NetworkTwoDLayer

class NetworkTwoDLayer.TwoDBaseLayer(n_out, **kwargs)[source]
create_xavier_weights(shape, name)[source]
class NetworkTwoDLayer.OneDToTwoDLayer(**kwargs)[source]
layer_class = '1Dto2D'[source]
recurrent = False[source]
class NetworkTwoDLayer.OneDToTwoDFixedSizeLayer(pad_x=0, pad_y=0, d_row=-1, **kwargs)[source]
layer_class = '1Dto2D_fixed_size'[source]
recurrent = True[source]
class NetworkTwoDLayer.TwoDToOneDLayer(collapse='mean', maxout=False, transpose=False, **kwargs)[source]
layer_class = '2Dto1D'[source]
recurrent = False[source]
class NetworkTwoDLayer.DeepLSTM(n_out, depth, **kwargs)[source]
layer_class = 'deep_lstm'[source]
recurrent = True[source]
create_and_add_2d_lstm_weights(n, m, name_suffix)[source]
create_and_add_bias(n_cells, name_suffix)[source]
class NetworkTwoDLayer.TwoDLSTMLayer(n_out, collapse_output=False, directions=4, projection='average', base=None, **kwargs)[source]
layer_class = 'mdlstm'[source]
recurrent = True[source]
create_and_add_2d_lstm_weights(n, m, name_suffix)[source]
create_and_add_bias(n_cells, name_suffix)[source]
NetworkTwoDLayer.conv_crop_pool_op(X, sizes, output_sizes, W, b, n_in, n_maps, filter_height, filter_width, filter_dilation, poolsize)[source]
class NetworkTwoDLayer.ConvBaseLayer(n_features, filter, base=None, activation='tanh', **kwargs)[source]
layer_class = 'conv_base'[source]
recurrent = False[source]
create_conv_weights(n_features, n_in, filter_height, filter_width, name_suffix='')[source]
create_and_add_bias(n_out, name_suffix='')[source]
conv_output_size_from_input_size(sizes)[source]
NetworkTwoDLayer.maybe_print_pad_warning(_, x)[source]
class NetworkTwoDLayer.ConvPoolLayer2(pool_size, filter_dilation=None, padding=False, **kwargs)[source]
layer_class = 'conv2'[source]
recurrent = True[source]
output_size_from_input_size(sizes)[source]
class NetworkTwoDLayer.ConvFMPLayer(factor=1.4142135623730951, decay=1.0, min_factor=None, padding=False, **kwargs)[source]
layer_class = 'conv_fmp'[source]
recurrent = False[source]