def build_model_vanilla(args, dtype=floatX):
logger.info('Building model ...')
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn, x_mask = get_prernn(args)
transitions = [
SimpleRecurrent(dim=args.state_dim, activation=Tanh())
for _ in range(args.layers)
]
rnn = RecurrentStack(transitions, skip_connections=args.skip_connections)
initialize_rnn(rnn, args)
# Prepare inputs and initial states for the RNN
kwargs, inits = get_rnn_kwargs(pre_rnn, args)
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, mask=x_mask, **kwargs)
# We have
# h = [state, state_1, state_2 ...] if args.layers > 1
# h = state if args.layers == 1
# If we have skip connections, concatenate all the states
# Else only consider the state of the highest layer
last_states = {}
hidden_states = []
if args.layers > 1:
# Save all the last states
for d in range(args.layers):
# TODO correct bug
# h[d] = h[d] * x_mask
last_states[d] = h[d][-1, :, :]
h[d].name = "hidden_state_" + str(d)
hidden_states.append(h[d])
if args.skip_connections or args.skip_output:
h = tensor.concatenate(h, axis=2)
else:
h = h[-1]
else:
# TODO correct bug
# hidden_states.append(h * x_mask)
hidden_states.append(h)
hidden_states[0].name = "hidden_state_0"
# Note: if we have mask, then updating initial state
# with last state does not make sence anymore.
last_states[0] = h[-1, :, :]
# The updates of the hidden states
updates = []
for d in range(args.layers):
updates.append((inits[0][d], last_states[d]))
presoft = get_presoft(h, args)
cost, unregularized_cost = get_costs(presoft, args)
return cost, unregularized_cost, updates, hidden_states
def __init__(self, vocab_size, embedding_dim, dgru_state_dim, dgru_depth,
**kwargs):
super(Decimator, self).__init__(**kwargs)
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.dgru_state_dim = dgru_state_dim
self.embedding_dim = embedding_dim
self.lookup = LookupTable(name='embeddings')
self.dgru_depth = dgru_depth
# representation
self.dgru = RecurrentStack([
DGRU(activation=Tanh(), dim=self.dgru_state_dim)
for _ in range(dgru_depth)
],
skip_connections=True)
# importance of this representation
self.bidir_w = Bidirectional(RecurrentWithFork(
DGRU(activation=Tanh(), dim=self.dgru_state_dim // 2),
self.embedding_dim,
name='src_word_with_fork'),
name='bidir_src_word_encoder')
self.gru_fork = Fork(
[name for name in self.dgru.apply.sequences if name != 'mask'],
prototype=Linear(),
name='gru_fork')
# map to a energy scalar
self.wl = Linear(input_dim=dgru_state_dim, output_dim=1)
self.children = [
self.lookup, self.dgru, self.gru_fork, self.bidir_w, self.wl
]
def build_model_vanilla(args, dtype=floatX):
logger.info('Building model ...')
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn, x_mask = get_prernn(args)
transitions = [SimpleRecurrent(dim=args.state_dim, activation=Tanh())
for _ in range(args.layers)]
rnn = RecurrentStack(transitions, skip_connections=args.skip_connections)
initialize_rnn(rnn, args)
# Prepare inputs and initial states for the RNN
kwargs, inits = get_rnn_kwargs(pre_rnn, args)
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, mask=x_mask, **kwargs)
# We have
# h = [state, state_1, state_2 ...] if args.layers > 1
# h = state if args.layers == 1
# If we have skip connections, concatenate all the states
# Else only consider the state of the highest layer
last_states = {}
hidden_states = []
if args.layers > 1:
# Save all the last states
for d in range(args.layers):
# TODO correct bug
# h[d] = h[d] * x_mask
last_states[d] = h[d][-1, :, :]
h[d].name = "hidden_state_" + str(d)
hidden_states.append(h[d])
if args.skip_connections or args.skip_output:
h = tensor.concatenate(h, axis=2)
else:
h = h[-1]
else:
# TODO correct bug
# hidden_states.append(h * x_mask)
hidden_states.append(h)
hidden_states[0].name = "hidden_state_0"
# Note: if we have mask, then updating initial state
# with last state does not make sence anymore.
last_states[0] = h[-1, :, :]
# The updates of the hidden states
updates = []
for d in range(args.layers):
updates.append((inits[0][d], last_states[d]))
presoft = get_presoft(h, args)
cost, unregularized_cost = get_costs(presoft, args)
return cost, unregularized_cost, updates, hidden_states
def __init__(self,
vocab_size,
embedding_dim,
igru_state_dim,
igru_depth,
trg_dgru_depth,
emitter,
feedback_brick,
merge=None,
merge_prototype=None,
post_merge=None,
**kwargs):
merged_dim = igru_state_dim
if not merge:
merge = Merge(input_names=kwargs['source_names'],
prototype=merge_prototype)
if not post_merge:
post_merge = Bias(dim=merged_dim)
# for compatible
if igru_depth == 1:
self.igru = IGRU(dim=igru_state_dim)
else:
self.igru = RecurrentStack(
[IGRU(dim=igru_state_dim, name='igru')] + [
UpperIGRU(dim=igru_state_dim,
activation=Tanh(),
name='upper_igru' + str(i))
for i in range(1, igru_depth)
],
skip_connections=True)
self.embedding_dim = embedding_dim
self.emitter = emitter
self.feedback_brick = feedback_brick
self.merge = merge
self.post_merge = post_merge
self.merged_dim = merged_dim
self.igru_depth = igru_depth
self.trg_dgru_depth = trg_dgru_depth
self.lookup = LookupTable(name='embeddings')
self.vocab_size = vocab_size
self.igru_state_dim = igru_state_dim
self.gru_to_softmax = Linear(input_dim=igru_state_dim,
output_dim=vocab_size)
self.gru_fork = Fork([
name for name in self.igru.apply.sequences
if name != 'mask' and name != 'input_states'
],
prototype=Linear(),
name='gru_fork')
children = [
self.emitter, self.feedback_brick, self.merge, self.post_merge,
self.igru, self.lookup, self.gru_to_softmax, self.gru_fork
]
kwargs.setdefault('children', []).extend(children)
super(Interpolator, self).__init__(**kwargs)
def setUp(self):
depth = 4
self.depth = depth
dim = 3 # don't change, hardwired in the code
transitions = [LSTM(dim=dim) for _ in range(depth)]
self.stack0 = RecurrentStack(transitions,
weights_init=Constant(2),
biases_init=Constant(0))
self.stack0.initialize()
self.stack2 = RecurrentStack(transitions,
weights_init=Constant(2),
biases_init=Constant(0),
skip_connections=True)
self.stack2.initialize()
def __init__(self,hidden_size_recurrent, k, **kwargs):
super(Scribe, self).__init__(**kwargs)
readout_size =6*k+1
transition = [GatedRecurrent(dim=hidden_size_recurrent,
name = "gru_{}".format(i) ) for i in range(3)]
transition = RecurrentStack( transition,
name="transition", skip_connections = True)
emitter = BivariateGMMEmitter(k = k)
source_names = [name for name in transition.apply.states
if 'states' in name]
readout = Readout(
readout_dim = readout_size,
source_names =source_names,
emitter=emitter,
name="readout")
self.generator = SequenceGenerator(readout=readout,
transition=transition,
name = "generator")
self.children = [self.generator]
def test_split_suffix(self):
# generate some numbers
level1, level2 = numpy.random.randint(1, 150, size=(2,))
name1 = "somepart"
# test cases like (<given_name>, <expected_name>, <expected_level>)
# name, level, level2 and sep will be provided
test_cases = [
# case layer == 0
("{name}", "{name}", 0),
# case empty name part
("{sep}{level}", "", level1),
# normal case
("{name}{sep}{level}","{name}",level1),
# case nested recurrent stacks
("{name}{sep}{level}{sep}{level2}","{name}{sep}{level}", level2),
# some more edge cases...
("{sep}{name}{sep}{level}", "{sep}{name}", level1),
("{name}{sep}","{name}{sep}", 0),
("{name}{sep}{name}","{name}{sep}{name}", 0),
("{name}{sep}{level}{sep}{name}", "{name}{sep}{level}{sep}{name}", 0)
]
# check all test cases
for _name, _expected_name_part, expected_level in test_cases:
# fill in aktual details like the currend RECURRENTSTACK_SEPARATOR
name = _name.format(name=name1, level=level1, level2=level2, sep=RECURRENTSTACK_SEPARATOR)
expected_name_part = _expected_name_part.format(name=name1, level=level1, level2=level2,
sep=RECURRENTSTACK_SEPARATOR)
name_part, level = RecurrentStack.split_suffix(name)
assert name_part == expected_name_part and level == expected_level, \
"expected split_suffex(\"{}\") -> name(\"{}\"), level({}) got name(\"{}\"), level({})".format(
name, expected_name_part, expected_level, name_part, level)
def setUp(self):
prototype = SimpleRecurrent(dim=3, activation=Tanh())
self.layers = [
Bidirectional(weights_init=Orthogonal(), prototype=prototype)
for _ in range(3)
]
self.stack = RecurrentStack(self.layers)
for fork in self.stack.forks:
fork.weights_init = Identity(1)
fork.biases_init = Constant(0)
self.stack.initialize()
self.x_val = 0.1 * numpy.asarray(
list(itertools.permutations(range(4))), dtype=theano.config.floatX)
self.x_val = (numpy.ones(
(24, 4, 3), dtype=theano.config.floatX) * self.x_val[..., None])
self.mask_val = numpy.ones((24, 4), dtype=theano.config.floatX)
self.mask_val[12:24, 3] = 0
def __init__(self, vocab_size, embedding_dim, dgru_state_dim, dgru_depth,
**kwargs):
super(TargetWordEncoder, self).__init__(**kwargs)
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.dgru_state_dim = dgru_state_dim
self.embedding_dim = embedding_dim
self.lookup = LookupTable(name='embeddings')
self.dgru_depth = dgru_depth
self.dgru = RecurrentStack([
DGRU(activation=Tanh(), dim=self.dgru_state_dim)
for _ in range(dgru_depth)
],
skip_connections=True)
self.gru_fork = Fork(
[name for name in self.dgru.apply.sequences if name != 'mask'],
prototype=Linear(),
name='gru_fork')
self.children = [self.lookup, self.dgru, self.gru_fork]
def test_split_suffix(self):
# generate some numbers
level1, level2 = numpy.random.randint(1, 150, size=(2, ))
name1 = "somepart"
# test cases like (<given_name>, <expected_name>, <expected_level>)
# name, level, level2 and sep will be provided
test_cases = [
# case layer == 0
("{name}", "{name}", 0),
# case empty name part
("{sep}{level}", "", level1),
# normal case
("{name}{sep}{level}", "{name}", level1),
# case nested recurrent stacks
("{name}{sep}{level}{sep}{level2}", "{name}{sep}{level}", level2),
# some more edge cases...
("{sep}{name}{sep}{level}", "{sep}{name}", level1),
("{name}{sep}", "{name}{sep}", 0),
("{name}{sep}{name}", "{name}{sep}{name}", 0),
("{name}{sep}{level}{sep}{name}", "{name}{sep}{level}{sep}{name}",
0)
]
# check all test cases
for _name, _expected_name_part, expected_level in test_cases:
# fill in aktual details like the currend RECURRENTSTACK_SEPARATOR
name = _name.format(name=name1,
level=level1,
level2=level2,
sep=RECURRENTSTACK_SEPARATOR)
expected_name_part = _expected_name_part.format(
name=name1,
level=level1,
level2=level2,
sep=RECURRENTSTACK_SEPARATOR)
name_part, level = RecurrentStack.split_suffix(name)
condition = (name_part == expected_name_part
and level == expected_level)
assert condition, "expected split_suffex(\"{}\") " \
"-> name(\"{}\"), level({}) got " \
"name(\"{}\"), level({})".format(
name, expected_name_part,
expected_level,
name_part, level)
def setUp(self):
prototype = SimpleRecurrent(dim=3, activation=Tanh())
self.layers = [
Bidirectional(weights_init=Orthogonal(), prototype=prototype)
for _ in range(3)]
self.stack = RecurrentStack(self.layers)
for fork in self.stack.forks:
fork.weights_init = Identity(1)
fork.biases_init = Constant(0)
self.stack.initialize()
self.x_val = 0.1 * numpy.asarray(
list(itertools.permutations(range(4))),
dtype=theano.config.floatX)
self.x_val = (numpy.ones((24, 4, 3), dtype=theano.config.floatX) *
self.x_val[..., None])
self.mask_val = numpy.ones((24, 4), dtype=theano.config.floatX)
self.mask_val[12:24, 3] = 0
def test_suffix(self):
# level >= 0 !!
level1, = numpy.random.randint(1, 150, size=(1, ))
# name1 != "mask" !!
name1 = "somepart"
test_cases = [("mask", level1, "mask"), ("{name}", 0, "{name}"),
("{name}", level1, "{name}{sep}{level}")]
for _name, level, _expected_result in test_cases:
name = _name.format(name=name1,
level=level1,
sep=RECURRENTSTACK_SEPARATOR)
expected_result = _expected_result.format(
name=name1, level=level1, sep=RECURRENTSTACK_SEPARATOR)
resut = RecurrentStack.suffix(name, level)
assert resut == expected_result, "expected suffix(\"{}\",{}) -> \"{}\" got \"{}\"".format(
name, level, expected_result, resut)
def test_suffix(self):
# level >= 0 !!
level1, = numpy.random.randint(1, 150, size=(1,))
