def build_dcnn(input_var, batch_size, sentence_len, vocab_size, word_dimension, word_embedding,
non_static, n_out, arch_params):
windows = arch_params['windows']
n_filters = arch_params['n_filters']
k = arch_params['k']
activations = arch_params['activations']
k_top = arch_params['k_top']
dropout = arch_params['dropout']
# sentence_len может быть None
l_in = lasagne.layers.InputLayer(
shape=(batch_size, sentence_len),
input_var=input_var
)
l_embedding = CNN.embeddings.SentenceEmbeddingLayer(
l_in,
vocab_size=vocab_size,
word_dimension=word_dimension,
word_embedding=word_embedding,
non_static=non_static
)
assert len(windows) == 2
assert isinstance(windows[0], tuple)
assert isinstance(windows[1], tuple)
l_conv1 = CNN.Conv1DLayerSplitted(
l_embedding,
n_filters[0],
filter_hight=windows[0],
nonlinearity=lasagne.nonlinearities.linear
)
l_fold1 = CNN.folding.FoldingLayer(l_conv1)
# TODO: Заменить на dynamic k-max-pooling
l_pool1= CNN.pooling.KMaxPoolLayer(l_fold1, k=k)
l_nonlinear1 = lasagne.layers.NonlinearityLayer(l_pool1, nonlinearity=parse_activation(activations[0]))
l_conv2 = CNN.Conv1DLayerSplitted(
l_nonlinear1,
n_filters[1],
filter_hight=windows[1],
nonlinearity=lasagne.nonlinearities.linear,
border_mode = "full"
)
l_fold2 = CNN.folding.FoldingLayer(l_conv2)
l_pool2 = CNN.pooling.KMaxPoolLayer(l_fold2, k=k_top)
l_nonlinear2 = lasagne.layers.NonlinearityLayer(l_pool2, nonlinearity=parse_activation(activations[1]))
l_dropout2 = lasagne.layers.DropoutLayer(l_nonlinear2, p=dropout)
l_out = lasagne.layers.DenseLayer(
l_dropout2,
num_units=n_out,
nonlinearity=lasagne.nonlinearities.softmax
)
return l_out
