nn.models #

fn sequential[T]() &Sequential[T]

sequential creates a new sequential network with a new context.

fn sequential_from_ctx[T](ctx &autograd.Context[T]) &Sequential[T]

sequential_from_ctx creates a new sequential network with the given context.

fn sequential_from_ctx_with_layers[T](ctx &autograd.Context[T], given_layers []types.Layer[T]) &Sequential[T]

sequential_from_ctx_with_layers creates a new sequential network with the given context and the given layers.

fn sequential_info[T](ctx &autograd.Context[T], layers_ []types.Layer[T]) &SequentialInfo[T]

sequential_info creates a new neural network container with an empty list of layers.

fn sequential_with_layers[T](given_layers []types.Layer[T]) &Sequential[T]

sequential_with_layers creates a new sequential network with a new context and the given layers.

fn (mut ls SequentialInfo[T]) input(shape []int)

input adds a new input layer to the network with the given shape.

fn (mut ls SequentialInfo[T]) linear(output_size int)

linear adds a new linear layer to the network with the given output size

fn (mut ls SequentialInfo[T]) maxpool2d(kernel []int, padding []int, stride []int)

maxpool2d adds a new maxpool2d layer to the network with the given kernel size and stride.

fn (mut ls SequentialInfo[T]) mse_loss()

mse_loss sets the loss function to the mean squared error loss.

fn (mut ls SequentialInfo[T]) sigmoid_cross_entropy_loss()

sigmoid_cross_entropy_loss sets the loss function to the sigmoid cross entropy loss.

fn (mut ls SequentialInfo[T]) softmax_cross_entropy_loss()

softmax_cross_entropy_loss sets the loss function to the softmax cross entropy loss.

fn (mut ls SequentialInfo[T]) flatten()

flatten adds a new flatten layer to the network.

fn (mut ls SequentialInfo[T]) relu()

relu adds a new relu layer to the network.

fn (mut ls SequentialInfo[T]) leaky_relu()

leaky_relu adds a new leaky_relu layer to the network.

fn (mut ls SequentialInfo[T]) elu()

elu adds a new elu layer to the network.

fn (mut ls SequentialInfo[T]) sigmod()

sigmod adds a new sigmod layer to the network.

fn (mut nn Sequential[T]) input(shape []int)

input adds a new input layer to the network with the given shape.

fn (mut nn Sequential[T]) linear(output_size int)

linear adds a new linear layer to the network with the given output size

fn (mut nn Sequential[T]) maxpool2d(kernel []int, padding []int, stride []int)

maxpool2d adds a new maxpool2d layer to the network with the given kernel size and stride.

fn (mut nn Sequential[T]) mse_loss()

mse_loss sets the loss function to the mean squared error loss.

fn (mut nn Sequential[T]) sigmoid_cross_entropy_loss()

sigmoid_cross_entropy_loss sets the loss function to the sigmoid cross entropy loss.

fn (mut nn Sequential[T]) softmax_cross_entropy_loss()

softmax_cross_entropy_loss sets the loss function to the softmax cross entropy loss.

fn (mut nn Sequential[T]) flatten()

flatten adds a new flatten layer to the network.

fn (mut nn Sequential[T]) relu()

relu adds a new relu layer to the network.

fn (mut nn Sequential[T]) leaky_relu()

leaky_relu adds a new leaky_relu layer to the network.

fn (mut nn Sequential[T]) elu()

elu adds a new elu layer to the network.

fn (mut nn Sequential[T]) sigmod()

sigmod adds a new sigmod layer to the network.

fn (mut nn Sequential[T]) forward(mut train autograd.Variable[T]) !&autograd.Variable[T]

fn (mut nn Sequential[T]) loss(output &autograd.Variable[T], target &vtl.Tensor[T]) !&autograd.Variable[T]

struct Sequential[T] {
pub mut:
	info &SequentialInfo[T] = unsafe { nil }
}

struct SequentialInfo[T] {
	ctx &autograd.Context[T] = unsafe { nil }
pub mut:
	layers []types.Layer[T]
	loss   types.Loss
}