# name1 != "mask" !!
name1 = "somepart"
test_cases = [
("mask", level1, "mask"),
("{name}", 0, "{name}"),
("{name}", level1, "{name}{sep}{level}")
]
for _name, level, _expected_result in test_cases:
name = _name.format(name=name1, level=level1, sep=RECURRENTSTACK_SEPARATOR)
expected_result = _expected_result.format(name=name1, level=level1, sep=RECURRENTSTACK_SEPARATOR)
resut = RecurrentStack.suffix(name, level)
assert resut == expected_result, "expected suffix(\"{}\",{}) -> \"{}\" got \"{}\"".format(name, level,
expected_result,
resut)
class TestBidirectionalStack(unittest.TestCase):
def setUp(self):
prototype = SimpleRecurrent(dim=3, activation=Tanh())
self.layers = [
Bidirectional(weights_init=Orthogonal(), prototype=prototype)
for _ in range(3)
]
self.stack = RecurrentStack(self.layers)
for fork in self.stack.forks:
fork.weights_init = Identity(1)
fork.biases_init = Constant(0)
self.stack.initialize()
self.x_val = 0.1 * numpy.asarray(
list(itertools.permutations(range(4))), dtype=theano.config.floatX)
self.x_val = (numpy.ones(
(24, 4, 3), dtype=theano.config.floatX) * self.x_val[..., None])
self.mask_val = numpy.ones((24, 4), dtype=theano.config.floatX)
self.mask_val[12:24, 3] = 0
def test_steps(self):
x = tensor.tensor3('x')
mask = tensor.matrix('mask')
calc_stack_layers = [
theano.function([x, mask],
self.stack.apply(x, mask=mask)[i])
for i in range(len(self.layers))
]
stack_layers = [
f(self.x_val, self.mask_val) for f in calc_stack_layers
]
h_val = self.x_val
for stack_layer_value, bidir_net in zip(stack_layers, self.layers):
calc = theano.function([x, mask], bidir_net.apply(x, mask=mask))
simple_layer_value = calc(h_val, self.mask_val)
assert_allclose(stack_layer_value, simple_layer_value, rtol=1e-04)
h_val = simple_layer_value[..., :3]
def test_dims(self):
self.assertEqual(self.stack.get_dim("inputs"), 3)
for i in range(len(self.layers)):
state_name = self.stack.suffix("states", i)
self.assertEqual(self.stack.get_dim(state_name), 6)
class TestBidirectionalStack(unittest.TestCase):
def setUp(self):
prototype = SimpleRecurrent(dim=3, activation=Tanh())
self.layers = [
Bidirectional(weights_init=Orthogonal(), prototype=prototype)
for _ in range(3)]
self.stack = RecurrentStack(self.layers)
for fork in self.stack.forks:
fork.weights_init = Identity(1)
fork.biases_init = Constant(0)
self.stack.initialize()
self.x_val = 0.1 * numpy.asarray(
list(itertools.permutations(range(4))),
dtype=theano.config.floatX)
self.x_val = (numpy.ones((24, 4, 3), dtype=theano.config.floatX) *
self.x_val[..., None])
self.mask_val = numpy.ones((24, 4), dtype=theano.config.floatX)
self.mask_val[12:24, 3] = 0
def test_steps(self):
x = tensor.tensor3('x')
mask = tensor.matrix('mask')
calc_stack_layers = [
theano.function([x, mask], self.stack.apply(x, mask=mask)[i])
for i in range(len(self.layers))]
stack_layers = [
f(self.x_val, self.mask_val) for f in calc_stack_layers]
h_val = self.x_val
for stack_layer_value, bidir_net in zip(stack_layers, self.layers):
calc = theano.function([x, mask], bidir_net.apply(x, mask=mask))
simple_layer_value = calc(h_val, self.mask_val)
assert_allclose(stack_layer_value, simple_layer_value, rtol=1e-04)
h_val = simple_layer_value[..., :3]
def test_dims(self):
self.assertEqual(self.stack.get_dim("inputs"), 3)
for i in range(len(self.layers)):
state_name = self.stack.suffix("states", i)
self.assertEqual(self.stack.get_dim(state_name), 6)
def build_model_lstm(args, dtype=floatX):
logger.info('Building model ...')
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn, x_mask = get_prernn(args)
transitions = [LSTM(dim=args.state_dim, activation=Tanh())
for _ in range(args.layers)]
rnn = RecurrentStack(transitions, skip_connections=args.skip_connections)
initialize_rnn(rnn, args)
# Prepare inputs and initial states for the RNN
kwargs, inits = get_rnn_kwargs(pre_rnn, args)
# Apply the RNN to the inputs
h = rnn.apply(mask=x_mask, **kwargs)
# h = [state, cell, in, forget, out, state_1,
# cell_1, in_1, forget_1, out_1 ...]
last_states = {}
last_cells = {}
hidden_states = []
for d in range(args.layers):
# TODO correct bug
# h[5 * d] = h[5 * d] * x_mask
# h[5 * d + 1] = h[5 * d + 1] * x_mask
last_states[d] = h[5 * d][-1, :, :]
last_cells[d] = h[5 * d + 1][-1, :, :]
h[5 * d].name = "hidden_state_" + str(d)
h[5 * d + 1].name = "hidden_cell_" + str(d)
hidden_states.extend([h[5 * d], h[5 * d + 1]])
# The updates of the hidden states
# Note: if we have mask, then updating initial state
# with last state does not make sence anymore.
updates = []
for d in range(args.layers):
updates.append((inits[0][d], last_states[d]))
updates.append((inits[1][d], last_states[d]))
# h = [state, cell, in, forget, out, state_1,
# cell_1, in_1, forget_1, out_1 ...]
# Extract the values
in_gates = h[2::5]
forget_gates = h[3::5]
out_gates = h[4::5]
gate_values = {"in_gates": in_gates,
"forget_gates": forget_gates,
"out_gates": out_gates}
h = h[::5]
# Now we have correctly:
# h = [state, state_1, state_2 ...] if args.layers > 1
# h = [state] if args.layers == 1
# If we have skip connections, concatenate all the states
# Else only consider the state of the highest layer
if args.layers > 1:
if args.skip_connections or args.skip_output:
h = tensor.concatenate(h, axis=2)
else:
h = h[-1]
else:
h = h[0]
h.name = "hidden_state_all"
presoft = get_presoft(h, args)
cost, unregularized_cost = get_costs(presoft, args)
return cost, unregularized_cost, updates, gate_values, hidden_states
def build_model_cw(args, dtype=floatX):
logger.info('Building model ...')
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn, x_mask = get_prernn(args)
# Note that this order of the periods makes faster modules flow in slower
# ones with is the opposite of the original paper
if args.module_order == "fast_in_slow":
transitions = [ClockworkBase(
dim=args.state_dim, activation=Tanh(),
period=2 ** i) for i in range(args.layers)]
elif args.module_order == "slow_in_fast":
transitions = [ClockworkBase(
dim=args.state_dim,
activation=Tanh(),
period=2 ** (args.layers - i - 1)) for i in range(args.layers)]
else:
assert False
rnn = RecurrentStack(transitions, skip_connections=args.skip_connections)
initialize_rnn(rnn, args)
# Prepare inputs and initial states for the RNN
kwargs, inits = get_rnn_kwargs(pre_rnn, args)
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, mask=x_mask, **kwargs)
# In the Clockwork case:
# h = [state, time, state_1, time_1 ...]
h = h[::2]
# Now we have correctly:
# h = [state, state_1, state_2 ...] if args.layers > 1
# h = [state] if args.layers == 1
# If we have skip connections, concatenate all the states
# Else only consider the state of the highest layer
last_states = {}
hidden_states = []
if args.layers > 1:
# Save all the last states
for d in range(args.layers):
# TODO correct the bug
# h[d] = h[d] * x_mask
last_states[d] = h[d][-1, :, :]
h[d].name = "hidden_state_" + str(d)
hidden_states.append(h[d])
h = tensor.concatenate(h, axis=2)
else:
h = h[0] * x_mask
last_states[0] = h[-1, :, :]
h.name = "hidden_state_all"
# The updates of the hidden states
updates = []
for d in range(args.layers):
updates.append((inits[0][d], last_states[d]))
presoft = get_presoft(h, args)
cost, unregularized_cost = get_costs(presoft, args)
return cost, unregularized_cost, updates, hidden_states
def __init__(self,
batch_size,
frame_size,
k,
depth,
size,
**kwargs):
super(PyramidLayer, self).__init__(**kwargs)
target_size = frame_size * k
depth_x = depth
hidden_size_mlp_x = 32*size
depth_transition = depth-1
depth_theta = depth
hidden_size_mlp_theta = 32*size
hidden_size_recurrent = 32*size*3
depth_context = depth
hidden_size_mlp_context = 32*size
context_size = 32*size
activations_x = [Rectifier()]*depth_x
dims_x = [frame_size] + [hidden_size_mlp_x]*(depth_x-1) + \
[4*hidden_size_recurrent]
activations_theta = [Rectifier()]*depth_theta
dims_theta = [hidden_size_recurrent] + \
[hidden_size_mlp_theta]*depth_theta
activations_context = [Rectifier()]*depth_context
dims_context = [frame_size] + [hidden_size_mlp_context]*(depth_context-1) + \
[context_size]
mlp_x = MLP(activations = activations_x,
dims = dims_x,
name = "mlp_x")
feedback = DeepTransitionFeedback(mlp = mlp_x)
transition = [GatedRecurrent(dim=hidden_size_recurrent,
use_bias = True,
name = "gru_{}".format(i) ) for i in range(depth_transition)]
transition = RecurrentStack( transition,
name="transition", skip_connections = True)
self.transition = transition
mlp_theta = MLP( activations = activations_theta,
dims = dims_theta,
name = "mlp_theta")
mlp_gmm = GMMMLP(mlp = mlp_theta,
dim = target_size,
k = k,
const = 0.00001,
name = "gmm_wrap")
gmm_emitter = GMMEmitter(gmmmlp = mlp_gmm,
output_size = frame_size, k = k)
source_names = [name for name in transition.apply.states if 'states' in name]
attention = SimpleSequenceAttention(
state_names = source_names,
state_dims = [hidden_size_recurrent],
attended_dim = context_size,
name = "attention")
#ipdb.set_trace()
# Verify source names
readout = Readout(
readout_dim = hidden_size_recurrent,
source_names =source_names + ['feedback'] + ['glimpses'],
emitter=gmm_emitter,
feedback_brick = feedback,
name="readout")
self.generator = SequenceGenerator(readout=readout,
transition=transition,
attention = attention,
name = "generator")
self.mlp_context = MLP(activations = activations_context,
dims = dims_context)
self.children = [self.generator, self.mlp_context]
self.final_states = []
def build_fork_lookup(vocab_size, time_length, args):
x = tensor.lmatrix('features')
virtual_dim = 6
state_dim = 6
skip_connections = False
layers = 1
# Build the model
output_names = []
output_dims = []
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
output_names.append("inputs" + suffix)
output_dims.append(virtual_dim)
lookup = LookupTable(length=vocab_size, dim=virtual_dim)
lookup.weights_init = initialization.IsotropicGaussian(0.1)
lookup.biases_init = initialization.Constant(0)
fork = Fork(output_names=output_names, input_dim=time_length,
output_dims=output_dims,
prototype=FeedforwardSequence(
[lookup.apply]))
# Note that this order of the periods makes faster modules flow in slower
# ones with is the opposite of the original paper
transitions = [ClockworkBase(dim=state_dim, activation=Tanh(),
period=2 ** i) for i in range(layers)]
rnn = RecurrentStack(transitions, skip_connections=skip_connections)
# Return list of 3D Tensor, one for each layer
# (Batch X Time X embedding_dim)
pre_rnn = fork.apply(x)
# Give time as the first index for each element in the list:
# (Time X Batch X embedding_dim)
if layers > 1 and skip_connections:
for t in range(len(pre_rnn)):
pre_rnn[t] = pre_rnn[t].dimshuffle(1, 0, 2)
else:
pre_rnn = pre_rnn.dimshuffle(1, 0, 2)
f_pre_rnn = theano.function([x], pre_rnn)
# Prepare inputs for the RNN
kwargs = OrderedDict()
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
if skip_connections:
kwargs['inputs' + suffix] = pre_rnn[d]
else:
kwargs['inputs' + suffix] = pre_rnn
print kwargs
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, **kwargs)
fork.initialize()
rnn.weights_init = initialization.Orthogonal()
rnn.biases_init = initialization.Constant(0)
rnn.initialize()
f_h = theano.function([x], h)
return f_pre_rnn, f_h
class SimplePyramidLayer(Initializable):
"""Basic unit for the pyramid model.
