It takes as input a list of tensors of size 2, both of the same shape, and returns a single tensor, (`inputs[[1]] - inputs[[2]]``), also of the same shape.
[[1]: R:[1 [[2]: R:[2
layer_subtract(inputs, batch_size = NULL, dtype = NULL, name = NULL,
trainable = NULL, weights = NULL)A list of input tensors (exactly 2).
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 difference of the inputs.
Other merge layers: layer_add,
layer_average,
layer_concatenate, layer_dot,
layer_maximum, layer_multiply