It takes as input a list of tensors, all of the same shape expect for the concatenation axis, and returns a single tensor, the concatenation of all inputs.
layer_concatenate(
inputs,
axis = -1,
batch_size = NULL,
dtype = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)A list of input tensors (at least 2).
Concatenation axis.
Fixed batch size for layer
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.
A tensor, the concatenation of the inputs alongside axis axis.
Other merge layers:
layer_add(),
layer_average(),
layer_dot(),
layer_maximum(),
layer_minimum(),
layer_multiply(),
layer_subtract()