예제 #1
0
def construct_model(vocab_size, embedding_dim, ngram_order, hidden_dims,
                    activations):
    # Construct the model
    x = tensor.lmatrix('features')
    y = tensor.lvector('targets')

    lookup = LookupTable(length=vocab_size, dim=embedding_dim, name='lookup')
    hidden = MLP(activations=activations + [None],
                 dims=[ngram_order * embedding_dim] + hidden_dims +
                 [vocab_size])

    embeddings = lookup.apply(x)
    embeddings = embeddings.flatten(ndim=2)  # Concatenate embeddings
    activations = hidden.apply(embeddings)
    cost = Softmax().categorical_cross_entropy(y, activations)

    # Initialize parameters
    lookup.weights_init = IsotropicGaussian(0.001)
    hidden.weights_init = IsotropicGaussian(0.01)
    hidden.biases_init = Constant(0.001)
    lookup.initialize()
    hidden.initialize()

    return cost
예제 #2
0
def construct_model(vocab_size, embedding_dim, ngram_order, hidden_dims,
                    activations):
    # Construct the model
    x = tensor.lmatrix('features')
    y = tensor.lvector('targets')

    lookup = LookupTable(length=vocab_size, dim=embedding_dim, name='lookup')
    hidden = MLP(activations=activations + [None],
                 dims=[ngram_order * embedding_dim] + hidden_dims +
                 [vocab_size])

    embeddings = lookup.apply(x)
    embeddings = embeddings.flatten(ndim=2)  # Concatenate embeddings
    activations = hidden.apply(embeddings)
    cost = Softmax().categorical_cross_entropy(y, activations)

    # Initialize parameters
    lookup.weights_init = IsotropicGaussian(0.001)
    hidden.weights_init = IsotropicGaussian(0.01)
    hidden.biases_init = Constant(0.001)
    lookup.initialize()
    hidden.initialize()

    return cost
예제 #3
0
attention = SimpleSequenceAttention(state_names=source_names,
                                    state_dims=[hidden_size_recurrent],
                                    attended_dim=context_size)

generator = SequenceGenerator(readout=readout,
                              transition=transition,
                              attention=attention,
                              name="generator")

generator.weights_init = IsotropicGaussian(0.01)
generator.biases_init = Constant(0.)
generator.initialize()

mlp_context.weights_init = IsotropicGaussian(0.01)
mlp_context.biases_init = Constant(0.)
mlp_context.initialize()

#ipdb.set_trace()
cost_matrix = generator.cost_matrix(x,
                                    x_mask,
                                    attended=mlp_context.apply(context))
cost = cost_matrix.sum() / x_mask.sum()
cost.name = "sequence_log_likelihood"

cg = ComputationGraph(cost)
model = Model(cost)

#################
# Algorithm
#################
예제 #4
0
파일: l3.py 프로젝트: anirudh9119/SpeechSyn
attention = SimpleSequenceAttention(
              state_names = source_names,
              state_dims = [hidden_size_recurrent],
              attended_dim = context_size)

generator = SequenceGenerator(readout=readout, 
                              transition=transition,
                              attention = attention,
                              name = "generator")

generator.weights_init = IsotropicGaussian(0.01)
generator.biases_init = Constant(0.)
generator.initialize()

mlp_context.weights_init = IsotropicGaussian(0.01)
mlp_context.biases_init = Constant(0.)
mlp_context.initialize()

#ipdb.set_trace()
cost_matrix = generator.cost_matrix(x, x_mask,
        attended = mlp_context.apply(context))
cost = cost_matrix.sum()/x_mask.sum()
cost.name = "sequence_log_likelihood"

cg = ComputationGraph(cost)
model = Model(cost)

#################
# Algorithm
#################