def load_models(model_path=save_path,
in_size=len(input_columns),
out_size=len(output_columns) -
1 if cost_mode == 'RL-MDN' else len(output_columns),
hidden_size=hidden_size,
num_recurrent_layers=num_recurrent_layers,
model=layer_models[0]):
initials = []
if not os.path.isfile(model_path):
print 'Could not find model file.'
sys.exit(0)
print 'Loading model from {0}...'.format(model_path)
x = tensor.tensor3('features', dtype=theano.config.floatX)
y = tensor.tensor3('targets', dtype='floatX')
train_flag = [theano.shared(0)]
_, latent_size = load_encoder()
in_size = latent_size + len(input_columns)
y_hat, cost, cells = nn_fprop(x, y, in_size, out_size, hidden_size,
num_recurrent_layers, train_flag)
main_loop = MainLoop(algorithm=None,
data_stream=None,
model=Model(cost),
extensions=[saveload.Load(model_path)])
for extension in main_loop.extensions:
extension.main_loop = main_loop
main_loop._run_extensions('before_training')
bin_model = main_loop.model
print 'Model loaded. Building prediction function...'
hiddens = []
for i in range(num_recurrent_layers):
brick = [
b for b in bin_model.get_top_bricks()
if b.name == layer_models[i] + str(i)
][0]
hiddens.extend(
VariableFilter(theano_name=brick.name + '_apply_states')(
bin_model.variables))
hiddens.extend(
VariableFilter(theano_name=brick.name + '_apply_cells')(cells))
initials.extend(
VariableFilter(roles=[roles.INITIAL_STATE])(brick.parameters))
predict_func = theano.function([x], hiddens + [y_hat])
encoder, code_size = load_encoder()
return predict_func, initials, encoder, code_size
from blocks_extras.extensions.plot import Plot
from utils import get_stream, track_best, MainLoop, get_seed, make_wav, rescale
from model import nn_fprop
from config import config
# Load config parameters
locals().update(config)
# Set up model and prediction function
x = tensor.tensor3('inputs', dtype='float64')
y = tensor.tensor3('targets', dtype='float64')
model = 'bs'
with open ('gru_best.pkl', 'r') as picklefile:
model = load(picklefile)
y_hat, cost, cells = nn_fprop(x, y, frame_length, hidden_size, num_layers, model)
predict_fn = theano.function([x], y_hat)
# Generate
print "generating audio..."
seed = get_seed(hdf5_file, [seed_index])
sec = 16000
samples_to_generate = sec*secs_to_generate
num_frames_to_generate = samples_to_generate/frame_length + seq_length #don't include seed
predictions = []
prev_input = seed
for i in range(num_frames_to_generate):
prediction = predict_fn(prev_input)
predictions.append(prediction)
pred_min = numpy.min(predictions)
pred_max = numpy.max(predictions)
from model import nn_fprop
from config import config
# Load config parameters
locals().update(config)
# DATA
ix_to_char, char_to_ix, vocab_size = get_metadata(hdf5_file)
train_stream = get_stream(hdf5_file, 'train', batch_size)
dev_stream = get_stream(hdf5_file, 'dev', batch_size)
# MODEL
x = tensor.matrix('features', dtype='uint8')
y = tensor.matrix('targets', dtype='uint8')
y_hat, cost = nn_fprop(x, y, vocab_size, hidden_size, num_layers, model)
# COST
cg = ComputationGraph(cost)
if dropout > 0:
# Apply dropout only to the non-recurrent inputs (Zaremba et al. 2015)
inputs = VariableFilter(theano_name_regex=r'.*apply_input.*')(cg.variables)
