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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__
__init__(
target_shape,
**kwargs
)