Layers

struct Conv2d{T<:Union{Real, JuMP.VariableRef, JuMP.AffExpr}, U<:Union{Real, JuMP.VariableRef, JuMP.AffExpr}, V<:Integer} <: Layer

Represents 2-D convolution operation.

p(x) is shorthand for conv2d(x, p) when p is an instance of Conv2d.

Fields:

• filter

• bias

• stride

• padding

Conv2d(filter)

Convenience function to create a Conv2d struct with the specified filter and zero bias.

struct Flatten{T<:Integer} <: Layer

Represents a flattening operation.

p(x) is shorthand for permute_and_flatten(x, p.perm) when p is an instance of Flatten.

Fields:

• n_dim

• perm

struct Linear{T<:Real, U<:Real} <: Layer

Represents matrix multiplication.

p(x) is shorthand for matmul(x, p) when p is an instance of Linear.

Fields:

• matrix

• bias

struct MaskedReLU{T<:Real} <: Layer

Represents a masked ReLU activation, with mask controlling how the ReLU is applied to each output.

p(x) is shorthand for masked_relu(x, p.mask) when p is an instance of MaskedReLU.

Fields:

• mask

• tightening_algorithm

struct Normalize <: Layer

Represents a Normalization operation.

MaxPool(strides)

Convenience function to create a Pool struct for max-pooling.

struct Pool{N} <: Layer

Represents a pooling operation.

p(x) is shorthand for pool(x, p) when p is an instance of Pool.

Fields:

• strides

• pooling_function

struct ReLU <: Layer

Represents a ReLU operation.

p(x) is shorthand for relu(x) when p is an instance of ReLU.

struct SkipBlock <: Layer

TODO (vtjeng)

Fields:

• layers

struct Zero <: Layer

Always outputs exactly zero.

conv2d(input, params)

Computes the result of convolving input with the filter and bias stored in params.

Mirrors tf.nn.conv2d from the tensorflow package, with strides = [1, params.stride, params.stride, 1].

Supports three types of padding:

• 'same': Specify via SamePadding(). Padding is added so that the output has the same size as the input.
• 'valid': Specify via FixedPadding(). No padding is added.
• 'fixed': Specify via:
  • A single integer, interpreted as padding for both axes
  • A tuple of two integers, interpreted as (ypadding, xpadding)
  • A tuple of four integers, interpreted as (top, bottom, left, right)

Throws

• AssertionError if input and filter are not compatible.

Permute dimensions of array in specified order, then flattens the array.

matmul(x, params)

Computes the result of pre-multiplying x by the transpose of params.matrix and adding params.bias.

matmul(x, params)

Computes the result of pre-multiplying x by the transpose of params.matrix and adding params.bias. We write the computation out by hand when working with JuMPLinearType so that we are able to simplify the output as the computation is carried out.

getoutputsize(input_array, strides)

For pooling operations on an array, returns the expected size of the output array.

getpoolview(input_array, strides, output_index)

For pooling operations on an array, returns a view of the parent array corresponding to the output_index in the output array.

getsliceindex(input_array_size, stride, output_index)

For pooling operations on an array where a given element in the output array corresponds to equal-sized blocks in the input array, returns (for a given dimension) the index range in the input array corresponding to a particular index output_index in the output array.

Returns an empty array if the output_index does not correspond to any input indices.

Arguments

• stride::Integer: the size of the operating blocks along the active dimension.

pool(input, params)

Computes the result of applying the pooling function params.pooling_function to non-overlapping cells of input with sizes specified in params.strides.

poolmap(f, input_array, strides)

Returns output from applying f to subarrays of input_array, with the windows determined by the strides.