Flatten a given input, does not affect the batch size.

layer_flatten(
object,
data_format = NULL,
input_shape = NULL,
dtype = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)

## Arguments

object

What to compose the new Layer instance with. Typically a Sequential model or a Tensor (e.g., as returned by layer_input()). The return value depends on object. If object is:

• missing or NULL, the Layer instance is returned.

• a Sequential model, the model with an additional layer is returned.

• a Tensor, the output tensor from layer_instance(object) is returned.

data_format

A string. one of channels_last (default) or channels_first. The ordering of the dimensions in the inputs. The purpose of this argument is to preserve weight ordering when switching a model from one data format to another. 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".

input_shape

Input shape (list of integers, does not include the samples axis) which is required when using this layer as the first layer in a model.

dtype

The data type expected by the input, as a string (float32, float64, int32...)

name

An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.

trainable

Whether the layer weights will be updated during training.

weights

Initial weights for layer.

Other core layers: layer_activation(), layer_activity_regularization(), layer_attention(), layer_dense_features(), layer_dense(), layer_dropout(), layer_input(), layer_lambda(), layer_masking(), layer_permute(), layer_repeat_vector(), layer_reshape()