cost.name = "nll"
cg = ComputationGraph(cost)
model = Model(cost)
transition_matrix = VariableFilter(theano_name_regex="state_to_state")(cg.parameters)
for matr in transition_matrix:
matr.set_value(0.98 * numpy.eye(hidden_size_recurrent, dtype=floatX))
from play.utils import regex_final_value
extra_updates = []
for name, var in states.items():
update = tensor.switch(
start_flag, 0.0 * var, VariableFilter(theano_name_regex=regex_final_value(name))(cg.auxiliary_variables)[0]
)
extra_updates.append((var, update))
# Old values for n
load_name = "sp_and_f0_1"
from blocks.serialization import load
main_loop = load(save_dir + "pkl/best_" + load_name + ".pkl")
new_params = []
for key, value in model.get_parameter_dict().items():
if key in [
cg = ComputationGraph(cost)
model = Model(cost)
transition_matrix = VariableFilter(theano_name_regex="state_to_state")(
cg.parameters)
for matr in transition_matrix:
matr.set_value(0.98 * numpy.eye(hidden_size_recurrent, dtype=floatX))
from play.utils import regex_final_value
extra_updates = []
for name, var in states.items():
update = tensor.switch(
start_flag, 0. * var,
VariableFilter(theano_name_regex=regex_final_value(name))(
cg.auxiliary_variables)[0])
extra_updates.append((var, update))
#################
# Monitoring vars
#################
mean_data = x.mean(axis=(0, 1)).copy(name="data_mean")
sigma_data = x.std(axis=(0, 1)).copy(name="data_std")
max_data = x.max(axis=(0, 1)).copy(name="data_max")
min_data = x.min(axis=(0, 1)).copy(name="data_min")
monitoring_variables = [cost, lr]
data_monitoring = [mean_data, sigma_data, max_data, min_data]
voiced = tensor.matrix('voiced')
start_flag = tensor.scalar('start_flag')
sp = tensor.tensor3('sp')
f0s = f0.dimshuffle(0,1,'x')
voiceds = voiced.dimshuffle(0,1,'x')
x = tensor.concatenate([sp, f0s, voiceds], 2)
cost_matrix = generator.cost_matrix(x)
cg = ComputationGraph(cost_matrix)
from blocks.filter import VariableFilter
from play.utils import regex_final_value
extra_updates = []
for name, var in states.items():
update = VariableFilter(theano_name_regex=regex_final_value(name))(cg.auxiliary_variables)[0]
extra_updates.append((var, update))
#print function([f0, sp, voiced], cost_matrix, updates = extra_updates)(x_tr[0],x_tr[1],x_tr[2])
#generator.generate(n_steps=steps, batch_size=n_samples, iterate=True, **states)
#states = {}
sample = ComputationGraph(generator.generate(n_steps=steps,
batch_size=n_samples, iterate=True, **states))
sample_fn = sample.get_theano_function()
outputs_bp = sample_fn()[-2]
for this_sample in range(n_samples):
print "Iteration: ", this_sample
cost = cost_matrix.mean() + 0.*start_flag
cost.name = "nll"
cg = ComputationGraph(cost)
model = Model(cost)
transition_matrix = VariableFilter(
theano_name_regex="state_to_state")(cg.parameters)
for matr in transition_matrix:
matr.set_value(0.98*numpy.eye(hidden_size_recurrent, dtype=floatX))
from play.utils import regex_final_value
extra_updates = []
for name, var in states.items():
update = tensor.switch(start_flag, 0.*var,
VariableFilter(theano_name_regex=regex_final_value(name)
)(cg.auxiliary_variables)[0])
extra_updates.append((var, update))
#################
# Monitoring vars
#################
mean_data = x.mean(axis = (0,1)).copy(name="data_mean")
sigma_data = x.std(axis = (0,1)).copy(name="data_std")
max_data = x.max(axis = (0,1)).copy(name="data_max")
min_data = x.min(axis = (0,1)).copy(name="data_min")
monitoring_variables = [cost, lr]
data_monitoring = [mean_data, sigma_data,
voiced = tensor.matrix('voiced')
start_flag = tensor.scalar('start_flag')
sp = tensor.tensor3('sp')
f0s = f0.dimshuffle(0,1,'x')
voiceds = voiced.dimshuffle(0,1,'x')
x = tensor.concatenate([sp, f0s, voiceds], 2)
cost_matrix = generator.cost_matrix(x)
cg = ComputationGraph(cost_matrix)
from blocks.filter import VariableFilter
from play.utils import regex_final_value
extra_updates = []
for name, var in states.items():
update = VariableFilter(theano_name_regex=regex_final_value(name))(cg.auxiliary_variables)[0]
extra_updates.append((var, update))
#print function([f0, sp, voiced], cost_matrix, updates = extra_updates)(x_tr[0],x_tr[1],x_tr[2])
#generator.generate(n_steps=steps, batch_size=n_samples, iterate=True, **states)
#states = {}
sample = ComputationGraph(generator.generate(n_steps=steps,
batch_size=n_samples, iterate=True, **states))
sample_fn = sample.get_theano_function()
outputs_bp = sample_fn()[-2]
for this_sample in range(n_samples):
print "Iteration: ", this_sample