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
)
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.
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 (list of integers, does not include the samples axis) which is required when using this layer as the first layer in a model.
The data type expected by the input, as a string (float32
,
float64
, int32
...)
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.
Whether the layer weights will be updated during training.
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()