lasagne.layers¶
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 | The Layer class represents a single layer of a neural network. |
MergeLayer | This class represents a layer that aggregates input from multiple layers. |
InputLayer | This layer holds a symbolic variable that represents a network input. |
DenseLayer | A fully connected layer. |
NINLayer | Network-in-network layer. |
Conv1DLayer | 1D convolutional layer |
Conv2DLayer | 2D convolutional layer |
Conv3DLayer | 3D convolutional layer |
TransposedConv2DLayer | 2D transposed convolution layer |
Deconv2DLayer | alias of TransposedConv2DLayer |
DilatedConv2DLayer | 2D dilated convolution layer |
LocallyConnected2DLayer | 2D locally connected layer |
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 |
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. |
DropoutLayer | Dropout layer |
dropout | alias of 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. |
ReshapeLayer | A layer reshaping its input tensor to another tensor of the same total number of elements. |
reshape | alias of ReshapeLayer |
FlattenLayer | A layer that flattens its input. |
flatten | alias of FlattenLayer |
DimshuffleLayer | A layer that rearranges the dimension of its input tensor, maintaining the same same total number of elements. |
dimshuffle | alias of 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 PadLayer |
SliceLayer | Slices the input at a specific axis and at specific indices. |
ConcatLayer | Concatenates multiple inputs along the specified axis. |
concat | alias of ConcatLayer |
ElemwiseMergeLayer | This layer performs an elementwise merge of its input layers. |
ElemwiseSumLayer | This layer performs an elementwise sum of its input 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. |
EmbeddingLayer | A layer for word embeddings. |
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 [R6ee2c8fdcfdf-1]. |
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. |
corrmm.Conv2DMMLayer | 2D convolutional layer |
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 |
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. |