lasagne.layers

Helper functions

get_output

Computes the output of the network at one or more given layers.

get_output_shape

Computes the output shape of the network at one or more given layers.

get_all_layers

This function gathers all layers below one or more given Layer instances, including the given layer(s).

get_all_params

Returns a list of Theano shared variables or expressions that parameterize the layer.

count_params

This function counts all parameters (i.e., the number of scalar values) of all layers below one or more given Layer instances, including the layer(s) itself.

get_all_param_values

This function returns the values of the parameters of all layers below one or more given Layer instances, including the layer(s) itself.

set_all_param_values

Given a list of numpy arrays, this function sets the parameters of all layers below one or more given Layer instances (including the layer(s) itself) to the given values.

Layer base classes

Layer

The Layer class represents a single layer of a neural network.

MergeLayer

This class represents a layer that aggregates input from multiple layers.

Network input

InputLayer

This layer holds a symbolic variable that represents a network input.

Dense layers

DenseLayer

A fully connected layer.

NINLayer

Network-in-network layer.

Convolutional layers

Conv1DLayer

1D convolutional layer

Conv2DLayer

2D convolutional layer

Conv3DLayer

3D convolutional layer

TransposedConv2DLayer

2D transposed convolution layer

Deconv2DLayer

alias of lasagne.layers.conv.TransposedConv2DLayer

DilatedConv2DLayer

2D dilated convolution layer

Local layers

LocallyConnected2DLayer

2D locally connected layer

Pooling layers

MaxPool1DLayer

1D max-pooling layer

MaxPool2DLayer

2D max-pooling layer

MaxPool3DLayer

3D max-pooling layer

Pool1DLayer

1D pooling layer

Pool2DLayer

2D pooling layer

Pool3DLayer

3D pooling layer

Upscale1DLayer

1D upscaling layer

Upscale2DLayer

2D upscaling layer

Upscale3DLayer

3D upscaling layer

GlobalPoolLayer

Global pooling layer

FeaturePoolLayer

Feature pooling layer

FeatureWTALayer

‘Winner Take All’ layer

SpatialPyramidPoolingLayer

Spatial Pyramid Pooling Layer

Recurrent layers

CustomRecurrentLayer

A layer which implements a recurrent connection.

RecurrentLayer

Dense recurrent neural network (RNN) layer

LSTMLayer

A long short-term memory (LSTM) layer.

GRULayer

Gated Recurrent Unit (GRU) Layer

Gate

Simple class to hold the parameters for a gate connection.

Noise layers

DropoutLayer

Dropout layer

dropout

alias of lasagne.layers.noise.DropoutLayer

dropout_channels

Convenience function to drop full channels of feature maps.

spatial_dropout

Convenience function to drop full channels of feature maps.

dropout_locations

Convenience function to drop full locations of feature maps.

GaussianNoiseLayer

Gaussian noise layer.

Shape layers

ReshapeLayer

A layer reshaping its input tensor to another tensor of the same total number of elements.

reshape

alias of lasagne.layers.shape.ReshapeLayer

FlattenLayer

A layer that flattens its input.

flatten

alias of lasagne.layers.shape.FlattenLayer

DimshuffleLayer

A layer that rearranges the dimension of its input tensor, maintaining the same same total number of elements.

dimshuffle

alias of lasagne.layers.shape.DimshuffleLayer

PadLayer

Pad all dimensions except the first batch_ndim with width zeros on both sides, or with another value specified in val.

pad

alias of lasagne.layers.shape.PadLayer

SliceLayer

Slices the input at a specific axis and at specific indices.

Merge layers

ConcatLayer

Concatenates multiple inputs along the specified axis.

concat

alias of lasagne.layers.merge.ConcatLayer

ElemwiseMergeLayer

This layer performs an elementwise merge of its input layers.

ElemwiseSumLayer

This layer performs an elementwise sum of its input layers.

Normalization layers

LocalResponseNormalization2DLayer

Cross-channel Local Response Normalization for 2D feature maps.

BatchNormLayer

Batch Normalization

batch_norm

Apply batch normalization to an existing layer.

StandardizationLayer

Standardize inputs to zero mean and unit variance:

instance_norm

Apply instance normalization to an existing layer.

layer_norm

Apply layer normalization to an existing layer.

Embedding layers

EmbeddingLayer

A layer for word embeddings.

Special-purpose layers

NonlinearityLayer

A layer that just applies a nonlinearity.

BiasLayer

A layer that just adds a (trainable) bias term.

ScaleLayer

A layer that scales its inputs by learned coefficients.

standardize

Convenience function for standardizing inputs by applying a fixed offset and scale.

ExpressionLayer

This layer provides boilerplate for a custom layer that applies a simple transformation to the input.

InverseLayer

The InverseLayer class performs inverse operations for a single layer of a neural network by applying the partial derivative of the layer to be inverted with respect to its input: transposed layer for a DenseLayer, deconvolutional layer for Conv2DLayer, Conv1DLayer; or an unpooling layer for MaxPool2DLayer.

TransformerLayer

Spatial transformer layer

TPSTransformerLayer

Spatial transformer layer

ParametricRectifierLayer

A layer that applies parametric rectify nonlinearity to its input following [R33].

prelu

Convenience function to apply parametric rectify to a given layer’s output.

RandomizedRectifierLayer

A layer that applies a randomized leaky rectify nonlinearity to its input.

rrelu

Convenience function to apply randomized rectify to a given layer’s output.

lasagne.layers.corrmm

corrmm.Conv2DMMLayer

2D convolutional layer

lasagne.layers.cuda_convnet

cuda_convnet.Conv2DCCLayer

cuda_convnet.MaxPool2DCCLayer

cuda_convnet.ShuffleBC01ToC01BLayer

cuda_convnet.bc01_to_c01b

cuda_convnet.ShuffleC01BToBC01Layer

cuda_convnet.c01b_to_bc01

cuda_convnet.NINLayer_c01b

lasagne.layers.dnn

dnn.Conv2DDNNLayer

2D convolutional layer

dnn.Conv3DDNNLayer

3D convolutional layer

dnn.MaxPool2DDNNLayer

2D max-pooling layer

dnn.Pool2DDNNLayer

2D pooling layer

dnn.MaxPool3DDNNLayer

3D max-pooling layer

dnn.Pool3DDNNLayer

3D pooling layer

dnn.SpatialPyramidPoolingDNNLayer

Spatial Pyramid Pooling Layer

dnn.BatchNormDNNLayer

Batch Normalization

dnn.batch_norm_dnn

Apply cuDNN batch normalization to an existing layer.