def main(num_epochs=NUM_EPOCHS):
print("Loading data...")
dataset = load_data()
print("Building model and compiling functions...")
output_layer = build_model(
input_height=dataset["input_height"], input_width=dataset["input_width"], output_dim=dataset["output_dim"]
)
iter_funcs = create_iter_functions(dataset, output_layer, X_tensor_type=T.tensor4)
print("Starting training...")
now = time.time()
try:
for epoch in train(iter_funcs, dataset):
print("Epoch {} of {} took {:.3f}s".format(epoch["number"], num_epochs, time.time() - now))
now = time.time()
print(" training loss:\t\t{:.6f}".format(epoch["train_loss"]))
print(" validation loss:\t\t{:.6f}".format(epoch["valid_loss"]))
print(" validation accuracy:\t\t{:.2f} %%".format(epoch["valid_accuracy"] * 100))
if epoch["number"] >= num_epochs:
break
except KeyboardInterrupt:
pass
return output_layer
def main(num_epochs=NUM_EPOCHS):
dataset = load_data()
output_layer = build_model(
input_width=dataset['input_width'],
input_height=dataset['input_width'],
output_dim=dataset['output_dim'],
)
iter_funcs = create_iter_functions(
dataset,
output_layer,
X_tensor_type=T.tensor4,
)
print("Starting training...")
for epoch in train(iter_funcs, dataset):
print("Epoch %d of %d" % (epoch['number'], num_epochs))
print(" training loss:\t\t%.6f" % epoch['train_loss'])
print(" validation loss:\t\t%.6f" % epoch['valid_loss'])
print(" validation accuracy:\t\t%.2f %%" %
(epoch['valid_accuracy'] * 100))
if epoch['number'] >= num_epochs:
break
return output_layer
def main(num_epochs=NUM_EPOCHS):
dataset = load_data()
output_layer = build_model(
input_height=dataset['input_height'],
input_width=dataset['input_width'],
output_dim=dataset['output_dim'],
)
iter_funcs = create_iter_functions(
dataset,
output_layer,
X_tensor_type=T.tensor4,
)
print("Starting training...")
for epoch in train(iter_funcs, dataset):
print("Epoch %d of %d" % (epoch['number'], num_epochs))
print(" training loss:\t\t%.6f" % epoch['train_loss'])
print(" validation loss:\t\t%.6f" % epoch['valid_loss'])
print(" validation accuracy:\t\t%.2f %%" %
(epoch['valid_accuracy'] * 100))
if epoch['number'] >= num_epochs:
break
def main(num_epochs=NUM_EPOCHS):
print("Loading data...")
dataset = load_data()
print("Building model and compiling functions...")
output_layer = build_model(
input_height=dataset['input_height'],
input_width=dataset['input_width'],
output_dim=dataset['output_dim'],
)
iter_funcs = create_iter_functions(
dataset,
output_layer,
X_tensor_type=T.tensor4,
)
print("Starting training...")
now = time.time()
try:
for epoch in train(iter_funcs, dataset):
print("Epoch {} of {} took {:.3f}s".format(
epoch['number'], num_epochs, time.time() - now))
now = time.time()
print(" training loss:\t\t{:.6f}".format(epoch['train_loss']))
print(" validation loss:\t\t{:.6f}".format(epoch['valid_loss']))
print(" validation accuracy:\t\t{:.2f} %%".format(
epoch['valid_accuracy'] * 100))
if epoch['number'] % 100 is 0:
#### save and load weights
# save
weights_save = lasagne.layers.get_all_param_values(output_layer)
pickle.dump( weights_save, open( "weights_epoch%d.pkl" % epoch['number'], "wb" ) )
# load
#weights_load = pickle.load( open( "weights.pkl", "rb" ) )
#lasagne.layers.set_all_param_values(output_layer, weights_load)
if epoch['number'] >= num_epochs:
break
except KeyboardInterrupt:
pass
return output_layer
def main():
BATCH_SIZE = 100
MAX_PHRASE_LENGTH = 108
NUM_DATAPOINTS = 10000
print("Loading data...")
dataset = load_data(n=NUM_DATAPOINTS, max_phrase_length=MAX_PHRASE_LENGTH)
print("Building model and compiling functions...")
output_layer = build_model(
#input_height=dataset['input_height'],
#input_width=dataset['input_width'],
batch_size=BATCH_SIZE,
max_phrase_length=MAX_PHRASE_LENGTH,
output_dim=dataset['output_dim'],
)
iter_funcs = create_iter_functions(
dataset,
output_layer,
X_tensor_type=T.tensor3,
batch_size=BATCH_SIZE,
learning_rate=0.001,
momentum=0.9,
)
print("Starting training...")
now = time.time()
try:
for epoch in train(iter_funcs, dataset, BATCH_SIZE):
print("Epoch {} of {} took {:.3f}s".format(
epoch['number'], num_epochs, time.time() - now))
now = time.time()
print(" training loss:\t\t{:.6f}".format(epoch['train_loss']))
print(" validation loss:\t\t{:.6f}".format(epoch['valid_loss']))
print(" validation accuracy:\t\t{:.2f} %%".format(
epoch['valid_accuracy'] * 100))
if epoch['number'] >= num_epochs:
break
except KeyboardInterrupt:
pass
return output_layer
def main(num_epochs=NUM_EPOCHS):
print("Loading data...")
dataset = load_data()
print("Building model and compiling functions...")
output_layer = build_model(
input_height=dataset['input_height'],
input_width=dataset['input_width'],
output_dim=dataset['output_dim'],
)
iter_funcs = create_iter_functions(
dataset,
output_layer,
X_tensor_type=T.tensor4,
)
print("Starting training...")
now = time.time()
try:
for epoch in train(iter_funcs, dataset):
print("Epoch {} of {} took {:.3f}s".format(epoch['number'],
num_epochs,
time.time() - now))
now = time.time()
print(" training loss:\t\t{:.6f}".format(epoch['train_loss']))
print(" validation loss:\t\t{:.6f}".format(epoch['valid_loss']))
print(" validation accuracy:\t\t{:.2f} %%".format(
epoch['valid_accuracy'] * 100))
if epoch['number'] >= num_epochs:
break
except KeyboardInterrupt:
pass
return output_layer
