l0_45 = layers.Input2DLayer(BATCH_SIZE, NUM_INPUT_FEATURES, input_sizes[1][0],
input_sizes[1][1])
l0r = layers.MultiRotSliceLayer([l0, l0_45], part_size=45, include_flip=True)
l0s = cc_layers.ShuffleBC01ToC01BLayer(l0r)
l1a = cc_layers.CudaConvnetConv2DLayer(l0s,
n_filters=32,
filter_size=6,
weights_std=0.01,
init_bias_value=0.1,
dropout=0.0,
partial_sum=1,
untie_biases=True)
l1 = cc_layers.CudaConvnetPooling2DLayer(l1a, pool_size=2)
l2a = cc_layers.CudaConvnetConv2DLayer(l1,
n_filters=64,
filter_size=5,
weights_std=0.01,
init_bias_value=0.1,
dropout=0.0,
partial_sum=1,
untie_biases=True)
l2 = cc_layers.CudaConvnetPooling2DLayer(l2a, pool_size=2)
l3a = cc_layers.CudaConvnetConv2DLayer(l2,
n_filters=128,
filter_size=3,
weights_std=0.01,
#l0r = layers.MultiRotSliceLayer([l0, l0_45], part_size=45, include_flip=True)
l0r = layers.MultiRotSliceLayer([l0, l0_45], part_size=45, include_flip=False)
l0s = cc_layers.ShuffleBC01ToC01BLayer(l0r)
l1a = cc_layers.CudaConvnetConv2DLayer(l0s,
n_filters=32,
filter_size=6,
weights_std=0.01,
init_bias_value=0.1,
dropout=0.0,
partial_sum=1,
untie_biases=True)
#l1 = cc_layers.CudaConvnetPooling2DLayer(l1a, pool_size=2)
l12 = cc_layers.CudaConvnetPooling2DLayer(l1a, pool_size=4)
l3a = cc_layers.CudaConvnetConv2DLayer(l12,
n_filters=64,
filter_size=7,
pad=0,
weights_std=0.1,
init_bias_value=0.1,
dropout=0.0,
partial_sum=1,
untie_biases=True)
l3 = cc_layers.CudaConvnetPooling2DLayer(l3a, pool_size=2)
#l2a = cc_layers.CudaConvnetConv2DLayer(l1, n_filters=64, filter_size=5, weights_std=0.01, init_bias_value=0.1, dropout=0.0, partial_sum=1, untie_biases=True)
#l2 = cc_layers.CudaConvnetPooling2DLayer(l2a, pool_size=2)
