TensorFlow 1 version | View source on GitHub |
Layer that reshapes inputs into the given shape.
Inherits From: Layer
tf.keras.layers.Reshape(
target_shape, **kwargs
)
Input shape:
Arbitrary, although all dimensions in the input shape must be known/fixed.
Use the keyword argument input_shape
(tuple of integers, does not include
the samples/batch size 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 = tf.keras.Sequential()
model.add(tf.keras.layers.Reshape((3, 4), input_shape=(12,)))
# model.output_shape == (None, 3, 4), `None` is the batch size.
model.output_shape
(None, 3, 4)
# as intermediate layer in a Sequential model
model.add(tf.keras.layers.Reshape((6, 2)))
model.output_shape
(None, 6, 2)
# also supports shape inference using `-1` as dimension
model.add(tf.keras.layers.Reshape((-1, 2, 2)))
model.output_shape
(None, 3, 2, 2)
Args | |
---|---|
target_shape
|
Target shape. Tuple of integers, does not include the samples dimension (batch size). |
**kwargs
|
Any additional layer keyword arguments. |