tf.keras.layers.ReLU

TensorFlow 1 version

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Layer that reshapes inputs into the given shape.

Inherits From: Layer, Module

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.Reshape

tf.keras.layers.Reshape(
    target_shape, **kwargs
)

Used in the notebooks

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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.

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tf.keras / layers / layers.ReLU

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Used in the notebooks

Input shape:

Output shape:

Example:

Args