def train():
# build dataset
batch_size = 64
data = Mnist(batch_size=batch_size, train_valid_test_ratio=[5, 1, 1])
# build model
model = Sequential(input_var=T.matrix(), output_var=T.matrix())
model.add(Linear(prev_dim=28 * 28, this_dim=200))
model.add(RELU())
model.add(Linear(prev_dim=200, this_dim=100))
model.add(RELU())
model.add(Dropout(0.5))
model.add(Linear(prev_dim=100, this_dim=10))
model.add(Softmax())
# build learning method
decay_batch = int(data.train.X.shape[0] * 2 / batch_size)
learning_method = SGD(learning_rate=0.1,
momentum=0.9,
lr_decay_factor=0.9,
decay_batch=decay_batch)
# Build Logger
log = Log(
experiment_name='MLP',
description='This is a tutorial',
save_outputs=True, # log all the outputs from the screen
save_model=True, # save the best model
save_epoch_error=True, # log error at every epoch
save_to_database={
'name': 'Example.sqlite3',
'records': {
'Batch_Size': batch_size,
'Learning_Rate': learning_method.learning_rate,
'Momentum': learning_method.momentum
}
}) # end log
# put everything into the train object
train_object = TrainObject(model=model,
log=log,
dataset=data,
train_cost=mse,
valid_cost=error,
learning_method=learning_method,
stop_criteria={
'max_epoch': 100,
'epoch_look_back': 5,
'percent_decrease': 0.01
})
# finally run the code
train_object.setup()
train_object.run()
ypred = model.fprop(data.get_test().X)
ypred = np.argmax(ypred, axis=1)
y = np.argmax(data.get_test().y, axis=1)
accuracy = np.equal(ypred, y).astype('f4').sum() / len(y)
print('test accuracy:', accuracy)
def train():
"""
This examples implements the variational autoencoder from the paper
Auto-Encoding Variational Bayes by Diederik P Kingma, Max Welling, arXiv:1312.6114
"""
# build dataset
data = Mnist(batch_size=100, binary=False, train_valid_test_ratio=[5,1,1])
# for autoencoder, the output will be equal to input
data.set_train(X=data.get_train().X, y=data.get_train().X)
data.set_valid(X=data.get_valid().X, y=data.get_valid().X)
# build model
model = Sequential(input_var=T.matrix(), output_var=T.matrix())
model.add(VariationalAutoencoder(input_dim=28*28, bottlenet_dim=200, z_dim=20))
# build learning method
learning_method = SGD(learning_rate=0.0001, momentum=0.9,
lr_decay_factor=0.9, decay_batch=10000)
# put everything into the train object
train_object = TrainObject(model = model,
log = None,
dataset = data,
train_cost = SGVB_bin,
valid_cost = SGVB_bin,
learning_method = learning_method,
stop_criteria = {'max_epoch' : 10,
'epoch_look_back' : 5,
'percent_decrease' : 0.01}
)
# finally run the code
train_object.setup()
train_object.run()
def train():
"""
This examples implements the variational autoencoder from the paper
Auto-Encoding Variational Bayes by Diederik P Kingma, Max Welling, arXiv:1312.6114
"""
# build dataset
data = Mnist(batch_size=100, binary=False, train_valid_test_ratio=[5,1,1])
# for autoencoder, the output will be equal to input
data.set_train(X=data.get_train().X, y=data.get_train().X)
data.set_valid(X=data.get_valid().X, y=data.get_valid().X)
# build model
model = Sequential(input_var=T.matrix(), output_var=T.matrix())
model.add(VariationalAutoencoder(input_dim=28*28, bottlenet_dim=200, z_dim=20))
# build learning method
learning_method = SGD(learning_rate=0.0001, momentum=0.9,
lr_decay_factor=0.9, decay_batch=10000)
# put everything into the train object
train_object = TrainObject(model = model,
log = None,
dataset = data,
train_cost = SGVB_bin,
valid_cost = SGVB_bin,
learning_method = learning_method,
stop_criteria = {'max_epoch' : 10,
'epoch_look_back' : 5,
'percent_decrease' : 0.01}
)
# finally run the code
train_object.setup()
train_object.run()
def train():
# build dataset
batch_size = 64
data = Mnist(batch_size=batch_size, train_valid_test_ratio=[5,1,1])
# build model
model = Sequential(input_var=T.matrix(), output_var=T.matrix())
model.add(Linear(prev_dim=28*28, this_dim=200))
model.add(RELU())
model.add(Linear(prev_dim=200, this_dim=100))
model.add(RELU())
model.add(Dropout(0.5))
model.add(Linear(prev_dim=100, this_dim=10))
model.add(Softmax())
# build learning method
decay_batch = int(data.train.X.shape[0] * 2 / batch_size)
learning_method = SGD(learning_rate=0.1, momentum=0.9,
lr_decay_factor=0.9, decay_batch=decay_batch)
# Build Logger
log = Log(experiment_name = 'MLP',
description = 'This is a tutorial',
save_outputs = True, # log all the outputs from the screen
save_model = True, # save the best model
save_epoch_error = True, # log error at every epoch
save_to_database = {'name': 'Example.sqlite3',
'records': {'Batch_Size': batch_size,
'Learning_Rate': learning_method.learning_rate,
'Momentum': learning_method.momentum}}
) # end log
# put everything into the train object
train_object = TrainObject(model = model,
log = log,
dataset = data,
train_cost = mse,
valid_cost = error,
learning_method = learning_method,
stop_criteria = {'max_epoch' : 100,
'epoch_look_back' : 5,
'percent_decrease' : 0.01}
)
# finally run the code
train_object.setup()
train_object.run()
ypred = model.fprop(data.get_test().X)
ypred = np.argmax(ypred, axis=1)
y = np.argmax(data.get_test().y, axis=1)
accuracy = np.equal(ypred, y).astype('f4').sum() / len(y)
print 'test accuracy:', accuracy
def train():
# build dataset
data = Mnist(batch_size=64, train_valid_test_ratio=[5, 1, 1])
# for autoencoder, the output will be equal to input
data.set_train(X=data.get_train().X, y=data.get_train().X)
data.set_valid(X=data.get_valid().X, y=data.get_valid().X)
# build model
model = Sequential(input_var=T.matrix(), output_var=T.matrix())
# build encoder
model.add(Gaussian())
encode_layer1 = Linear(prev_dim=28 * 28, this_dim=200)
model.add(encode_layer1)
model.add(RELU())
encode_layer2 = Linear(prev_dim=200, this_dim=50)
model.add(encode_layer2)
model.add(Tanh())
# build decoder
decode_layer1 = Linear(prev_dim=50, this_dim=200, W=encode_layer2.W.T)
model.add(decode_layer1)
model.add(RELU())
decode_layer2 = Linear(prev_dim=200, this_dim=28 * 28, W=encode_layer1.W.T)
model.add(decode_layer2)
model.add(Sigmoid())
# build learning method
learning_method = AdaGrad(learning_rate=0.01,
momentum=0.9,
lr_decay_factor=0.9,
decay_batch=10000)
# put everything into the train object
train_object = TrainObject(model=model,
log=None,
dataset=data,
train_cost=entropy,
valid_cost=entropy,
learning_method=learning_method,
stop_criteria={
'max_epoch': 10,
'epoch_look_back': 5,
'percent_decrease': 0.01
})
# finally run the code
train_object.setup()
train_object.run()
def train():
# build dataset
data = Mnist(batch_size=64, train_valid_test_ratio=[5,1,1])
# for autoencoder, the output will be equal to input
data.set_train(X=data.get_train().X, y=data.get_train().X)
data.set_valid(X=data.get_valid().X, y=data.get_valid().X)
# build model
model = Sequential()
# build encoder
model.add(Gaussian(input_var=T.matrix()))
encode_layer1 = Linear(prev_dim=28*28, this_dim=200)
model.add(encode_layer1)
model.add(RELU())
encode_layer2 = Linear(prev_dim=200, this_dim=50)
model.add(encode_layer2)
model.add(Tanh())
# build decoder
decode_layer1 = Linear(prev_dim=50, this_dim=200, W=encode_layer2.W.T)
model.add(decode_layer1)
model.add(RELU())
decode_layer2 = Linear(prev_dim=200, this_dim=28*28, W=encode_layer1.W.T)
model.add(decode_layer2)
model.add(Sigmoid())
# build learning method
learning_method = AdaGrad(learning_rate=0.01, momentum=0.9,
lr_decay_factor=0.9, decay_batch=10000)
# put everything into the train object
train_object = TrainObject(model = model,
log = None,
dataset = data,
train_cost = entropy,
valid_cost = entropy,
learning_method = learning_method,
stop_criteria = {'max_epoch' : 10,
'epoch_look_back' : 5,
'percent_decrease' : 0.01}
)
# finally run the code
train_object.setup()
train_object.run()
def train():
# build dataset
data = Mnist(batch_size=64, train_valid_test_ratio=[5,1,1])
# build model
model = Sequential()
model.add(Linear(prev_dim=28*28, this_dim=200))
model.add(RELU())
model.add(Linear(prev_dim=200, this_dim=100))
model.add(RELU())
model.add(Dropout(0.5))
model.add(Linear(prev_dim=100, this_dim=10))
model.add(Softmax())
# build learning method
learning_method = AdaGrad(learning_rate=0.1, momentum=0.9,
lr_decay_factor=0.9, decay_batch=10000)
# put everything into the train object
train_object = TrainObject(model = model,
log = None,
dataset = data,
train_cost = mse,
valid_cost = error,
learning_method = learning_method,
stop_criteria = {'max_epoch' : 10,
'epoch_look_back' : 5,
'percent_decrease' : 0.01}
)
# finally run the code
train_object.setup()
train_object.run()
ypred = model.fprop(data.get_test().X)
ypred = np.argmax(ypred, axis=1)
y = np.argmax(data.get_test().y, axis=1)
print 'test accuracy:', accuracy_score(y, ypred)