"""
def __init__(self,
batch_size,
frame_size,
k,
depth,
size,
**kwargs):
super(SimplePyramidLayer, self).__init__(**kwargs)
target_size = frame_size * k
depth_x = depth
hidden_size_mlp_x = 32*size
depth_transition = depth-1
depth_theta = depth
hidden_size_mlp_theta = 32*size
hidden_size_recurrent = 32*size*3
activations_x = [Rectifier()]*depth_x
dims_x = [frame_size] + [hidden_size_mlp_x]*(depth_x-1) + \
[4*hidden_size_recurrent]
activations_theta = [Rectifier()]*depth_theta
dims_theta = [hidden_size_recurrent] + \
[hidden_size_mlp_theta]*depth_theta
self.mlp_x = MLP(activations = activations_x,
dims = dims_x,
name = "mlp_x")
transition = [GatedRecurrent(dim=hidden_size_recurrent,
use_bias = True,
name = "gru_{}".format(i) ) for i in range(depth_transition)]
self.transition = RecurrentStack( transition,
name="transition", skip_connections = True)
mlp_theta = MLP( activations = activations_theta,
dims = dims_theta,
name = "mlp_theta")
mlp_gmm = GMMMLP(mlp = mlp_theta,
dim = target_size,
k = k,
const = 0.00001,
name = "gmm_wrap")
self.gmm_emitter = GMMEmitter(gmmmlp = mlp_gmm,
output_size = frame_size, k = k)
normal_inputs = [name for name in self.transition.apply.sequences
if 'mask' not in name]
self.fork = Fork(normal_inputs,
input_dim = 4*hidden_size_recurrent,
output_dims = self.transition.get_dims(normal_inputs))
self.children = [self.mlp_x, self.transition,
self.gmm_emitter, self.fork]
def monitoring_vars(self, cg):
mu, sigma, coeff = VariableFilter(
applications = [self.gmm_emitter.gmmmlp.apply],
name_regex = "output")(cg.variables)
min_sigma = sigma.min().copy(name="sigma_min")
mean_sigma = sigma.mean().copy(name="sigma_mean")
max_sigma = sigma.max().copy(name="sigma_max")
min_mu = mu.min().copy(name="mu_min")
mean_mu = mu.mean().copy(name="mu_mean")
max_mu = mu.max().copy(name="mu_max")
monitoring_vars = [mean_sigma, min_sigma,
min_mu, max_mu, mean_mu, max_sigma]
return monitoring_vars
@application
def cost(self, x, context, **kwargs):
x_g = self.mlp_x.apply(context)
inputs = self.fork.apply(x_g, as_dict = True)
h = self.transition.apply(**dict_union(inputs, kwargs))
self.final_states = []
for var in h:
self.final_states.append(var[-1].copy(name = var.name + "_final_value"))
cost = self.gmm_emitter.cost(h[-1], x)
return cost.mean()
@application
def generate(context):
x_g = self.mlp_x.apply(context)
inputs = self.fork.apply(x_g, as_dict = True)
h = self.transition.apply(**dict_union(inputs, kwargs))
return self.gmm_emitter.emit(h[-1])
def __init__(self,
batch_size,
frame_size,
k,
depth,
size,
**kwargs):
super(SimplePyramidLayer, self).__init__(**kwargs)
target_size = frame_size * k
depth_x = depth
hidden_size_mlp_x = 32*size
depth_transition = depth-1
depth_theta = depth
hidden_size_mlp_theta = 32*size
hidden_size_recurrent = 32*size*3
activations_x = [Rectifier()]*depth_x
dims_x = [frame_size] + [hidden_size_mlp_x]*(depth_x-1) + \
[4*hidden_size_recurrent]
activations_theta = [Rectifier()]*depth_theta
dims_theta = [hidden_size_recurrent] + \
[hidden_size_mlp_theta]*depth_theta
self.mlp_x = MLP(activations = activations_x,
dims = dims_x,
name = "mlp_x")
transition = [GatedRecurrent(dim=hidden_size_recurrent,
use_bias = True,
name = "gru_{}".format(i) ) for i in range(depth_transition)]
self.transition = RecurrentStack( transition,
name="transition", skip_connections = True)
mlp_theta = MLP( activations = activations_theta,
dims = dims_theta,
name = "mlp_theta")
mlp_gmm = GMMMLP(mlp = mlp_theta,
dim = target_size,
k = k,
const = 0.00001,
name = "gmm_wrap")
self.gmm_emitter = GMMEmitter(gmmmlp = mlp_gmm,
output_size = frame_size, k = k)
normal_inputs = [name for name in self.transition.apply.sequences
if 'mask' not in name]
self.fork = Fork(normal_inputs,
input_dim = 4*hidden_size_recurrent,
output_dims = self.transition.get_dims(normal_inputs))
self.children = [self.mlp_x, self.transition,
self.gmm_emitter, self.fork]
def build_model_cw(args, dtype=floatX):
logger.info('Building model ...')
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn, x_mask = get_prernn(args)
# Note that this order of the periods makes faster modules flow in slower
# ones with is the opposite of the original paper
if args.module_order == "fast_in_slow":
transitions = [
ClockworkBase(dim=args.state_dim, activation=Tanh(), period=2**i)
for i in range(args.layers)
]
elif args.module_order == "slow_in_fast":
transitions = [
ClockworkBase(dim=args.state_dim,
activation=Tanh(),
period=2**(args.layers - i - 1))
for i in range(args.layers)
]
else:
assert False
rnn = RecurrentStack(transitions, skip_connections=args.skip_connections)
initialize_rnn(rnn, args)
# Prepare inputs and initial states for the RNN
kwargs, inits = get_rnn_kwargs(pre_rnn, args)
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, mask=x_mask, **kwargs)
# In the Clockwork case:
# h = [state, time, state_1, time_1 ...]
h = h[::2]
# Now we have correctly:
# h = [state, state_1, state_2 ...] if args.layers > 1
# h = [state] if args.layers == 1
# If we have skip connections, concatenate all the states
# Else only consider the state of the highest layer
last_states = {}
hidden_states = []
if args.layers > 1:
# Save all the last states
for d in range(args.layers):
# TODO correct the bug
# h[d] = h[d] * x_mask
last_states[d] = h[d][-1, :, :]
h[d].name = "hidden_state_" + str(d)
hidden_states.append(h[d])
h = tensor.concatenate(h, axis=2)
else:
h = h[0] * x_mask
last_states[0] = h[-1, :, :]
h.name = "hidden_state_all"
# The updates of the hidden states
updates = []
for d in range(args.layers):
updates.append((inits[0][d], last_states[d]))
presoft = get_presoft(h, args)
cost, unregularized_cost = get_costs(presoft, args)
return cost, unregularized_cost, updates, hidden_states
def main(name, epochs, batch_size, learning_rate,
dim, mix_dim, old_model_name, max_length, bokeh, GRU, dropout,
depth, max_grad, step_method, epsilon, sample, skip, uniform, top):
#----------------------------------------------------------------------
datasource = name
def shnum(x):
""" Convert a positive float into a short tag-usable string
E.g.: 0 -> 0, 0.005 -> 53, 100 -> 1-2
"""
return '0' if x <= 0 else '%s%d' % (("%e"%x)[0], -np.floor(np.log10(x)))
jobname = "%s-%dX%dm%dd%dr%sb%de%s" % (datasource, depth, dim, mix_dim,
int(dropout*10),
shnum(learning_rate), batch_size,
shnum(epsilon))
if max_length != 600:
jobname += '-L%d'%max_length
if GRU:
jobname += 'g'
if max_grad != 5.:
jobname += 'G%g'%max_grad
if step_method != 'adam':
jobname += step_method
if skip:
jobname += 'D'
assert depth > 1
if top:
jobname += 'T'
assert depth > 1
if uniform > 0.:
jobname += 'u%d'%int(uniform*100)
if debug:
jobname += ".debug"
if sample:
print("Sampling")
else:
print("\nRunning experiment %s" % jobname)
if old_model_name:
print("starting from model %s"%old_model_name)
#----------------------------------------------------------------------
transitions = [GatedRecurrent(dim=dim) if GRU else LSTM(dim=dim)
for _ in range(depth)]
if depth > 1:
transition = RecurrentStack(transitions, name="transition",
skip_connections=skip or top)
if skip:
source_names=[RecurrentStack.suffix('states', d) for d in range(depth)]
else:
source_names=[RecurrentStack.suffix('states', depth-1)]
else:
transition = transitions[0]
transition.name = "transition"
source_names=['states']
emitter = SketchEmitter(mix_dim=mix_dim,
epsilon=epsilon,
name="emitter")
readout = Readout(
readout_dim=emitter.get_dim('inputs'),
source_names=source_names,
emitter=emitter,
name="readout")
generator = SequenceGenerator(readout=readout, transition=transition)
# Initialization settings
if uniform > 0.:
generator.weights_init = Uniform(width=uniform*2.)
else:
generator.weights_init = OrthogonalGlorot()
generator.biases_init = Constant(0)
# Build the cost computation graph [steps, batch_size, 3]
x = T.tensor3('features', dtype=floatX)
if debug:
x.tag.test_value = np.ones((max_length,batch_size,3)).astype(floatX)
x = x[:max_length,:,:] # has to be after setting test_value
cost = generator.cost(x)
cost.name = "sequence_log_likelihood"
# Give an idea of what's going on
model = Model(cost)
params = model.get_parameter_dict()
logger.info("Parameters:\n" +
pprint.pformat(
[(key, value.get_value().shape) for key, value
in params.items()],
width=120))
model_size = 0
for v in params.itervalues():
s = v.get_value().shape
model_size += s[0] * (s[1] if len(s) > 1 else 1)
logger.info("Total number of parameters %d"%model_size)
#------------------------------------------------------------
extensions = []
if old_model_name:
if old_model_name == 'continue':
old_model_name = jobname
with open(old_model_name + '_model', "rb") as f:
old_model = pickle.load(f)
model.set_parameter_values(old_model.get_parameter_values())
del old_model
else:
# Initialize parameters
for brick in model.get_top_bricks():
brick.initialize()
if sample:
assert old_model_name and old_model_name != 'continue'
Sample(generator, steps=max_length, path=old_model_name).do(None)
exit(0)
#------------------------------------------------------------
# Define the training algorithm.
cg = ComputationGraph(cost)
if dropout > 0.:
from blocks.roles import INPUT, OUTPUT
dropout_target = VariableFilter(roles=[OUTPUT],
bricks=transitions,
name_regex='states')(cg.variables)
print('# dropout %d' % len(dropout_target))
cg = apply_dropout(cg, dropout_target, dropout)
opt_cost = cg.outputs[0]
else:
opt_cost = cost
if step_method == 'adam':
step_rule = Adam(learning_rate)
elif step_method == 'rmsprop':
step_rule = RMSProp(learning_rate, decay_rate=0.95)
elif step_method == 'adagrad':
step_rule = AdaGrad(learning_rate)
elif step_method == 'adadelta':
step_rule = AdaDelta()
elif step_method == 'scale':
step_rule = Scale(learning_rate)
else:
raise Exception('Unknown sttep method %s'%step_method)
step_rule = CompositeRule([StepClipping(max_grad), step_rule])
algorithm = GradientDescent(
cost=opt_cost, parameters=cg.parameters,
step_rule=step_rule)
#------------------------------------------------------------
observables = [cost]
# Fetch variables useful for debugging
(energies,) = VariableFilter(
applications=[generator.readout.readout],
name_regex="output")(cg.variables)
min_energy = energies.min().copy(name="min_energy")
max_energy = energies.max().copy(name="max_energy")
observables += [min_energy, max_energy]
# (activations,) = VariableFilter(
# applications=[generator.transition.apply],
# name=generator.transition.apply.states[0])(cg.variables)
# mean_activation = named_copy(abs(activations).mean(),
# "mean_activation")
# observables.append(mean_activation)
observables += [algorithm.total_step_norm, algorithm.total_gradient_norm]
for name, param in params.items():
observables.append(param.norm(2).copy(
name=name + "_norm"))
observables.append(algorithm.gradients[param].norm(2).copy(
name=name + "_grad_norm"))
#------------------------------------------------------------
datasource_fname = os.path.join(fuel.config.data_path[0], datasource,
datasource+'.hdf5')
train_ds = H5PYDataset(datasource_fname, #max_length=max_length,
which_sets=['train'], sources=('features',),
load_in_memory=True)
train_stream = DataStream(train_ds,
iteration_scheme=ShuffledScheme(
train_ds.num_examples, batch_size))
test_ds = H5PYDataset(datasource_fname, #max_length=max_length,
which_sets=['test'], sources=('features',),
load_in_memory=True)
test_stream = DataStream(test_ds,
iteration_scheme=SequentialScheme(
test_ds.num_examples, batch_size))
train_stream = Mapping(train_stream, _transpose)
test_stream = Mapping(test_stream, _transpose)
def stream_stats(ds, label):
itr = ds.get_epoch_iterator(as_dict=True)
batch_count = 0
examples_count = 0
for batch in itr:
batch_count += 1
examples_count += batch['features'].shape[1]
print('%s #batch %d #examples %d' %
(label, batch_count, examples_count))
stream_stats(train_stream, 'train')
stream_stats(test_stream, 'test')
extensions += [Timing(every_n_batches=10),
TrainingDataMonitoring(
observables, prefix="train",
every_n_batches=10),
DataStreamMonitoring(
[cost], # without dropout
test_stream,
prefix="test",
on_resumption=True,
after_epoch=False, # by default this is True
every_n_batches=100),
# all monitored data is ready so print it...
# (next steps may take more time and we want to see the
# results as soon as possible so print as soon as you can)
Printing(every_n_batches=10),
# perform multiple dumps at different intervals
# so if one of them breaks (has nan) we can hopefully
# find a model from few batches ago in the other
Checkpoint(jobname,
before_training=False, after_epoch=True,
save_separately=['log', 'model']),
Sample(generator, steps=max_length,
path=jobname+'.test',
every_n_batches=100),
ProgressBar(),
FinishAfter(after_n_epochs=epochs)
# This shows a way to handle NaN emerging during
# training: simply finish it.
.add_condition(["after_batch"], _is_nan),
]
if bokeh:
from blocks.extras.extensions.plot import Plot
extensions.append(Plot(
'sketch',
channels=[['cost']], every_n_batches=10))
# Construct the main loop and start training!
main_loop = MainLoop(
model=model,
data_stream=train_stream,
algorithm=algorithm,
extensions=extensions
)
main_loop.run()
class Decimator(Initializable):
"""Source word encoder, mapping a charater-level word to a vector.
This encoder is able to learn the morphology.
For compatibility with previous version, we call it Decimator.
