Layer factory function to create a 1D convolution layer with optional non-linearity. Same as Convolution() except that filter_shape is verified to be 1-dimensional. See Convolution() for extensive documentation.

Convolution1D(filter_shape, num_filters = NULL,
  activation = activation_identity, init = init_glorot_uniform(),
  pad = FALSE, strides = 1, bias = TRUE, init_bias = 0,
  reduction_rank = 1, name = "")

Arguments

filter_shape

int or list of int - shape (spatial extent) of the receptive field, not including the input feature-map depth. E.g. (3,3) for a 2D convolution.

num_filters

(int, defaults to None) – number of filters (output feature-map depth), or () to denote scalar output items (output shape will have no depth axis).

activation

(Function) - optional activation Function

init

(scalar or matrix or initializer, defaults to init_glorot_uniform()) – initial value of weights W

pad

(bool or list of bools) – if False, then the operation will be shifted over the “valid” area of input, that is, no value outside the area is used. If pad=True on the other hand, the operation will be applied to all input positions, and positions outside the valid region will be considered containing zero. Use a list to specify a per-axis value.

strides

(int or tuple of ints, defaults to 1) – stride of the operation. Use a list of ints to specify a per-axis value.

bias

(bool) – whether to include bias

init_bias

(scalar or matrix or initializer, defaults to 0) – initial value of weights b

name

string (optional) the name of the Function instance in the network