cg = apply_dropout(cg, inputs, dropout)
cost = cg.outputs[0]
# Learning algorithm
step_rules = [RMSProp(learning_rate=learning_rate, decay_rate=decay_rate),
StepClipping(step_clipping)]
algorithm = GradientDescent(cost=cost, parameters=cg.parameters,
step_rule=CompositeRule(step_rules))
from model import nn_fprop
from config import config
# Load config parameters
locals().update(config)
# DATA
ix_to_char, char_to_ix, vocab_size = get_metadata(hdf5_file)
train_stream = get_stream(hdf5_file, 'train', batch_size)
dev_stream = get_stream(hdf5_file, 'dev', batch_size)
# MODEL
x = tensor.matrix('features', dtype='uint8')
y = tensor.matrix('targets', dtype='uint8')
y_hat, cost, cells = nn_fprop(x, y, vocab_size, hidden_size, num_layers, model)
# COST
cg = ComputationGraph(cost)
if dropout > 0:
# Apply dropout only to the non-recurrent inputs (Zaremba et al. 2015)
inputs = VariableFilter(theano_name_regex=r'.*apply_input.*')(cg.variables)
cg = apply_dropout(cg, inputs, dropout)
cost = cg.outputs[0]
# Learning algorithm
step_rules = [RMSProp(learning_rate=learning_rate, decay_rate=decay_rate),
StepClipping(step_clipping)]
algorithm = GradientDescent(cost=cost, parameters=cg.parameters,
step_rule=CompositeRule(step_rules))
# MODEL
x = T.TensorType('floatX', [False] * 3)('features')
y = T.tensor3('targets', dtype='floatX')
train_flag = [theano.shared(0)]
x = x.swapaxes(0, 1)
y = y.swapaxes(0, 1)
out_size = len(output_columns) - 1 if cost_mode == 'RL-MDN' else len(
output_columns)
_, latent_size = load_encoder()
in_size = latent_size + len(input_columns)
# mean = x[:,:,0:latent_size]
# var = T.clip(T.exp(x[:,:,latent_size:latent_size*2]), .0001, 1000)
# rrng = MRG_RandomStreams(seed)
# rand = rrng.normal(var.shape, 0, 1, dtype=theano.config.floatX)
# x = ifelse(T.lt(train_flag[0], .5), T.concatenate([mean , x[:,:,latent_size*2:]], axis=2) , T.concatenate([mean + (var * rand), x[:,:,latent_size*2:]], axis=2))
y_hat, cost, cells = nn_fprop(x, y, in_size, out_size, hidden_size,
num_recurrent_layers, train_flag)
# COST
cg = ComputationGraph(cost)
extra_updates = []
# Learning optimizer
if training_optimizer == 'Adam':
step_rules = [
Adam(learning_rate=learning_rate),
StepClipping(step_clipping)
] # , VariableClipping(threshold=max_norm_threshold)
elif training_optimizer == 'RMSProp':
step_rules = [
RMSProp(learning_rate=learning_rate, decay_rate=decay_rate),
StepClipping(step_clipping)
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
args = parser.parse_args()
print('Loading model from {0}...'.format(save_path[network_mode]))
x = tensor.tensor3('features', dtype='floatX')
y = tensor.tensor3('targets', dtype='floatX')
x = x.swapaxes(0, 1)
y = y.swapaxes(0, 1)
in_size = num_features
out_size = num_features
y_hat, cost, cells = nn_fprop(x,
y,
in_size,
out_size,
hidden_size[network_mode],
num_layers,
layer_models[network_mode][0],
'MDN',
training=False)
main_loop = MainLoop(algorithm=None,
data_stream=None,
model=Model(cost),
extensions=[saveload.Load(save_path[network_mode])])
for extension in main_loop.extensions:
extension.main_loop = main_loop
main_loop._run_extensions('before_training')
bin_model = main_loop.model
print 'Model loaded. Building prediction function...'
