tf.keras.layers.Reshape

TensorFlow 2 version View source on GitHub

Class Reshape

Reshapes an output to a certain shape.

Inherits From: Layer

Aliases:

Arguments:

  • target_shape: Target shape. Tuple of integers, does not include the samples dimension (batch size).

Input shape:

Arbitrary, although all dimensions in the input shaped must be fixed. Use the keyword argument input_shape (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model.

Output shape:

(batch_size,) + target_shape

Example:

# as first layer in a Sequential model
model = Sequential()
model.add(Reshape((3, 4), input_shape=(12,)))
# now: model.output_shape == (None, 3, 4)
# note: `None` is the batch dimension

# as intermediate layer in a Sequential model
model.add(Reshape((6, 2)))
# now: model.output_shape == (None, 6, 2)

# also supports shape inference using `-1` as dimension
model.add(Reshape((-1, 2, 2)))
# now: model.output_shape == (None, None, 2, 2)

__init__

View source

__init__(
    target_shape,
    **kwargs
)