tf.keras.layers.Flatten

Flattens the input. Does not affect the batch size.

Inherits From: Layer, Operation

tf.keras.layers.Flatten(
    data_format=None, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Effective Tensorflow 2 Migrate early stopping Use TF1.x models in TF2 workflows tf.data: Build TensorFlow input pipelines Migrate checkpoint saving	Image classification Scalable model compression Simple audio recognition: Recognizing keywords Custom training with tf.distribute.Strategy Using DTensors with Keras

Note: If inputs are shaped (batch,) without a feature axis, then flattening adds an extra channel dimension and output shape is (batch, 1).

Args

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, ..., channels) while "channels_first" corresponds to inputs with shape (batch, channels, ...). When unspecified, uses image_data_format value found in your Keras config file at ~/.keras/keras.json (if exists). Defaults to "channels_last".

Example:

x = keras.Input(shape=(10, 64))
y = keras.layers.Flatten()(x)
y.shape
(None, 640)

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates a layer from its config.

This method is the reverse of get_config, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by set_weights).

Args
config	A Python dictionary, typically the output of get_config.

Returns
A layer instance.

symbolic_call

View source

symbolic_call(
    *args, **kwargs
)