visual illustration of applying a Dense layer on an input with two or more dimensions in Keras]

In conclusion, the Dense layer in Keras applies independently to the last axis of the input tensor, leading to unflattened output in certain scenarios. This behavior has implications for model design and parameter sharing.

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