tf.keras.layers.ELU

TensorFlow 1 version

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Flattens the input. Does not affect the batch size.

Inherits From: Layer, Module

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.Flatten

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

Used in the notebooks

Used in the guide	Used in the tutorials
Migrate your TensorFlow 1 code to TensorFlow 2 Introduction to graphs and tf.functions tf.data: Build TensorFlow input pipelines Training Keras models with TensorFlow Cloud Use TPUs	Image classification Simple audio recognition: Recognizing keywords Custom training with tf.distribute.Strategy Distributed training with Keras Multi-worker training with Estimator

Arguments
`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, ...)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last".

Arguments

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, ...). It defaults to the image_data_format value found in your Keras config file at ~/.keras/keras.json. If you never set it, then it will be "channels_last".

Example:

model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(64, 3, 3, input_shape=(3, 32, 32)))
model.output_shape
(None, 1, 10, 64)

model.add(Flatten())
model.output_shape
(None, 640)

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tf.keras / layers / layers.ELU

View aliases

Used in the notebooks

Arguments

Example: