def __init__(self, input1_size, input2_size, lookup1_dim=200, lookup2_dim=200, hidden_size=512):
self.hidden_size = hidden_size
self.input1_size = input1_size
self.input2_size = input2_size
self.lookup1_dim = lookup1_dim
self.lookup2_dim = lookup2_dim
x1 = tensor.lmatrix('durations')
x2 = tensor.lmatrix('syllables')
y = tensor.lmatrix('pitches')
lookup1 = LookupTable(dim=self.lookup1_dim, length=self.input1_size, name='lookup1',
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
lookup1.initialize()
lookup2 = LookupTable(dim=self.lookup2_dim, length=self.input2_size, name='lookup2',
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
lookup2.initialize()
merge = Merge(['lookup1', 'lookup2'], [self.lookup1_dim, self.lookup2_dim], self.hidden_size,
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
merge.initialize()
recurrent_block = LSTM(dim=self.hidden_size, activation=Tanh(),
weights_init=initialization.Uniform(width=0.01)) #RecurrentStack([LSTM(dim=self.hidden_size, activation=Tanh())] * 3)
recurrent_block.initialize()
linear = Linear(input_dim=self.hidden_size, output_dim=self.input1_size,
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
linear.initialize()
softmax = NDimensionalSoftmax()
l1 = lookup1.apply(x1)
l2 = lookup2.apply(x2)
m = merge.apply(l1, l2)
h = recurrent_block.apply(m)
a = linear.apply(h)
y_hat = softmax.apply(a, extra_ndim=1)
# ValueError: x must be 1-d or 2-d tensor of floats. Got TensorType(float64, 3D)
self.Cost = softmax.categorical_cross_entropy(y, a, extra_ndim=1).mean()
self.ComputationGraph = ComputationGraph(self.Cost)
self.Model = Model(y_hat)
def __init__(self, input_sources_list, input_sources_vocab_size_list,
output_source, output_source_vocab_size,
lookup_dim=200, hidden_size=256, recurrent_stack_size=1):
self.InputSources = input_sources_list
self.InputSourcesVocab = input_sources_vocab_size_list
self.OutputSource = output_source
self.OutputSourceVocab = output_source_vocab_size
inputs = [tensor.lmatrix(source) for source in input_sources_list]
output = tensor.lmatrix(output_source)
lookups = self.get_lookups(lookup_dim, input_sources_vocab_size_list)
for lookup in lookups:
lookup.initialize()
merge = Merge([lookup.name for lookup in lookups], [lookup.dim for lookup in lookups], hidden_size,
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
merge.initialize()
linear0 = Linear(input_dim=hidden_size, output_dim=hidden_size,
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0), name='linear0')
linear0.initialize()
recurrent_blocks = []
for i in range(recurrent_stack_size):
recurrent_blocks.append(SimpleRecurrent(
dim=hidden_size, activation=Tanh(),
weights_init=initialization.Uniform(width=0.01),
use_bias=False))
for i, recurrent_block in enumerate(recurrent_blocks):
recurrent_block.name = 'recurrent'+str(i+1)
recurrent_block.initialize()
linear_out = Linear(input_dim=hidden_size, output_dim=output_source_vocab_size,
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0), name='linear_out')
linear_out.initialize()
softmax = NDimensionalSoftmax(name='softmax')
lookup_outputs = [lookup.apply(input) for lookup, input in zip(lookups, inputs)]
m = merge.apply(*lookup_outputs)
r = linear0.apply(m)
for block in recurrent_blocks:
r = block.apply(r)
a = linear_out.apply(r)
self.Cost = softmax.categorical_cross_entropy(output, a, extra_ndim=1).mean()
self.Cost.name = 'cost'
y_hat = softmax.apply(a, extra_ndim=1)
y_hat.name = 'y_hat'
self.ComputationGraph = ComputationGraph(self.Cost)
self.Function = None
self.MainLoop = None
self.Model = Model(y_hat)
def __init__(self,
input1_size,
input2_size,
lookup1_dim=200,
lookup2_dim=200,
hidden_size=512):
self.hidden_size = hidden_size
self.input1_size = input1_size
self.input2_size = input2_size
self.lookup1_dim = lookup1_dim
self.lookup2_dim = lookup2_dim
x1 = tensor.lmatrix('durations')
x2 = tensor.lmatrix('syllables')
y = tensor.lmatrix('pitches')
lookup1 = LookupTable(dim=self.lookup1_dim,
length=self.input1_size,
name='lookup1',
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
lookup1.initialize()
lookup2 = LookupTable(dim=self.lookup2_dim,
length=self.input2_size,
name='lookup2',
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
lookup2.initialize()
merge = Merge(['lookup1', 'lookup2'],
[self.lookup1_dim, self.lookup2_dim],
self.hidden_size,
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
merge.initialize()
recurrent_block = LSTM(
dim=self.hidden_size,
activation=Tanh(),
weights_init=initialization.Uniform(width=0.01)
) #RecurrentStack([LSTM(dim=self.hidden_size, activation=Tanh())] * 3)
recurrent_block.initialize()
linear = Linear(input_dim=self.hidden_size,
output_dim=self.input1_size,
weights_init=initialization.Uniform(width=0.01),
biases_init=Constant(0))
linear.initialize()
softmax = NDimensionalSoftmax()
l1 = lookup1.apply(x1)
l2 = lookup2.apply(x2)
m = merge.apply(l1, l2)
h = recurrent_block.apply(m)
a = linear.apply(h)
y_hat = softmax.apply(a, extra_ndim=1)
# ValueError: x must be 1-d or 2-d tensor of floats. Got TensorType(float64, 3D)
self.Cost = softmax.categorical_cross_entropy(y, a,
extra_ndim=1).mean()
self.ComputationGraph = ComputationGraph(self.Cost)
self.Model = Model(y_hat)