"""
def __init__(self, vocab_size, embedding_dim, dgru_state_dim, dgru_depth,
**kwargs):
super(Decimator, self).__init__(**kwargs)
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.dgru_state_dim = dgru_state_dim
self.embedding_dim = embedding_dim
self.lookup = LookupTable(name='embeddings')
self.dgru_depth = dgru_depth
# representation
self.dgru = RecurrentStack([
DGRU(activation=Tanh(), dim=self.dgru_state_dim)
for _ in range(dgru_depth)
],
skip_connections=True)
# importance of this representation
self.bidir_w = Bidirectional(RecurrentWithFork(
DGRU(activation=Tanh(), dim=self.dgru_state_dim // 2),
self.embedding_dim,
name='src_word_with_fork'),
name='bidir_src_word_encoder')
self.gru_fork = Fork(
[name for name in self.dgru.apply.sequences if name != 'mask'],
prototype=Linear(),
name='gru_fork')
# map to a energy scalar
self.wl = Linear(input_dim=dgru_state_dim, output_dim=1)
self.children = [
self.lookup, self.dgru, self.gru_fork, self.bidir_w, self.wl
]
def _push_allocation_config(self):
self.lookup.length = self.vocab_size
self.lookup.dim = self.embedding_dim
self.gru_fork.input_dim = self.embedding_dim
self.gru_fork.output_dims = [
self.dgru.get_dim(name) for name in self.gru_fork.output_names
]
@application(inputs=['char_seq', 'sample_matrix', 'char_aux'],
outputs=['representation', 'weight'])
def apply(self, char_seq, sample_matrix, char_aux):
# Time as first dimension
embeddings = self.lookup.apply(char_seq)
gru_out = self.dgru.apply(**merge(
self.gru_fork.apply(embeddings, as_dict=True), {'mask': char_aux}))
wgru_out = tensor.exp(
self.wl.apply(self.bidir_w.apply(embeddings, char_aux)))
if self.dgru_depth > 1:
gru_out = gru_out[-1]
gru_out = tensor.addbroadcast(wgru_out, 2) * gru_out
sampled_representation = tensor.tanh(
tensor.batched_dot(sample_matrix, gru_out.dimshuffle([1, 0, 2])))
return sampled_representation.dimshuffle([1, 0, 2]), wgru_out
def get_dim(self, name):
if name == 'output':
return self.dgru_state_dim
super(Decimator, self).get_dim(name)
def __init__(
self,
input_dims,
input_num_chars,
eos_label,
num_phonemes,
dim_dec,
dims_bidir,
enc_transition,
dec_transition,
use_states_for_readout,
attention_type,
criterion,
bottom,
lm=None,
character_map=None,
bidir=True,
subsample=None,
dims_top=None,
prior=None,
conv_n=None,
post_merge_activation=None,
post_merge_dims=None,
dim_matcher=None,
embed_outputs=True,
dim_output_embedding=None,
dec_stack=1,
conv_num_filters=1,
data_prepend_eos=True,
# softmax is the default set in SequenceContentAndConvAttention
energy_normalizer=None,
# for speech this is the approximate phoneme duration in frames
max_decoded_length_scale=1,
**kwargs):
if post_merge_activation is None:
post_merge_activation = Tanh()
super(SpeechRecognizer, self).__init__(**kwargs)
self.eos_label = eos_label
self.data_prepend_eos = data_prepend_eos
self.rec_weights_init = None
self.initial_states_init = None
self.enc_transition = enc_transition
self.dec_transition = dec_transition
self.dec_stack = dec_stack
self.criterion = criterion
self.max_decoded_length_scale = max_decoded_length_scale
post_merge_activation = post_merge_activation
if dim_matcher is None:
dim_matcher = dim_dec
# The bottom part, before BiRNN
bottom_class = bottom.pop('bottom_class')
bottom = bottom_class(input_dims=input_dims,
input_num_chars=input_num_chars,
name='bottom',
**bottom)
# BiRNN
if not subsample:
subsample = [1] * len(dims_bidir)
encoder = Encoder(self.enc_transition,
dims_bidir,
bottom.get_dim(bottom.apply.outputs[0]),
subsample,
bidir=bidir)
dim_encoded = encoder.get_dim(encoder.apply.outputs[0])
generators = [None, None]
for i in range(2):
# The top part, on top of BiRNN but before the attention
if dims_top:
top = MLP([Tanh()], [dim_encoded] + dims_top + [dim_encoded],
name="top{}".format(i))
else:
top = Identity(name='top{}'.format(i))
if dec_stack == 1:
transition = self.dec_transition(dim=dim_dec,
activation=Tanh(),
name="transition{}".format(i))
else:
transitions = [
self.dec_transition(dim=dim_dec,
activation=Tanh(),
name="transition_{}_{}".format(
i, trans_level))
for trans_level in xrange(dec_stack)
]
transition = RecurrentStack(transitions=transitions,
skip_connections=True)
# Choose attention mechanism according to the configuration
if attention_type == "content":
attention = SequenceContentAttention(
state_names=transition.apply.states,
attended_dim=dim_encoded,
match_dim=dim_matcher,
name="cont_att" + i)
elif attention_type == "content_and_conv":
attention = SequenceContentAndConvAttention(
state_names=transition.apply.states,
conv_n=conv_n,
conv_num_filters=conv_num_filters,
attended_dim=dim_encoded,
match_dim=dim_matcher,
prior=prior,
energy_normalizer=energy_normalizer,
name="conv_att{}".format(i))
else:
raise ValueError(
"Unknown attention type {}".format(attention_type))
if embed_outputs:
feedback = LookupFeedback(
num_phonemes + 1, dim_dec
if dim_output_embedding is None else dim_output_embedding)
else:
feedback = OneOfNFeedback(num_phonemes + 1)
if criterion['name'] == 'log_likelihood':
emitter = SoftmaxEmitter(initial_output=num_phonemes,
name="emitter{}".format(i))
if lm:
# In case we use LM it is Readout that is responsible
# for normalization.
emitter = LMEmitter()
elif criterion['name'].startswith('mse'):
emitter = RewardRegressionEmitter(criterion['name'],
eos_label,
num_phonemes,
criterion.get(
'min_reward', -1.0),
name="emitter")
else:
raise ValueError("Unknown criterion {}".format(
criterion['name']))
readout_config = dict(
readout_dim=num_phonemes,
source_names=(transition.apply.states if use_states_for_readout
else []) + [attention.take_glimpses.outputs[0]],
emitter=emitter,
feedback_brick=feedback,
name="readout{}".format(i))
if post_merge_dims:
readout_config['merged_dim'] = post_merge_dims[0]
readout_config['post_merge'] = InitializableSequence(
[
Bias(post_merge_dims[0]).apply,
post_merge_activation.apply,
MLP(
[post_merge_activation] *
(len(post_merge_dims) - 1) + [Identity()],
# MLP was designed to support Maxout is activation
# (because Maxout in a way is not one). However
# a single layer Maxout network works with the trick below.
# For deeper Maxout network one has to use the
# Sequence brick.
[
d //
getattr(post_merge_activation, 'num_pieces', 1)
for d in post_merge_dims
] + [num_phonemes]).apply,
],
name='post_merge{}'.format(i))
readout = Readout(**readout_config)
language_model = None
if lm and lm.get('path'):
lm_weight = lm.pop('weight', 0.0)
normalize_am_weights = lm.pop('normalize_am_weights', True)
normalize_lm_weights = lm.pop('normalize_lm_weights', False)
normalize_tot_weights = lm.pop('normalize_tot_weights', False)
am_beta = lm.pop('am_beta', 1.0)
if normalize_am_weights + normalize_lm_weights + normalize_tot_weights < 1:
logger.warn(
"Beam search is prone to fail with no log-prob normalization"
)
language_model = LanguageModel(nn_char_map=character_map, **lm)
readout = ShallowFusionReadout(
lm_costs_name='lm_add',
lm_weight=lm_weight,
normalize_am_weights=normalize_am_weights,
normalize_lm_weights=normalize_lm_weights,
normalize_tot_weights=normalize_tot_weights,
am_beta=am_beta,
**readout_config)
generators[i] = SequenceGenerator(readout=readout,
transition=transition,
attention=attention,
language_model=language_model,
name="generator{}".format(i))
self.generator = generators[0]
self.forward_to_backward = Linear(dim_dec, dim_dec)
# Remember child bricks
self.encoder = encoder
self.bottom = bottom
self.top = top
self.generators = generators
self.children = [self.forward_to_backward, encoder, top, bottom
] + generators
# Create input variables
self.inputs = self.bottom.batch_inputs
self.inputs_mask = self.bottom.mask
self.labels = tensor.lmatrix('labels')
self.labels_mask = tensor.matrix("labels_mask")
self.single_inputs = self.bottom.single_inputs
self.single_labels = tensor.lvector('labels')
self.n_steps = tensor.lscalar('n_steps')
def __init__(self,
input_dims,
input_num_chars,
bos_label, eos_label,
num_labels,
dim_dec, dims_bidir,
enc_transition, dec_transition,
use_states_for_readout,
attention_type,
criterion,
bottom,
lm=None, token_map=None,
bidir=True, window_size=None,
max_length=None, subsample=None,
dims_top=None, extra_input_dim=None,
prior=None, conv_n=None,
post_merge_activation=None,
post_merge_dims=None,
dim_matcher=None,
embed_outputs=True,
dim_output_embedding=None,
reuse_bottom_lookup_table=False,
dec_stack=1,
conv_num_filters=1,
data_prepend_eos=True,
# softmax is the default set in SequenceContentAndConvAttention
energy_normalizer=None,
# for speech this is the approximate phoneme duration in frames
max_decoded_length_scale=1,
# for criterions involving generation of outputs, whether
# or not they should be generated by the recognizer itself
generate_predictions=True,
compute_targets=True,
extra_generation_steps=3,
**kwargs):
all_arguments = copy.deepcopy(locals())
all_arguments.update(copy.deepcopy(kwargs))
del all_arguments['kwargs']
del all_arguments['self']
if post_merge_activation is None:
post_merge_activation = Tanh()
super(EncoderDecoder, self).__init__(**kwargs)
self.bos_label = bos_label
self.eos_label = eos_label
self.data_prepend_eos = data_prepend_eos
self.rec_weights_init = None
self.initial_states_init = None
self.enc_transition = enc_transition
self.dec_transition = dec_transition
self.dec_stack = dec_stack
self.criterion = criterion
self.generate_predictions = generate_predictions
self.extra_generation_steps = extra_generation_steps
self.compute_targets = compute_targets
self.max_decoded_length_scale = max_decoded_length_scale
post_merge_activation = post_merge_activation
if dim_matcher is None:
dim_matcher = dim_dec
# The bottom part, before BiRNN
bottom_class = bottom.pop('bottom_class')
bottom = bottom_class(
input_dims=input_dims, input_num_chars=input_num_chars,
name='bottom',
**bottom)
# BiRNN
if dims_bidir:
if not subsample:
subsample = [1] * len(dims_bidir)
encoder = Encoder(self.enc_transition, dims_bidir,
bottom.get_dim(bottom.apply.outputs[0]),
subsample, bidir=bidir)
elif window_size:
encoder = ConvEncoder(
max_length, bottom.get_dim(bottom.apply.outputs[0]), window_size)
else:
raise ValueError("Don't know which Encoder to use")
dim_encoded = encoder.get_dim(encoder.apply.outputs[0])
# The top part, on top of BiRNN but before the attention
if dims_top:
top = MLP([Tanh()],
[dim_encoded] + dims_top + [dim_encoded], name="top")
else:
top = Identity(name='top')
if dec_stack == 1:
transition = self.dec_transition(
dim=dim_dec, activation=Tanh(), name="transition")
else:
assert not extra_input_dim
transitions = [self.dec_transition(dim=dim_dec,
activation=Tanh(),
name="transition_{}".format(trans_level))
for trans_level in xrange(dec_stack)]
transition = RecurrentStack(transitions=transitions,
skip_connections=True)
# Choose attention mechanism according to the configuration
if attention_type == "content":
attention = SequenceContentAttention(
state_names=transition.apply.states,
attended_dim=dim_encoded, match_dim=dim_matcher,
name="cont_att")
elif attention_type == "content_and_conv":
attention = SequenceContentAndConvAttention(
state_names=transition.apply.states,
conv_n=conv_n,
conv_num_filters=conv_num_filters,
attended_dim=dim_encoded, match_dim=dim_matcher,
prior=prior,
energy_normalizer=energy_normalizer,
name="conv_att")
else:
raise ValueError("Unknown attention type {}"
.format(attention_type))
if not embed_outputs:
raise ValueError("embed_outputs=False is not supported any more")
if not reuse_bottom_lookup_table:
embedding = LookupTable(num_labels + 1,
dim_dec if
dim_output_embedding is None
else dim_output_embedding)
else:
embedding = bottom.children[0]
feedback = Feedback(
embedding=embedding,
output_names=[s for s in transition.apply.sequences
if s != 'mask'])
# Create a readout
readout_config = dict(
num_tokens=num_labels,
input_names=(transition.apply.states if use_states_for_readout else [])
+ [attention.take_glimpses.outputs[0]],
name="readout")
if post_merge_dims:
readout_config['merge_dim'] = post_merge_dims[0]
readout_config['post_merge'] = InitializableSequence([
Bias(post_merge_dims[0]).apply,
post_merge_activation.apply,
MLP([post_merge_activation] * (len(post_merge_dims) - 1) + [Identity()],
# MLP was designed to support Maxout is activation
# (because Maxout in a way is not one). However
# a single layer Maxout network works with the trick below.
# For deeper Maxout network one has to use the
# Sequence brick.
[d//getattr(post_merge_activation, 'num_pieces', 1)
for d in post_merge_dims] + [num_labels]).apply,
], name='post_merge')
if 'reward' in criterion and criterion['name'] != 'log_likelihood':
if criterion['reward'] == 'edit_distance':
readout_config['reward_brick'] = EditDistanceReward(
self.bos_label, self.eos_label)
elif criterion['reward'] == 'delta_edit_distance':
readout_config['reward_brick'] = EditDistanceReward(
self.bos_label, self.eos_label, deltas=True)
elif criterion['reward'] == 'bleu':
readout_config['reward_brick'] = BleuReward(
self.bos_label, self.eos_label, deltas=False)
elif criterion['reward'] == 'delta_bleu':
readout_config['reward_brick'] = BleuReward(
self.bos_label, self.eos_label, deltas=True)
else:
raise ValueError("Unknown reward type")
if criterion['name'] == 'log_likelihood':
readout_class = SoftmaxReadout
elif criterion['name'] == 'critic':
readout_class = CriticReadout
criterion_copy = dict(criterion)
del criterion_copy['name']
readout_config.update(**criterion_copy)
elif criterion['name'] == 'reinforce':
readout_class = ReinforceReadout
readout_config['merge_names'] = list(readout_config['input_names'])
readout_config['entropy'] = criterion.get('entropy')
readout_config['input_names'] += ['attended', 'attended_mask']
elif criterion['name'] in ['sarsa', 'actor_critic']:
readout_class = ActorCriticReadout
if criterion['name'] == 'actor_critic':
critic_arguments = dict(all_arguments)
# No worries, critic will not compute log likelihood values.
# We
critic_arguments['criterion'] = {
'name': 'critic',
'value_softmax': criterion.get('value_softmax'),
'same_value_for_wrong': criterion.get('same_value_for_wrong'),
'groundtruth_word_bonus': criterion.get('groundtruth_word_bonus'),
'dueling_outputs': criterion.get('dueling_outputs')}
critic_arguments['name'] = 'critic'
if criterion.get('critic_uses_actor_states'):
critic_arguments['extra_input_dim'] = dim_dec
if (criterion.get('value_softmax')
or criterion.get('same_value_for_wrong')
or criterion.get('dueling_outputs')):
# Add an extra output for the critic
critic_arguments['num_labels'] = num_labels + 1
if criterion.get('force_bidir'):
critic_arguments['dims_bidir'] = [dim_dec]
critic_arguments['reuse_bottom_lookup_table'] = True
critic_arguments['input_num_chars'] = {'inputs': num_labels}
if criterion.get('downsize_critic'):
critic_arguments = _downsize_config(
critic_arguments, criterion['downsize_critic'])
critic = EncoderDecoder(**critic_arguments)
readout_config['critic'] = critic
readout_config['merge_names'] = list(readout_config['input_names'])
readout_config['freeze_actor'] = criterion.get('freeze_actor')
readout_config['freeze_critic'] = criterion.get('freeze_critic')
readout_config['critic_uses_actor_states'] = criterion.get('critic_uses_actor_states')
readout_config['critic_uses_groundtruth'] = criterion.get('critic_uses_groundtruth')
readout_config['critic_burnin_steps'] = criterion.get('critic_burnin_steps')
readout_config['critic_loss'] = criterion.get('critic_loss')
readout_config['discount'] = criterion.get('discount')
readout_config['entropy_reward_coof'] = criterion.get('entropy_reward_coof')
readout_config['cross_entropy_reward_coof'] = criterion.get('cross_entropy_reward_coof')
readout_config['value_penalty'] = criterion.get('value_penalty')
readout_config['value_penalty_type'] = criterion.get('value_penalty_type')
readout_config['critic_policy_t'] = criterion.get('critic_policy_t')
readout_config['bos_token'] = bos_label
readout_config['accumulate_outputs'] = criterion.get('accumulate_outputs')
readout_config['use_value_biases'] = criterion.get('use_value_biases')
readout_config['actor_grad_estimate'] = criterion.get('actor_grad_estimate')
readout_config['input_names'] += ['attended', 'attended_mask']
# Note, that settings below are for the "clean" mode.
# When get_cost_graph() is run with training=True, they
# are temporarily overriden with the "real" settings from
# "criterion"
readout_config['compute_targets'] = True
readout_config['trpo_coef'] = 0.0
readout_config['solve_bellman'] = True
else:
raise ValueError("Unknown criterion {}".format(criterion['name']))
readout = readout_class(**readout_config)
if lm:
raise ValueError("LM is currently not supported")
recurrent = AttentionRecurrent(transition, attention)
if extra_input_dim:
recurrent = RecurrentWithExtraInput(
recurrent, "extra_inputs", extra_input_dim, name="with_extra_inputs")
generator = SequenceGenerator(
recurrent=recurrent, readout=readout, feedback=feedback,
name="generator")
# Remember child bricks
self.encoder = encoder
self.bottom = bottom
self.top = top
self.generator = generator
self.softmax = Softmax()
self.children = [encoder, top, bottom, generator, self.softmax]
# Create input variables
self.inputs = self.bottom.batch_inputs
self.inputs_mask = self.bottom.mask
self.labels = tensor.lmatrix('labels')
self.labels_mask = tensor.matrix("labels_mask")
self.predicted_labels = tensor.lmatrix('predicted_labels')
self.predicted_mask = tensor.matrix('predicted_mask')
self.prefix_labels = tensor.lmatrix('prefix_labels')
self.prefix_steps = tensor.lscalar('prefix_steps')
self.single_inputs = self.bottom.single_inputs
self.single_labels = tensor.lvector('labels')
self.single_predicted_labels = tensor.lvector('predicted_labels')
self.n_steps = tensor.lscalar('n_steps')
# Configure mixed_generate
if criterion['name'] == 'actor_critic':
critic = self.generator.readout.critic
self.mixed_generate.sequences = []
self.mixed_generate.states = (
['step'] +
self.generator.recurrent.apply.states +
['critic_' + name for name in critic.generator.recurrent.apply.states])
self.mixed_generate.outputs = (
['samples', 'step'] +
self.generator.recurrent.apply.outputs +
['critic_' + name for name in critic.generator.recurrent.apply.outputs])
self.mixed_generate.contexts = (
self.generator.recurrent.apply.contexts +
['critic_' + name for name in critic.generator.recurrent.apply.contexts]
+ ['groundtruth', 'groundtruth_mask'])
self.initial_states.outputs = self.mixed_generate.states
self.prefix_generate.sequences = []
self.prefix_generate.states = ['step'] + self.generator.recurrent.apply.states
self.prefix_generate.outputs = ['samples', 'step'] + self.generator.recurrent.apply.outputs
self.prefix_generate.contexts = self.generator.recurrent.apply.contexts
def build_model_vanilla(vocab_size, args, dtype=floatX):
logger.info('Building model ...')
# Parameters for the model
context = args.context
state_dim = args.state_dim
layers = args.layers
skip_connections = args.skip_connections
# Symbolic variables
# In both cases: Time X Batch
x = tensor.lmatrix('features')
y = tensor.lmatrix('targets')
# Build the model
output_names = []
output_dims = []
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
output_names.append("inputs" + suffix)
output_dims.append(state_dim)
lookup = LookupTable(length=vocab_size, dim=state_dim)
lookup.weights_init = initialization.IsotropicGaussian(0.1)
lookup.biases_init = initialization.Constant(0)
fork = Fork(output_names=output_names, input_dim=args.mini_batch_size,
output_dims=output_dims,
prototype=FeedforwardSequence(
[lookup.apply]))
transitions = [SimpleRecurrent(dim=state_dim, activation=Tanh())
for _ in range(layers)]
rnn = RecurrentStack(transitions, skip_connections=skip_connections)
# If skip_connections: dim = layers * state_dim
# else: dim = state_dim
output_layer = Linear(
input_dim=skip_connections * layers *
state_dim + (1 - skip_connections) * state_dim,
output_dim=vocab_size, name="output_layer")
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn = fork.apply(x)
# Give a name to the input of each layer
if skip_connections:
for t in range(len(pre_rnn)):
pre_rnn[t].name = "pre_rnn_" + str(t)
else:
pre_rnn.name = "pre_rnn"
# Prepare inputs for the RNN
kwargs = OrderedDict()
init_states = {}
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if skip_connections:
kwargs['inputs' + suffix] = pre_rnn[d]
elif d == 0:
kwargs['inputs'] = pre_rnn
init_states[d] = theano.shared(
numpy.zeros((args.mini_batch_size, state_dim)).astype(floatX),
name='state0_%d' % d)
kwargs['states' + suffix] = init_states[d]
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, **kwargs)
# We have
# h = [state, state_1, state_2 ...] if layers > 1
# h = state if layers == 1
# If we have skip connections, concatenate all the states
# Else only consider the state of the highest layer
last_states = {}
if layers > 1:
# Save all the last states
for d in range(layers):
last_states[d] = h[d][-1, :, :]
if skip_connections:
h = tensor.concatenate(h, axis=2)
else:
h = h[-1]
else:
last_states[0] = h[-1, :, :]
h.name = "hidden_state"
# The updates of the hidden states
updates = []
for d in range(layers):
updates.append((init_states[d], last_states[d]))
presoft = output_layer.apply(h[context:, :, :])
# Define the cost
# Compute the probability distribution
time, batch, feat = presoft.shape
presoft.name = 'presoft'
cross_entropy = Softmax().categorical_cross_entropy(
y[context:, :].flatten(),
presoft.reshape((batch * time, feat)))
cross_entropy = cross_entropy / tensor.log(2)
cross_entropy.name = "cross_entropy"
# TODO: add regularisation for the cost
# the log(1) is here in order to differentiate the two variables
# for monitoring
cost = cross_entropy + tensor.log(1)
cost.name = "regularized_cost"
# Initialize the model
logger.info('Initializing...')
fork.initialize()
rnn.weights_init = initialization.Orthogonal()
rnn.biases_init = initialization.Constant(0)
rnn.initialize()
output_layer.weights_init = initialization.IsotropicGaussian(0.1)
output_layer.biases_init = initialization.Constant(0)
output_layer.initialize()
return cost, cross_entropy, updates
def build_model_lstm(vocab_size, args, dtype=floatX):
logger.info('Building model ...')
# Parameters for the model
context = args.context
state_dim = args.state_dim
layers = args.layers
skip_connections = args.skip_connections
virtual_dim = 4 * state_dim
# Symbolic variables
# In both cases: Time X Batch
x = tensor.lmatrix('features')
y = tensor.lmatrix('targets')
# Build the model
output_names = []
output_dims = []
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
output_names.append("inputs" + suffix)
output_dims.append(virtual_dim)
lookup = LookupTable(length=vocab_size, dim=virtual_dim)
lookup.weights_init = initialization.IsotropicGaussian(0.1)
lookup.biases_init = initialization.Constant(0)
# Make sure time_length is what we need
fork = Fork(output_names=output_names,
input_dim=args.mini_batch_size,
output_dims=output_dims,
prototype=FeedforwardSequence([lookup.apply]))
transitions = [
LSTM(dim=state_dim, activation=Tanh()) for _ in range(layers)
]
rnn = RecurrentStack(transitions, skip_connections=skip_connections)
# If skip_connections: dim = layers * state_dim
# else: dim = state_dim
output_layer = Linear(input_dim=skip_connections * layers * state_dim +
(1 - skip_connections) * state_dim,
output_dim=vocab_size,
name="output_layer")
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn = fork.apply(x)
# Give a name to the input of each layer
if skip_connections:
for t in range(len(pre_rnn)):
pre_rnn[t].name = "pre_rnn_" + str(t)
else:
pre_rnn.name = "pre_rnn"
# Prepare inputs for the RNN
kwargs = OrderedDict()
init_states = {}
init_cells = {}
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if skip_connections:
kwargs['inputs' + suffix] = pre_rnn[d]
elif d == 0:
kwargs['inputs'] = pre_rnn
init_states[d] = theano.shared(numpy.zeros(
(args.mini_batch_size, state_dim)).astype(floatX),
name='state0_%d' % d)
init_cells[d] = theano.shared(numpy.zeros(
(args.mini_batch_size, state_dim)).astype(floatX),
name='cell0_%d' % d)
kwargs['states' + suffix] = init_states[d]
kwargs['cells' + suffix] = init_cells[d]
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, **kwargs)
# h = [state, cell, in, forget, out, state_1,
# cell_1, in_1, forget_1, out_1 ...]
last_states = {}
last_cells = {}
for d in range(layers):
last_states[d] = h[5 * d][-1, :, :]
last_cells[d] = h[5 * d + 1][-1, :, :]
# The updates of the hidden states
updates = []
for d in range(layers):
updates.append((init_states[d], last_states[d]))
updates.append((init_cells[d], last_states[d]))
# h = [state, cell, in, forget, out, state_1,
# cell_1, in_1, forget_1, out_1 ...]
# Extract the values
in_gates = h[2::5]
forget_gates = h[3::5]
out_gates = h[4::5]
gate_values = {
"in_gates": in_gates,
"forget_gates": forget_gates,
"out_gates": out_gates
}
h = h[::5]
# Now we have correctly:
# h = [state, state_1, state_2 ...] if layers > 1
# h = [state] if layers == 1
# If we have skip connections, concatenate all the states
# Else only consider the state of the highest layer
if layers > 1:
if skip_connections:
h = tensor.concatenate(h, axis=2)
else:
h = h[-1]
else:
h = h[0]
h.name = "hidden_state"
presoft = output_layer.apply(h[context:, :, :])
# Define the cost
# Compute the probability distribution
time, batch, feat = presoft.shape
presoft.name = 'presoft'
cross_entropy = Softmax().categorical_cross_entropy(
y[context:, :].flatten(), presoft.reshape((batch * time, feat)))
cross_entropy = cross_entropy / tensor.log(2)
cross_entropy.name = "cross_entropy"
# TODO: add regularisation for the cost
# the log(1) is here in order to differentiate the two variables
# for monitoring
cost = cross_entropy + tensor.log(1)
cost.name = "regularized_cost"
# Initialize the model
logger.info('Initializing...')
