Nodes: artificial neural network

Artificial neural network (ANN) nodes

GeneralConv(out_channels, kernel_size[, ...])	Applies a convolution to the inputs.
Conv1D(out_channels, kernel_size, **kwargs)
Conv2D(out_channels, kernel_size, **kwargs)
Conv3D(out_channels, kernel_size, **kwargs)
Dropout(prob[, seed])	A layer that stochastically ignores a subset of inputs each training step.
VanillaRNN(num_unit[, state_initializer, ...])	Basic fully-connected RNN core.
GRU(num_unit[, wi_initializer, ...])	Gated Recurrent Unit.
LSTM(num_unit[, wi_initializer, ...])	Long short-term memory (LSTM) RNN core.
Pool(init_v, reduce_fn, window_shape, ...)
MaxPool(window_shape[, strides, padding])	Pools the input by taking the maximum over a window.
AvgPool(window_shape[, strides, padding])	Pools the input by taking the average over a window.
MinPool(window_shape[, strides, padding])	Pools the input by taking the minimum over a window.
BatchNorm(axis[, epsilon, use_bias, ...])	Batch Normalization node.
BatchNorm1d([axis])	1-D batch normalization.
BatchNorm2d([axis])	2-D batch normalization.
BatchNorm3d([axis])	3-D batch normalization.
GroupNorm([num_groups, group_size, epsilon, ...])	Group normalization layer.
LayerNorm([epsilon, use_bias, use_scale, ...])	Layer normalization (https://arxiv.org/abs/1607.06450).
InstanceNorm([epsilon, use_bias, use_scale, ...])	Instance normalization layer.