tf.compat.v1.layers.flatten

Flattens an input tensor while preserving the batch axis (axis 0).

tf.compat.v1.layers.flatten(
    inputs, name=None, data_format='channels_last'
)

Migrate to TF2

Caution: This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the TensorFlow v1 to TensorFlow v2 migration guide for instructions on how to migrate the rest of your code.

This API is a legacy api that is only compatible with eager execution and tf.function if you combine it with tf.compat.v1.keras.utils.track_tf1_style_variables

Please refer to tf.layers model mapping section of the migration guide to learn how to use your TensorFlow v1 model in TF2 with Keras.

The corresponding TensorFlow v2 layer is tf.keras.layers.Flatten.

Structural Mapping to Native TF2

None of the supported arguments have changed name.

Before:

 y = tf.compat.v1.layers.flatten(x)

After:

To migrate code using TF1 functional layers use the Keras Functional API:

 x = tf.keras.Input((28, 28, 1))
 y = tf.keras.layers.Flatten()(x)
 model = tf.keras.Model(x, y)

Description

Args
inputs	Tensor input.
name	The name of the layer (string).
data_format	A string, one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch, height, width, channels) while channels_first corresponds to inputs with shape (batch, channels, height, width).

Returns
Reshaped tensor.

Examples:

  x = tf.compat.v1.placeholder(shape=(None, 4, 4), dtype='float32')
  y = flatten(x)
  # now `y` has shape `(None, 16)`

  x = tf.compat.v1.placeholder(shape=(None, 3, None), dtype='float32')
  y = flatten(x)
  # now `y` has shape `(None, None)`