fork.initialize()
# Dont initialize as Orthogonal if we are about to load new parameters
if args.load_path is not None:
rnn.weights_init = initialization.Constant(0)
else:
rnn.weights_init = initialization.Orthogonal()
rnn.biases_init = initialization.Constant(0)
rnn.initialize()
output_layer.weights_init = initialization.IsotropicGaussian(0.1)
output_layer.biases_init = initialization.Constant(0)
output_layer.initialize()
return cost, cross_entropy, updates, gate_values
class TargetWordEncoder(Initializable):
"""Word encoder in target side use a single RNN to map a charater-level word to a vector"""
def __init__(self, vocab_size, embedding_dim, dgru_state_dim, dgru_depth,
**kwargs):
super(TargetWordEncoder, self).__init__(**kwargs)
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.dgru_state_dim = dgru_state_dim
self.embedding_dim = embedding_dim
self.lookup = LookupTable(name='embeddings')
self.dgru_depth = dgru_depth
self.dgru = RecurrentStack([
DGRU(activation=Tanh(), dim=self.dgru_state_dim)
for _ in range(dgru_depth)
],
skip_connections=True)
self.gru_fork = Fork(
[name for name in self.dgru.apply.sequences if name != 'mask'],
prototype=Linear(),
name='gru_fork')
self.children = [self.lookup, self.dgru, self.gru_fork]
def _push_allocation_config(self):
self.lookup.length = self.vocab_size
self.lookup.dim = self.embedding_dim
self.gru_fork.input_dim = self.embedding_dim
self.gru_fork.output_dims = [
self.dgru.get_dim(name) for name in self.gru_fork.output_names
]
@application(inputs=['char_seq', 'sample_matrix', 'char_aux'],
outputs=['representation'])
def apply(self, char_seq, sample_matrix, char_aux):
# Time as first dimension
embeddings = self.lookup.apply(char_seq)
gru_out = self.dgru.apply(**merge(
self.gru_fork.apply(embeddings, as_dict=True), {'mask': char_aux}))
if self.dgru_depth > 1:
gru_out = gru_out[-1]
sampled_representation = tensor.batched_dot(
sample_matrix, gru_out.dimshuffle([1, 0, 2]))
return sampled_representation.dimshuffle([1, 0, 2])
@application(inputs=['target_single_char'])
def single_emit(self, target_single_char, batch_size, mask, states=None):
# Time as first dimension
# only one batch
embeddings = self.lookup.apply(target_single_char)
if states is None:
states = self.dgru.initial_states(batch_size)
states_dict = {'states': states[0]}
for i in range(1, self.dgru_depth):
states_dict['states' + RECURRENTSTACK_SEPARATOR +
str(i)] = states[i]
gru_out = self.dgru.apply(**merge(
self.gru_fork.apply(embeddings, as_dict=True), states_dict, {
'mask': mask,
'iterate': False
}))
return gru_out
@single_emit.property('outputs')
def single_emit_outputs(self):
return [
'gru_out' + RECURRENTSTACK_SEPARATOR + str(i)
for i in range(self.dgru_depth)
]
def get_dim(self, name):
if name in ['output', 'feedback']:
return self.dgru_state_dim
super(TargetWordEncoder, self).get_dim(name)
dims_x = [frame_size] + [hidden_size_mlp_x]*(depth_x-1) + \
[4*hidden_size_recurrent]
activations_theta = [Rectifier()]*depth_theta
dims_theta = [hidden_size_recurrent] + \
[hidden_size_mlp_theta]*depth_theta
mlp_x = MLP(activations = activations_x,
dims = dims_x,
name = "mlp_x")
transition = [LSTM(dim=hidden_size_recurrent,
name = "lstm_{}".format(i) ) for i in range(depth_lstm)]
transition = RecurrentStack( transition,
name="transition", skip_connections = True)
mlp_theta = MLP( activations = activations_theta,
dims = dims_theta,
name = "mlp_theta")
mlp_gmm = GMMMLP(mlp = mlp_theta,
dim = target_size,
k = k,
const = 0.00001,
name = "gmm_wrap")
gmm_emitter = GMMEmitter(gmmmlp = mlp_gmm, output_size = frame_size, k = k)
bricks = [mlp_x, transition, gmm_emitter]
class Interpolator(AbstractReadout):
"""Readout char by char."""
def __init__(self,
vocab_size,
embedding_dim,
igru_state_dim,
igru_depth,
trg_dgru_depth,
emitter,
feedback_brick,
merge=None,
merge_prototype=None,
post_merge=None,
**kwargs):
merged_dim = igru_state_dim
if not merge:
merge = Merge(input_names=kwargs['source_names'],
prototype=merge_prototype)
if not post_merge:
post_merge = Bias(dim=merged_dim)
# for compatible
if igru_depth == 1:
self.igru = IGRU(dim=igru_state_dim)
else:
self.igru = RecurrentStack(
[IGRU(dim=igru_state_dim, name='igru')] + [
UpperIGRU(dim=igru_state_dim,
activation=Tanh(),
name='upper_igru' + str(i))
for i in range(1, igru_depth)
],
skip_connections=True)
self.embedding_dim = embedding_dim
self.emitter = emitter
self.feedback_brick = feedback_brick
self.merge = merge
self.post_merge = post_merge
self.merged_dim = merged_dim
self.igru_depth = igru_depth
self.trg_dgru_depth = trg_dgru_depth
self.lookup = LookupTable(name='embeddings')
self.vocab_size = vocab_size
self.igru_state_dim = igru_state_dim
self.gru_to_softmax = Linear(input_dim=igru_state_dim,
output_dim=vocab_size)
self.gru_fork = Fork([
name for name in self.igru.apply.sequences
if name != 'mask' and name != 'input_states'
],
prototype=Linear(),
name='gru_fork')
children = [
self.emitter, self.feedback_brick, self.merge, self.post_merge,
self.igru, self.lookup, self.gru_to_softmax, self.gru_fork
]
kwargs.setdefault('children', []).extend(children)
super(Interpolator, self).__init__(**kwargs)
def _push_allocation_config(self):
self.lookup.length = self.vocab_size
self.lookup.dim = self.embedding_dim
self.emitter.readout_dim = self.get_dim('readouts')
self.merge.input_names = self.source_names
self.merge.input_dims = self.source_dims
self.merge.output_dim = self.merged_dim
self.post_merge.input_dim = self.merged_dim
self.post_merge.output_dim = self.igru_state_dim
self.gru_fork.input_dim = self.embedding_dim
self.gru_fork.output_dims = [
self.igru.get_dim(name) for name in self.gru_fork.output_names
]
@application
def initial_igru_outputs(self, batch_size):
return self.igru.initial_states(batch_size)
@application
def emit(self, readouts):
return self.emitter.emit(readouts)
@application
def cost(self, readouts, outputs):
return self.emitter.cost(readouts, outputs)
@application
def initial_outputs(self, batch_size):
return self.emitter.initial_outputs(batch_size)
@application(outputs=['feedback'])
def feedback(self, outputs):
return self.feedback_brick.feedback(outputs)
@application(outputs=['feedback'])
def feedback_apply(self, target_char_seq, target_sample_matrix,
target_char_aux):
return self.feedback_brick.apply(target_char_seq, target_sample_matrix,
target_char_aux)
@application
def single_feedback(self,
target_single_char,
batch_size,
mask=None,
states=None):
return self.feedback_brick.single_emit(target_single_char, batch_size,
mask, states)
@single_feedback.property('outputs')
def single_feedback_outputs(self):
return [
'single_feedback' + RECURRENTSTACK_SEPARATOR + str(i)
for i in range(self.trg_dgru_depth)
]
@application(outputs=['gru_out', 'readout_chars'])
def single_readout_gru(self, target_prev_char, target_prev_char_aux,
input_states, states):
embeddings = self.lookup.apply(target_prev_char)
states_dict = {'states': states[0]}
if self.igru_depth > 1:
for i in range(1, self.igru_depth):
states_dict['states' + RECURRENTSTACK_SEPARATOR +
str(i)] = states[i]
gru_out = self.igru.apply(**merge(
self.gru_fork.apply(embeddings, as_dict=True), states_dict, {
'mask': target_prev_char_aux,
'input_states': input_states,
'iterate': False
}))
if self.igru_depth > 1:
readout_chars = self.gru_to_softmax.apply(gru_out[-1])
else:
readout_chars = self.gru_to_softmax.apply(gru_out)
return gru_out, readout_chars
@application
def readout(self, **kwargs):
merged = self.merge.apply(
**{name: kwargs[name]
for name in self.merge.input_names})
merged = self.post_merge.apply(merged)
return merged
@application(outputs=['readout_chars'])
def readout_gru(self, target_prev_char_seq, target_prev_char_aux,
input_states):
embeddings = self.lookup.apply(target_prev_char_seq)
gru_out = self.igru.apply(
**merge(self.gru_fork.apply(embeddings, as_dict=True), {
'mask': target_prev_char_aux,
'input_states': input_states
}))
if self.igru_depth > 1:
gru_out = gru_out[-1]
readout_chars = self.gru_to_softmax.apply(gru_out)
return readout_chars
def get_dim(self, name):
if name == 'outputs':
return self.emitter.get_dim(name)
elif name == 'feedback':
return self.feedback_brick.get_dim(name)
elif name == 'readouts':
return self.readout_dim
return super(AbstractReadout, self).get_dim(name)
class TestRecurrentStack(unittest.TestCase):
def setUp(self):
depth = 4
self.depth = depth
dim = 3 # don't change, hardwired in the code
transitions = [LSTM(dim=dim) for _ in range(depth)]
self.stack0 = RecurrentStack(transitions,
weights_init=Constant(2),
biases_init=Constant(0))
self.stack0.initialize()
self.stack2 = RecurrentStack(transitions,
weights_init=Constant(2),
biases_init=Constant(0),
skip_connections=True)
self.stack2.initialize()
def do_one_step(self, stack, skip_connections=False, low_memory=False):
depth = self.depth
# batch=2
h0_val = 0.1 * numpy.array([[[1, 1, 0], [0, 1, 1]]] * depth,
dtype=theano.config.floatX)
c0_val = 0.1 * numpy.array([[[1, 1, 0], [0, 1, 1]]] * depth,
dtype=theano.config.floatX)
x_val = 0.1 * numpy.array([range(12), range(12, 24)],
dtype=theano.config.floatX)
# we will use same weights on all layers
W_state2x_val = 2 * numpy.ones((3, 12), dtype=theano.config.floatX)
W_state_val = 2 * numpy.ones((3, 12), dtype=theano.config.floatX)
W_cell_to_in = 2 * numpy.ones((3,), dtype=theano.config.floatX)
W_cell_to_out = 2 * numpy.ones((3,), dtype=theano.config.floatX)
W_cell_to_forget = 2 * numpy.ones((3,), dtype=theano.config.floatX)
kwargs = OrderedDict()
for d in range(depth):
if d > 0:
suffix = RECURRENTSTACK_SEPARATOR + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
kwargs['inputs' + suffix] = tensor.matrix('inputs' + suffix)
kwargs['inputs' + suffix].tag.test_value = x_val
kwargs['states' + suffix] = tensor.matrix('states' + suffix)
kwargs['states' + suffix].tag.test_value = h0_val[d]
kwargs['cells' + suffix] = tensor.matrix('cells' + suffix)
kwargs['cells' + suffix].tag.test_value = c0_val[d]
results = stack.apply(iterate=False, low_memory=low_memory, **kwargs)
next_h = theano.function(inputs=list(kwargs.values()),
outputs=results)
def sigmoid(x):
return 1. / (1. + numpy.exp(-x))
h1_val = []
x_v = x_val
args_val = []
for d in range(depth):
if d == 0 or skip_connections:
args_val.append(x_val)
h0_v = h0_val[d]
args_val.append(h0_v)
c0_v = c0_val[d]
args_val.append(c0_v)
# omitting biases because they are zero
activation = numpy.dot(h0_v, W_state_val) + x_v
if skip_connections and d > 0:
activation += x_val
i_t = sigmoid(activation[:, :3] + c0_v * W_cell_to_in)
f_t = sigmoid(activation[:, 3:6] + c0_v * W_cell_to_forget)
next_cells = f_t * c0_v + i_t * numpy.tanh(activation[:, 6:9])
o_t = sigmoid(activation[:, 9:12] +
next_cells * W_cell_to_out)
h1_v = o_t * numpy.tanh(next_cells)
# current layer output state transformed to input of next
x_v = numpy.dot(h1_v, W_state2x_val)
h1_val.append(h1_v)
res = next_h(*args_val)
for d in range(depth):
assert_allclose(h1_val[d], res[d * 2], rtol=1e-6)
def test_one_step(self):
self.do_one_step(self.stack0)
self.do_one_step(self.stack0, low_memory=True)
self.do_one_step(self.stack2, skip_connections=True)
self.do_one_step(self.stack2, skip_connections=True, low_memory=True)
def do_many_steps(self, stack, skip_connections=False, low_memory=False):
depth = self.depth
# 24 steps
# 4 batch examples
# 12 dimensions per step
x_val = (0.1 * numpy.asarray(
list(itertools.islice(itertools.permutations(range(12)), 0, 24)),
dtype=theano.config.floatX))
x_val = numpy.ones((24, 4, 12),
dtype=theano.config.floatX) * x_val[:, None, :]
# mask the last third of steps
mask_val = numpy.ones((24, 4), dtype=theano.config.floatX)
mask_val[12:24, 3] = 0
# unroll all states and cells for all steps and also initial value
h_val = numpy.zeros((depth, 25, 4, 3), dtype=theano.config.floatX)
c_val = numpy.zeros((depth, 25, 4, 3), dtype=theano.config.floatX)
# we will use same weights on all layers
W_state2x_val = 2 * numpy.ones((3, 12), dtype=theano.config.floatX)
W_state_val = 2 * numpy.ones((3, 12), dtype=theano.config.floatX)
W_cell_to_in = 2 * numpy.ones((3,), dtype=theano.config.floatX)
W_cell_to_out = 2 * numpy.ones((3,), dtype=theano.config.floatX)
W_cell_to_forget = 2 * numpy.ones((3,), dtype=theano.config.floatX)
kwargs = OrderedDict()
for d in range(depth):
if d > 0:
suffix = RECURRENTSTACK_SEPARATOR + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
kwargs['inputs' + suffix] = tensor.tensor3('inputs' + suffix)
kwargs['inputs' + suffix].tag.test_value = x_val
kwargs['mask'] = tensor.matrix('mask')
kwargs['mask'].tag.test_value = mask_val
