def __init__(self,
input_dim,
output_dim,
weights_initialiser=init.UniformInit(-0.1, 0.1),
biases_initialiser=init.ConstantInit(0.),
weights_penalty=None,
biases_penalty=None):
"""Initialises a parameterised affine layer.
Args:
input_dim (int): Dimension of inputs to the layer.
output_dim (int): Dimension of the layer outputs.
weights_initialiser: Initialiser for the weight parameters.
biases_initialiser: Initialiser for the bias parameters.
weights_penalty: Weights-dependent penalty term (regulariser) or
None if no regularisation is to be applied to the weights.
biases_penalty: Biases-dependent penalty term (regulariser) or
None if no regularisation is to be applied to the biases.
"""
self.input_dim = input_dim
self.output_dim = output_dim
self.weights = weights_initialiser((self.output_dim, self.input_dim))
self.biases = biases_initialiser(self.output_dim)
self.weights_penalty = weights_penalty
self.biases_penalty = biases_penalty
def __init__(self,
num_input_channels,
num_output_channels,
input_dim_1,
input_dim_2,
kernel_dim_1,
kernel_dim_2,
stride=1,
kernels_init=init.UniformInit(-0.01, 0.01),
biases_init=init.ConstantInit(0.),
kernels_penalty=None,
biases_penalty=None):
"""Initialises a parameterised convolutional layer.
Args:
num_input_channels (int): Number of channels in inputs to
layer (this may be number of colour channels in the input
images if used as the first layer in a model, or the
number of output channels, a.k.a. feature maps, from a
a previous convolutional layer).
num_output_channels (int): Number of channels in outputs
from the layer, a.k.a. number of feature maps.
input_dim_1 (int): Size of first input dimension of each 2D
channel of inputs.
input_dim_2 (int): Size of second input dimension of each 2D
channel of inputs.
kernel_dim_x (int): Size of first dimension of each 2D channel of
kernels.
kernel_dim_y (int): Size of second dimension of each 2D channel of
kernels.
kernels_intialiser: Initialiser for the kernel parameters.
biases_initialiser: Initialiser for the bias parameters.
kernels_penalty: Kernel-dependent penalty term (regulariser) or
None if no regularisation is to be applied to the kernels.
biases_penalty: Biases-dependent penalty term (regulariser) or
None if no regularisation is to be applied to the biases.
"""
self.num_input_channels = num_input_channels
self.num_output_channels = num_output_channels
self.input_dim_1 = input_dim_1
self.input_dim_2 = input_dim_2
self.kernel_dim_1 = kernel_dim_1
self.kernel_dim_2 = kernel_dim_2
self.output_dim_1 = self.input_dim_1 - self.kernel_dim_1 + 1
self.output_dim_2 = self.input_dim_2 - self.kernel_dim_2 + 1
self.stride = stride
self.kernels_init = kernels_init
self.biases_init = biases_init
self.kernels_shape = (num_output_channels, num_input_channels,
kernel_dim_1, kernel_dim_2)
self.inputs_shape = (None, num_input_channels, input_dim_1,
input_dim_2)
self.kernels = self.kernels_init(self.kernels_shape)
self.biases = self.biases_init(num_output_channels)
self.kernels_penalty = kernels_penalty
self.biases_penalty = biases_penalty
def __init__(self, input_dim, output_dim,
weights_initialiser=init.UniformInit(-0.1, 0.1),
biases_initialiser=init.ConstantInit(0.)):
"""Initialises a parameterised affine layer.
Args:
input_dim (int): Dimension of inputs to the layer.
output_dim (int): Dimension of the layer outputs.
weights_initialiser: Initialiser for the weight parameters.
biases_initialiser: Initialiser for the bias parameters.
"""
self.input_dim = input_dim
self.output_dim = output_dim
self.weights = weights_initialiser((self.output_dim, self.input_dim))
self.biases = biases_initialiser(self.output_dim)
