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Layer that reshapes inputs into the given shape.
tf.keras.layers.Reshape( target_shape, **kwargs )
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.
(batch_size,) + target_shape
# 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. |
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Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/layers/Reshape