hiddens = []
def load_models(
models=hierarchy_models,
in_size=len(hierarchy_input_columns[level_number_in_hierarchy]),
out_size=len(hierarchy_output_columns[level_number_in_hierarchy]),
hidden_size=hidden_size,
num_layers=num_layers,
model=layer_models[0]):
predict_funcs = []
initials = []
for hierarchy_index in range(len(models)):
saved_model = models[hierarchy_index]
print 'Loading model from {0}...'.format(models[hierarchy_index])
x = tensor.tensor3('features', dtype=theano.config.floatX)
y = tensor.tensor3('targets', dtype=theano.config.floatX)
y_hat, cost, cells = nn_fprop(x,
y,
in_size,
out_size,
hidden_size,
num_layers,
model,
training=False)
main_loop = MainLoop(algorithm=None,
data_stream=None,
model=Model(cost),
extensions=[saveload.Load(saved_model)])
for extension in main_loop.extensions:
extension.main_loop = main_loop
main_loop._run_extensions('before_training')
bin_model = main_loop.model
print 'Model loaded. Building prediction function...'
hiddens = []
initials.append([])
for i in range(num_layers - specialized_layer_num):
brick = [
b for b in bin_model.get_top_bricks()
if b.name == layer_models[i] + str(i) + '-' + str(-1)
][0]
hiddens.extend(
VariableFilter(theano_name=brick.name + '_apply_states')(
bin_model.variables))
hiddens.extend(
VariableFilter(theano_name=brick.name + '_apply_cells')(cells))
initials[hierarchy_index].extend(
VariableFilter(roles=[roles.INITIAL_STATE])(brick.parameters))
specialized_count = len(game_tasks) if task_specialized else 0
for task in range(specialized_count):
for i in range(num_layers - specialized_layer_num, num_layers):
brick = [
b for b in bin_model.get_top_bricks()
if b.name == layer_models[i] + str(i) + '-' + str(task)
][0]
hiddens.extend(
VariableFilter(theano_name=brick.name + '_apply_states')(
bin_model.variables))
hiddens.extend(
VariableFilter(theano_name=brick.name +
'_apply_cells')(cells))
initials[hierarchy_index].extend(
VariableFilter(roles=[roles.INITIAL_STATE])(
brick.parameters))
output_count = len(game_tasks) if task_specialized else 1
predict_funcs.append([])
for task in range(output_count):
predict_funcs[hierarchy_index].append(
theano.function([x], hiddens + [y_hat[task]]))
return predict_funcs, initials
test_stream = get_stream(hdf5_file, 'test', batch_size)
# MODEL
x = tensor.tensor3('features', dtype='floatX')
y = tensor.tensor3('targets',
dtype='uint8' if cost_mode == 'Softmax' else 'floatX')
out_size = len(hierarchy_output_columns[level_number_in_hierarchy])
if use_helper_model:
in_size = helper_hidden_size * helper_num_layers + len(
hierarchy_input_columns[level_number_in_hierarchy])
else:
in_size = len(hierarchy_input_columns[level_number_in_hierarchy])
y_hat, cost, cells = nn_fprop(x,
y,
in_size,
out_size,
hidden_size,
num_layers,
layer_models[0],
training=True)
# COST
cg = ComputationGraph(cost)
if dropout > 0:
# Apply dropout only to the non-recurrent inputs (Zaremba et al. 2015)
inputs = VariableFilter(theano_name_regex=r'.*apply_input.*')(cg.variables)
print inputs
cg = apply_dropout(cg, inputs, dropout)
cost = cg.outputs[0]
# Learning algorithm
batch_size[network_mode])
test_stream = get_stream(hdf5_file[network_mode], 'test',
batch_size[network_mode])
# MODEL
x = T.tensor3('features', dtype='floatX')
y = T.tensor3('targets', dtype='floatX')
x = x.swapaxes(0, 1)
y = y.swapaxes(0, 1)
in_size = num_features
out_size = num_features
linear_output, cost, cells = nn_fprop(x,
y,
in_size,
out_size,
hidden_size[network_mode],
num_layers,
layer_models[network_mode][0],
'MDN',
training=True)
# COST
cg = ComputationGraph(cost)
if dropout[network_mode] > 0:
# Apply dropout only to the non-recurrent inputs (Zaremba et al. 2015)
inputs = VariableFilter(theano_name_regex=r'.*apply_input.*')(cg.variables)
print inputs
cg = apply_dropout(cg, inputs, dropout[network_mode])
cost = cg.outputs[0]
# Learning algorithm
