ap.add_argument("--random_glimpse", default=False)
args = ap.parse_args()
mnist = MiniBatches((MnistDataset()), batch_size=1)
model_path = args.model
network = get_network(model_path, std=args.variance,
disable_reinforce=args.disable_reinforce, random_glimpse=args.random_glimpse)
trainer_conf = TrainerConfig()
trainer_conf.learning_rate = args.learning_rate
trainer_conf.weight_l2 = 0.0001
trainer_conf.hidden_l2 = 0.0001
trainer_conf.method = args.method
trainer_conf.disable_reinforce=args.disable_reinforce
trainer_conf.disable_backprop=args.disable_backprop
trainer = AttentionTrainer(network, network.layers[0], config=trainer_conf)
trainer_conf.report()
timer = Timer()
for _ in list(trainer.train(mnist.train_set(), mnist.valid_set(), mnist.test_set())):
pass
timer.end()
network.save_params(model_path)
timer.report()
model_path = args.model
network = get_network(model_path,
std=args.variance,
disable_reinforce=args.disable_reinforce,
random_glimpse=args.random_glimpse)
trainer_conf = TrainerConfig()
trainer_conf.learning_rate = LearningRateAnnealer.learning_rate(
args.learning_rate)
trainer_conf.weight_l2 = 0.0001
trainer_conf.hidden_l2 = 0.0001
trainer_conf.method = args.method
trainer = FirstGlimpseTrainer(network,
network.layers[0],
config=trainer_conf)
annealer = LearningRateAnnealer(trainer, patience=5)
timer = Timer()
for _ in trainer.train(mnist.train_set(), mnist.valid_set(),
mnist.test_set()):
if annealer.invoke():
break
timer.end()
network.save_params(model_path)
timer.report()
batch_set = MiniBatches(dataset)
if __name__ == '__main__':
model = NeuralClassifier(input_dim=26, input_tensor=3)
model.stack(
RNN(hidden_size=30,
input_type="sequence",
output_type="sequence",
vector_core=0.1),
RNN(hidden_size=30,
input_type="sequence",
output_type="sequence",
vector_core=0.3),
RNN(hidden_size=30,
input_type="sequence",
output_type="sequence",
vector_core=0.6),
RNN(hidden_size=30,
input_type="sequence",
output_type="one",
vector_core=0.9), Dense(4), Softmax())
trainer = SGDTrainer(model)
annealer = LearningRateAnnealer()
trainer.run(batch_set.train_set(),
batch_set.valid_set(),
controllers=[annealer])
outputs=train_monitors.values(),
updates=gradient_updates,
allow_input_downcast=True)
valid_iteration = theano.function(inputs=model.input_variables,
outputs=test_monitors.values(),
allow_input_downcast=True)
max_epochs = 10
mnist = MiniBatches(MnistDataset(), batch_size=20)
for i in range(max_epochs):
# Training
cost_matrix = []
for inputs in mnist.train_set():
costs = train_iteration(*inputs)
cost_matrix.append(costs)
train_costs = list(zip(train_monitors.keys(), np.mean(cost_matrix, axis=0)))
print "train", i, train_costs
# Test with valid data
cost_matrix = []
for inputs in mnist.valid_set():
costs = valid_iteration(*inputs)
cost_matrix.append(costs)
valid_costs = list(zip(test_monitors.keys(), np.mean(cost_matrix, axis=0)))
print "valid", i, valid_costs
model.save_params(model_path)
updates=gradient_updates,
allow_input_downcast=True)
valid_iteration = theano.function(inputs=model.input_variables,
outputs=test_monitors.values(),
allow_input_downcast=True)
max_epochs = 10
mnist = MiniBatches(MnistDataset(), batch_size=20)
for i in range(max_epochs):
# Training
cost_matrix = []
for inputs in mnist.train_set():
costs = train_iteration(*inputs)
cost_matrix.append(costs)
train_costs = list(
zip(train_monitors.keys(), np.mean(cost_matrix, axis=0)))
print "train", i, train_costs
# Test with valid data
cost_matrix = []
for inputs in mnist.valid_set():
costs = valid_iteration(*inputs)
cost_matrix.append(costs)
valid_costs = list(
zip(test_monitors.keys(), np.mean(cost_matrix, axis=0)))
print "valid", i, valid_costs
model.save_params(model_path)
# Shuffle the data
random.Random(3).shuffle(data)
# Separate data
valid_size = int(len(data) * 0.15)
train_set = data[valid_size:]
valid_set = data[:valid_size]
dataset = SequentialDataset(train_set, valid=valid_set)
dataset.pad_left(20)
dataset.report()
batch_set = MiniBatches(dataset)
if __name__ == '__main__':
model = NeuralClassifier(input_dim=26, input_tensor=3)
model.stack(RNN(hidden_size=30, input_type="sequence", output_type="sequence", vector_core=0.1),
RNN(hidden_size=30, input_type="sequence", output_type="sequence", vector_core=0.3),
RNN(hidden_size=30, input_type="sequence", output_type="sequence", vector_core=0.6),
RNN(hidden_size=30, input_type="sequence", output_type="one", vector_core=0.9),
Dense(4),
Softmax())
trainer = SGDTrainer(model)
annealer = LearningRateAnnealer(trainer)
trainer.run(batch_set.train_set(), batch_set.valid_set(), controllers=[annealer])