results = stack.apply(iterate=True, low_memory=low_memory, **kwargs)
calc_h = theano.function(inputs=list(kwargs.values()),
outputs=results)
def sigmoid(x):
return 1. / (1. + numpy.exp(-x))
for i in range(1, 25):
x_v = x_val[i - 1]
h_vs = []
c_vs = []
for d in range(depth):
h_v = h_val[d][i - 1, :, :]
c_v = c_val[d][i - 1, :, :]
activation = numpy.dot(h_v, W_state_val) + x_v
if skip_connections and d > 0:
activation += x_val[i - 1]
i_t = sigmoid(activation[:, :3] + c_v * W_cell_to_in)
f_t = sigmoid(activation[:, 3:6] + c_v * W_cell_to_forget)
c_v1 = f_t * c_v + i_t * numpy.tanh(activation[:, 6:9])
o_t = sigmoid(activation[:, 9:12] +
c_v1 * W_cell_to_out)
h_v1 = o_t * numpy.tanh(c_v1)
h_v = (mask_val[i - 1, :, None] * h_v1 +
(1 - mask_val[i - 1, :, None]) * h_v)
c_v = (mask_val[i - 1, :, None] * c_v1 +
(1 - mask_val[i - 1, :, None]) * c_v)
# current layer output state transformed to input of next
x_v = numpy.dot(h_v, W_state2x_val)
h_vs.append(h_v)
c_vs.append(c_v)
for d in range(depth):
h_val[d][i, :, :] = h_vs[d]
c_val[d][i, :, :] = c_vs[d]
args_val = [x_val]*(depth if skip_connections else 1) + [mask_val]
res = calc_h(*args_val)
for d in range(depth):
assert_allclose(h_val[d][1:], res[d * 2], rtol=1e-4)
assert_allclose(c_val[d][1:], res[d * 2 + 1], rtol=1e-4)
# Also test that initial state is a parameter
for h in results:
initial_states = VariableFilter(roles=[INITIAL_STATE])(
ComputationGraph(h))
assert all(is_shared_variable(initial_state)
for initial_state in initial_states)
def test_many_steps(self):
self.do_many_steps(self.stack0)
self.do_many_steps(self.stack0, low_memory=True)
self.do_many_steps(self.stack2, skip_connections=True)
self.do_many_steps(self.stack2, skip_connections=True, low_memory=True)
def __init__(self,
vocab_size,
embedding_dim,
dgru_state_dim,
igru_state_dim,
state_dim,
representation_dim,
transition_depth,
trg_igru_depth,
trg_dgru_depth,
trg_space_idx,
trg_bos,
theano_seed=None,
**kwargs):
super(Decoder, self).__init__(**kwargs)
self.vocab_size = vocab_size
self.embedding_dim = embedding_dim
self.dgru_state_dim = dgru_state_dim
self.igru_state_dim = igru_state_dim
self.state_dim = state_dim
self.trg_space_idx = trg_space_idx
self.representation_dim = representation_dim
self.theano_seed = theano_seed
# Initialize gru with special initial state
self.transition = RecurrentStack([
GRUInitialState(attended_dim=state_dim,
dim=state_dim,
activation=Tanh(),
name='decoder_gru_withinit')
] + [
GatedRecurrent(
dim=state_dim, activation=Tanh(), name='decoder_gru' + str(i))
for i in range(1, transition_depth)
],
skip_connections=False)
# Initialize the attention mechanism
self.attention = SequenceContentAttention(
state_names=self.transition.apply.states,
attended_dim=representation_dim,
match_dim=state_dim,
name="attention")
self.interpolator = Interpolator(
vocab_size=vocab_size,
embedding_dim=embedding_dim,
igru_state_dim=igru_state_dim,
igru_depth=trg_igru_depth,
trg_dgru_depth=trg_dgru_depth,
source_names=[
'states', 'feedback', self.attention.take_glimpses.outputs[0]
],
readout_dim=self.vocab_size,
emitter=SoftmaxEmitter(initial_output=trg_bos,
theano_seed=theano_seed),
feedback_brick=TargetWordEncoder(vocab_size, embedding_dim,
self.dgru_state_dim,
trg_dgru_depth))
# Build sequence generator accordingly
self.sequence_generator = SequenceGeneratorDCNMT(
trg_space_idx=self.trg_space_idx,
readout=self.interpolator,
transition=self.transition,
attention=self.attention,
transition_depth=transition_depth,
igru_depth=trg_igru_depth,
trg_dgru_depth=trg_dgru_depth,
fork=Fork([
name
for name in self.transition.apply.sequences if name != 'mask'
],
prototype=Linear()))
self.children = [self.sequence_generator]
activations_theta = [Rectifier()] * depth_theta
dims_theta = [hidden_size_recurrent] + \
[hidden_size_mlp_theta]*depth_theta
mlp_x = MLP(activations=activations_x, dims=dims_x)
feedback = DeepTransitionFeedback(mlp=mlp_x)
transition = [
GatedRecurrent(dim=hidden_size_recurrent, name="gru_{}".format(i))
for i in range(depth_recurrent)
]
transition = RecurrentStack(transition,
name="transition",
skip_connections=True)
mlp_theta = MLP(activations=activations_theta, dims=dims_theta)
mlp_gmm = GMMMLP(mlp=mlp_theta, dim=target_size, k=k, const=0.00001)
emitter = GMMEmitter(gmmmlp=mlp_gmm,
output_size=frame_size,
k=k,
name="emitter")
source_names = [name for name in transition.apply.states if 'states' in name]
readout = Readout(readout_dim=hidden_size_recurrent,
source_names=source_names,
emitter=emitter,
def __init__(
self,
recordings_source,
labels_source,
eos_label,
num_features,
num_phonemes,
dim_dec,
dims_bidir,
dims_bottom,
enc_transition,
dec_transition,
use_states_for_readout,
attention_type,
lm=None,
character_map=None,
subsample=None,
dims_top=None,
prior=None,
conv_n=None,
bottom_activation=None,
post_merge_activation=None,
post_merge_dims=None,
dim_matcher=None,
embed_outputs=True,
dec_stack=1,
conv_num_filters=1,
data_prepend_eos=True,
energy_normalizer=None, # softmax is th edefault set in SequenceContentAndConvAttention
**kwargs):
if bottom_activation is None:
bottom_activation = Tanh()
if post_merge_activation is None:
post_merge_activation = Tanh()
super(SpeechRecognizer, self).__init__(**kwargs)
self.recordings_source = recordings_source
self.labels_source = labels_source
self.eos_label = eos_label
self.data_prepend_eos = data_prepend_eos
self.rec_weights_init = None
self.initial_states_init = None
self.enc_transition = enc_transition
self.dec_transition = dec_transition
self.dec_stack = dec_stack
bottom_activation = bottom_activation
post_merge_activation = post_merge_activation
if dim_matcher is None:
dim_matcher = dim_dec
# The bottom part, before BiRNN
if dims_bottom:
bottom = MLP([bottom_activation] * len(dims_bottom),
[num_features] + dims_bottom,
name="bottom")
else:
bottom = Identity(name='bottom')
# BiRNN
if not subsample:
subsample = [1] * len(dims_bidir)
encoder = Encoder(
self.enc_transition, dims_bidir,
dims_bottom[-1] if len(dims_bottom) else num_features, subsample)
# The top part, on top of BiRNN but before the attention
if dims_top:
top = MLP([Tanh()],
[2 * dims_bidir[-1]] + dims_top + [2 * dims_bidir[-1]],
name="top")
else:
top = Identity(name='top')
if dec_stack == 1:
transition = self.dec_transition(dim=dim_dec,
activation=Tanh(),
name="transition")
else:
transitions = [
self.dec_transition(dim=dim_dec,
activation=Tanh(),
name="transition_{}".format(trans_level))
for trans_level in xrange(dec_stack)
]
transition = RecurrentStack(transitions=transitions,
skip_connections=True)
# Choose attention mechanism according to the configuration
if attention_type == "content":
attention = SequenceContentAttention(
state_names=transition.apply.states,
attended_dim=2 * dims_bidir[-1],
match_dim=dim_matcher,
name="cont_att")
elif attention_type == "content_and_conv":
attention = SequenceContentAndConvAttention(
state_names=transition.apply.states,
conv_n=conv_n,
conv_num_filters=conv_num_filters,
attended_dim=2 * dims_bidir[-1],
match_dim=dim_matcher,
prior=prior,
energy_normalizer=energy_normalizer,
name="conv_att")
else:
raise ValueError(
"Unknown attention type {}".format(attention_type))
if embed_outputs:
feedback = LookupFeedback(num_phonemes + 1, dim_dec)
else:
feedback = OneOfNFeedback(num_phonemes + 1)
if lm:
# In case we use LM it is Readout that is responsible
# for normalization.
emitter = LMEmitter()
else:
emitter = SoftmaxEmitter(initial_output=num_phonemes,
name="emitter")
readout_config = dict(readout_dim=num_phonemes,
source_names=(transition.apply.states if
use_states_for_readout else []) +
[attention.take_glimpses.outputs[0]],
emitter=emitter,
feedback_brick=feedback,
name="readout")
if post_merge_dims:
readout_config['merged_dim'] = post_merge_dims[0]
readout_config['post_merge'] = InitializableSequence(
[
Bias(post_merge_dims[0]).apply,
post_merge_activation.apply,
MLP(
[post_merge_activation] *
(len(post_merge_dims) - 1) + [Identity()],
# MLP was designed to support Maxout is activation
# (because Maxout in a way is not one). However
# a single layer Maxout network works with the trick below.
# For deeper Maxout network one has to use the
# Sequence brick.
[
d //
getattr(post_merge_activation, 'num_pieces', 1)
for d in post_merge_dims
] + [num_phonemes]).apply,
],
name='post_merge')
readout = Readout(**readout_config)
language_model = None
if lm:
lm_weight = lm.pop('weight', 0.0)
normalize_am_weights = lm.pop('normalize_am_weights', True)
normalize_lm_weights = lm.pop('normalize_lm_weights', False)
normalize_tot_weights = lm.pop('normalize_tot_weights', False)
am_beta = lm.pop('am_beta', 1.0)
if normalize_am_weights + normalize_lm_weights + normalize_tot_weights < 1:
logger.warn(
"Beam search is prone to fail with no log-prob normalization"
)
language_model = LanguageModel(nn_char_map=character_map, **lm)
readout = ShallowFusionReadout(
lm_costs_name='lm_add',
lm_weight=lm_weight,
normalize_am_weights=normalize_am_weights,
normalize_lm_weights=normalize_lm_weights,
normalize_tot_weights=normalize_tot_weights,
am_beta=am_beta,
**readout_config)
generator = SequenceGenerator(readout=readout,
transition=transition,
attention=attention,
language_model=language_model,
name="generator")
# Remember child bricks
self.encoder = encoder
self.bottom = bottom
self.top = top
self.generator = generator
self.children = [encoder, top, bottom, generator]
# Create input variables
self.recordings = tensor.tensor3(self.recordings_source)
self.recordings_mask = tensor.matrix(self.recordings_source + "_mask")
self.labels = tensor.lmatrix(self.labels_source)
self.labels_mask = tensor.matrix(self.labels_source + "_mask")
self.batch_inputs = [
self.recordings, self.recordings_source, self.labels,
self.labels_mask
]
self.single_recording = tensor.matrix(self.recordings_source)
self.single_transcription = tensor.lvector(self.labels_source)
def main_rnn(config):
x = tensor.tensor3('features')
y = tensor.matrix('targets')
# if 'LSTM' in config['model'] :
# from models import getLSTMstack
# y_hat = getLSTMstack(input_dim=13, input_var=x, depth=int(config['model'][-1]))
# else :
# raise Exception("These are not the LSTM we are looking for")
# y_hat = model.apply(x)
emitter = TestEmitter()
# emitter = TrivialEmitter(readout_dim=config['lstm_hidden_size'])
# cost_func = SquaredError()
# @application
# def qwe(self, readouts, outputs=None):
# print(type(self), type(readouts))
# x = cost_func.apply(readouts,outputs)
# return x
print(type(emitter.cost))
# emitter.cost = qwe
# print(type(qwe))
steps = 2
n_samples= config['target_size']
transition = [LSTM(config['lstm_hidden_size']) for _ in range(4)]
transition = RecurrentStack(transition,
name="transition", skip_connections=False)
source_names = [name for name in transition.apply.states if 'states' in name]
readout = Readout(emitter, readout_dim=config['lstm_hidden_size'], source_names=source_names,feedback_brick=None, merge=None, merge_prototype=None, post_merge=None, merged_dim=None)
seqgen = SequenceGenerator(readout, transition, attention=None, add_contexts=False)
seqgen.weights_init = IsotropicGaussian(0.01)
seqgen.biases_init = Constant(0.)
seqgen.push_initialization_config()
seqgen.transition.biases_init = IsotropicGaussian(0.01,1)
seqgen.transition.push_initialization_config()
seqgen.initialize()
states = seqgen.transition.apply.outputs
print('states',states)
states = {name: shared_floatx_zeros((n_samples, config['lstm_hidden_size']))
for name in states}
cost_matrix = seqgen.cost_matrix(x, **states)
cost = cost_matrix.mean()
cost.name = "nll"
cg = ComputationGraph(cost)
model = Model(cost)
#Cost
# cost = SquaredError().apply(y_hat ,y)
#cost = CategoricalCrossEntropy().apply(T.flatten(),Y)
#
#for sampling
#cg = ComputationGraph(seqgen.generate(n_steps=steps,batch_size=n_samples, iterate=True))
algorithm = GradientDescent(
cost=cost, parameters=cg.parameters,
step_rule=Scale(learning_rate=config['learning_rate']))
#Getting the stream
train_stream = MFCC.get_stream(config['batch_size'],config['source_size'],config['target_size'],config['num_examples'])
#Monitoring stuff
extensions = [Timing(),
FinishAfter(after_n_batches=config['num_batches']),
#DataStreamMonitoring([cost, error_rate],test_stream,prefix="test"),
TrainingDataMonitoring([cost], prefix="train", every_n_batches=1),
#Checkpoint(save_to),
ProgressBar(),
Printing(every_n_batches=1)]
main_loop = MainLoop(
algorithm,
train_stream,
# model=model,
extensions=extensions)
main_loop.run()
def build_model_hard(vocab_size, args, dtype=floatX):
logger.info('Building model ...')
# Parameters for the model
context = args.context
state_dim = args.state_dim
layers = args.layers
skip_connections = args.skip_connections
# Symbolic variables
# In both cases: Time X Batch
x = tensor.lmatrix('features')
y = tensor.lmatrix('targets')
# Build the model
output_names = []
output_dims = []
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
output_names.append("inputs" + suffix)
output_dims.append(state_dim)
lookup = LookupTable(length=vocab_size, dim=state_dim)
lookup.weights_init = initialization.IsotropicGaussian(0.1)
lookup.biases_init = initialization.Constant(0)
fork = Fork(output_names=output_names,
input_dim=args.mini_batch_size,
output_dims=output_dims,
prototype=FeedforwardSequence([lookup.apply]))
transitions = [SimpleRecurrent(dim=state_dim, activation=Tanh())]
for i in range(layers - 1):
mlp = MLP(activations=[Logistic()],
dims=[2 * state_dim, 1],
weights_init=initialization.IsotropicGaussian(0.1),
biases_init=initialization.Constant(0),
name="mlp_" + str(i))
transitions.append(
HardGatedRecurrent(dim=state_dim, mlp=mlp, activation=Tanh()))
rnn = RecurrentStack(transitions, skip_connections=skip_connections)
# dim = layers * state_dim
output_layer = Linear(input_dim=layers * state_dim,
output_dim=vocab_size,
name="output_layer")
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn = fork.apply(x)
# Give a name to the input of each layer
if skip_connections:
for t in range(len(pre_rnn)):
pre_rnn[t].name = "pre_rnn_" + str(t)
else:
pre_rnn.name = "pre_rnn"
# Prepare inputs for the RNN
kwargs = OrderedDict()
init_states = {}
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if skip_connections:
kwargs['inputs' + suffix] = pre_rnn[d]
elif d == 0:
kwargs['inputs' + suffix] = pre_rnn
init_states[d] = theano.shared(numpy.zeros(
(args.mini_batch_size, state_dim)).astype(floatX),
name='state0_%d' % d)
kwargs['states' + suffix] = init_states[d]
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, **kwargs)
# Now we have correctly:
# h = [state_1, state_2, state_3 ...]
# Save all the last states
last_states = {}
for d in range(layers):
last_states[d] = h[d][-1, :, :]
# Concatenate all the states
if layers > 1:
h = tensor.concatenate(h, axis=2)
h.name = "hidden_state"
# The updates of the hidden states
updates = []
for d in range(layers):
updates.append((init_states[d], last_states[d]))
presoft = output_layer.apply(h[context:, :, :])
# Define the cost
# Compute the probability distribution
time, batch, feat = presoft.shape
presoft.name = 'presoft'
cross_entropy = Softmax().categorical_cross_entropy(
y[context:, :].flatten(), presoft.reshape((batch * time, feat)))
cross_entropy = cross_entropy / tensor.log(2)
cross_entropy.name = "cross_entropy"
# TODO: add regularisation for the cost
# the log(1) is here in order to differentiate the two variables
# for monitoring
cost = cross_entropy + tensor.log(1)
cost.name = "regularized_cost"
# Initialize the model
logger.info('Initializing...')
fork.initialize()
rnn.weights_init = initialization.Orthogonal()
rnn.biases_init = initialization.Constant(0)
rnn.initialize()
output_layer.weights_init = initialization.IsotropicGaussian(0.1)
output_layer.biases_init = initialization.Constant(0)
output_layer.initialize()
return cost, cross_entropy, updates
bos_token = None,
eos_token = None,
unk_token = '<UNK>',
level = 'character')
alphabet_size = len(dictionary.keys())
lstm_dim = 512
lstm1 = LSTM(dim=lstm_dim, use_bias=False,
weights_init=Orthogonal())
lstm2 = LSTM(dim=lstm_dim, use_bias=False,
weights_init=Orthogonal())
lstm3 = LSTM(dim=lstm_dim, use_bias=False,
weights_init=Orthogonal())
rnn = RecurrentStack([lstm1, lstm2, lstm3],
name="transition")
readout = Readout(readout_dim = alphabet_size,
source_names=["states#2"],
emitter=SoftmaxEmitter(name="emitter"),
feedback_brick=LookupFeedback(alphabet_size,
feedback_dim=alphabet_size,
name="feedback"),
name="readout")
seq_gen = SequenceGenerator(readout=readout,
transition=rnn,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
name="generator")
def build_model_soft(vocab_size, args, dtype=floatX):
logger.info('Building model ...')
# Parameters for the model
context = args.context
state_dim = args.state_dim
layers = args.layers
skip_connections = args.skip_connections
# Symbolic variables
# In both cases: Time X Batch
x = tensor.lmatrix('features')
y = tensor.lmatrix('targets')
# Build the model
output_names = []
output_dims = []
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if d == 0 or skip_connections:
output_names.append("inputs" + suffix)
output_dims.append(state_dim)
lookup = LookupTable(length=vocab_size, dim=state_dim)
lookup.weights_init = initialization.IsotropicGaussian(0.1)
lookup.biases_init = initialization.Constant(0)
fork = Fork(output_names=output_names, input_dim=args.mini_batch_size,
output_dims=output_dims,
prototype=FeedforwardSequence(
[lookup.apply]))
transitions = [SimpleRecurrent(dim=state_dim, activation=Tanh())]
# Build the MLP
dims = [2 * state_dim]
activations = []
for i in range(args.mlp_layers):
activations.append(Rectifier())
dims.append(state_dim)
# Activation of the last layer of the MLP
if args.mlp_activation == "logistic":
activations.append(Logistic())
elif args.mlp_activation == "rectifier":
activations.append(Rectifier())
elif args.mlp_activation == "hard_logistic":
activations.append(HardLogistic())
else:
assert False
# Output of MLP has dimension 1
dims.append(1)
for i in range(layers - 1):
mlp = MLP(activations=activations, dims=dims,
weights_init=initialization.IsotropicGaussian(0.1),
biases_init=initialization.Constant(0),
name="mlp_" + str(i))
transitions.append(
SoftGatedRecurrent(dim=state_dim,
mlp=mlp,
activation=Tanh()))
rnn = RecurrentStack(transitions, skip_connections=skip_connections)
# dim = layers * state_dim
output_layer = Linear(
input_dim=layers * state_dim,
output_dim=vocab_size, name="output_layer")
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn = fork.apply(x)
# Give a name to the input of each layer
if skip_connections:
for t in range(len(pre_rnn)):
pre_rnn[t].name = "pre_rnn_" + str(t)
else:
pre_rnn.name = "pre_rnn"
# Prepare inputs for the RNN
kwargs = OrderedDict()
init_states = {}
for d in range(layers):
if d > 0:
suffix = '_' + str(d)
else:
suffix = ''
if skip_connections:
kwargs['inputs' + suffix] = pre_rnn[d]
elif d == 0:
kwargs['inputs' + suffix] = pre_rnn
init_states[d] = theano.shared(
numpy.zeros((args.mini_batch_size, state_dim)).astype(floatX),
name='state0_%d' % d)
kwargs['states' + suffix] = init_states[d]
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, **kwargs)
# Now we have:
# h = [state, state_1, gate_value_1, state_2, gate_value_2, state_3, ...]
# Extract gate_values
gate_values = h[2::2]
new_h = [h[0]]
new_h.extend(h[1::2])
h = new_h
# Now we have:
# h = [state, state_1, state_2, ...]
# gate_values = [gate_value_1, gate_value_2, gate_value_3]
for i, gate_value in enumerate(gate_values):
gate_value.name = "gate_value_" + str(i)
# Save all the last states
last_states = {}
for d in range(layers):
last_states[d] = h[d][-1, :, :]
# Concatenate all the states
if layers > 1:
h = tensor.concatenate(h, axis=2)
h.name = "hidden_state"
# The updates of the hidden states
updates = []
for d in range(layers):
updates.append((init_states[d], last_states[d]))
presoft = output_layer.apply(h[context:, :, :])
# Define the cost
# Compute the probability distribution
time, batch, feat = presoft.shape
presoft.name = 'presoft'
cross_entropy = Softmax().categorical_cross_entropy(
y[context:, :].flatten(),
presoft.reshape((batch * time, feat)))
cross_entropy = cross_entropy / tensor.log(2)
cross_entropy.name = "cross_entropy"
# TODO: add regularisation for the cost
# the log(1) is here in order to differentiate the two variables
# for monitoring
cost = cross_entropy + tensor.log(1)
cost.name = "regularized_cost"
# Initialize the model
logger.info('Initializing...')
fork.initialize()
rnn.weights_init = initialization.Orthogonal()
rnn.biases_init = initialization.Constant(0)
rnn.initialize()
output_layer.weights_init = initialization.IsotropicGaussian(0.1)
output_layer.biases_init = initialization.Constant(0)
output_layer.initialize()
return cost, cross_entropy, updates, gate_values
'c': 3,
'<UNK>': 4
},
bos_token=None,
eos_token=None,
unk_token='<UNK>',
level='character')
alphabet_size = 4
lstm_dim = 2
lstm1 = LSTM(dim=lstm_dim, use_bias=False, weights_init=Orthogonal())
lstm2 = LSTM(dim=lstm_dim, use_bias=False, weights_init=Orthogonal())
rnn = RecurrentStack([lstm1, lstm2], name="transition")
readout = Readout(readout_dim=alphabet_size,
source_names=["states"],
emitter=SoftmaxEmitter(name="emitter"),
feedback_brick=LookupFeedback(alphabet_size,
feedback_dim=alphabet_size,
name="feedback"),
name="readout")
seq_gen = SequenceGenerator(readout=readout,
transition=rnn,
weights_init=IsotropicGaussian(0.01),
biases_init=Constant(0),
name="generator")
[4*hidden_size_recurrent]
activations_theta = [Rectifier()] * depth_theta
dims_theta = [hidden_size_recurrent] + \
[hidden_size_mlp_theta]*depth_theta
mlp_x = MLP(activations=activations_x, dims=dims_x, name="mlp_x")
transition = [
LSTM(dim=hidden_size_recurrent, name="lstm_{}".format(i))
for i in range(depth_lstm)
]
transition = RecurrentStack(transition,
name="transition",
skip_connections=True)
mlp_theta = MLP(activations=activations_theta,
dims=dims_theta,
name="mlp_theta")
mlp_gmm = GMMMLP(mlp=mlp_theta,
dim=target_size,
k=k,
const=0.00001,
name="gmm_wrap")
gmm_emitter = GMMEmitter(gmmmlp=mlp_gmm, output_size=frame_size, k=k)
bricks = [mlp_x, transition, gmm_emitter]
def build_model_soft(args, dtype=floatX):
logger.info('Building model ...')
# Return list of 3D Tensor, one for each layer
# (Time X Batch X embedding_dim)
pre_rnn, x_mask = get_prernn(args)
transitions = [SimpleRecurrent(dim=args.state_dim, activation=Tanh())]
# Build the MLP
dims = [2 * args.state_dim]
activations = []
for i in range(args.mlp_layers):
activations.append(Rectifier())
dims.append(args.state_dim)
# Activation of the last layer of the MLP
if args.mlp_activation == "logistic":
activations.append(Logistic())
elif args.mlp_activation == "rectifier":
activations.append(Rectifier())
elif args.mlp_activation == "hard_logistic":
activations.append(HardLogistic())
else:
assert False
# Output of MLP has dimension 1
dims.append(1)
for i in range(args.layers - 1):
mlp = MLP(activations=activations, dims=dims,
weights_init=initialization.IsotropicGaussian(0.1),
biases_init=initialization.Constant(0),
name="mlp_" + str(i))
transitions.append(
SoftGatedRecurrent(dim=args.state_dim,
mlp=mlp,
activation=Tanh()))
rnn = RecurrentStack(transitions, skip_connections=args.skip_connections)
initialize_rnn(rnn, args)
# Prepare inputs and initial states for the RNN
kwargs, inits = get_rnn_kwargs(pre_rnn, args)
# Apply the RNN to the inputs
h = rnn.apply(low_memory=True, mask=x_mask, **kwargs)
# Now we have:
# h = [state, state_1, gate_value_1, state_2, gate_value_2, state_3, ...]
# Extract gate_values
gate_values = h[2::2]
new_h = [h[0]]
new_h.extend(h[1::2])
h = new_h
# Now we have:
# h = [state, state_1, state_2, ...]
# gate_values = [gate_value_1, gate_value_2, gate_value_3]
for i, gate_value in enumerate(gate_values):
gate_value.name = "gate_value_" + str(i)
# Save all the last states
last_states = {}
hidden_states = []
for d in range(args.layers):
h[d] = h[d] * x_mask
last_states[d] = h[d][-1, :, :]
h[d].name = "hidden_state_" + str(d)
hidden_states.append(h[d])
# Concatenate all the states
if args.layers > 1:
h = tensor.concatenate(h, axis=2)
h.name = "hidden_state_all"
# The updates of the hidden states
updates = []
for d in range(args.layers):
updates.append((inits[0][d], last_states[d]))
presoft = get_presoft(h, args)
cost, cross_entropy = get_costs(presoft, args)
return cost, cross_entropy, updates, gate_values, hidden_states
