def build_dataset(self):
dataset = None
preprocessor = None if self.state.dataset.preprocessor.type is None else \
getattr(preproc, self.state.dataset.preprocessor.type)()
if self.state.dataset.preprocessor.type == 'Scale':
preprocessor.max = self.state.dataset.preprocessor.global_max
preprocessor.min = self.state.dataset.preprocessor.global_min
preprocessor.buffer = self.state.dataset.preprocessor.buffer
preprocessor.scale_range = self.state.dataset.preprocessor.scale_range
if self.state.dataset.type == 'Mnist':
dataset = Mnist(train_valid_test_ratio=self.state.dataset.
train_valid_test_ratio,
preprocessor=preprocessor,
batch_size=self.state.dataset.batch_size,
num_batches=self.state.dataset.num_batches,
iter_class=self.state.dataset.iter_class,
rng=self.state.dataset.rng)
if self.state.dataset.type[:11] == 'TransFactor':
dataset = getattr(tf, self.state.dataset.type)(
# feature_size = self.state.dataset.feature_size,
# target_size = self.state.dataset.target_size,
train_valid_test_ratio=self.state.dataset.
train_valid_test_ratio,
preprocessor=preprocessor,
batch_size=self.state.dataset.batch_size,
num_batches=self.state.dataset.num_batches,
iter_class=self.state.dataset.iter_class,
rng=self.state.dataset.rng)
elif self.state.dataset.type[:12] == 'Mnist_Blocks':
dataset = getattr(mnist, self.state.dataset.type)(
feature_size=self.state.dataset.feature_size,
target_size=self.state.dataset.feature_size,
train_valid_test_ratio=self.state.dataset.
train_valid_test_ratio,
preprocessor=preprocessor,
batch_size=self.state.dataset.batch_size,
num_batches=self.state.dataset.num_batches,
iter_class=self.state.dataset.iter_class,
rng=self.state.dataset.rng)
return dataset
import os os.environ['PYNET_DATA_PATH'] = '/Volumes/Storage/Dropbox/CodingProjects/pynet/data/' from pynet.datasets.mnist import Mnist from pynet.datasets.preprocessor import GCN data = Mnist() train = data.get_train() proc = GCN() out = proc.apply(train.X) inv = proc.invert(out)
def build_dataset(self):
dataset = None
preprocessor = None if self.state.dataset.preprocessor.type is None else \
getattr(preproc, self.state.dataset.preprocessor.type)()
noise = None if self.state.dataset.dataset_noise.type is None else \
getattr(data_noise, self.state.dataset.dataset_noise.type)()
if self.state.dataset.dataset_noise.type == 'Gaussian':
noise.std = self.state.dataset.dataset_noise.std
if self.state.dataset.preprocessor.type == 'Scale':
preprocessor.max = self.state.dataset.preprocessor.global_max
preprocessor.min = self.state.dataset.preprocessor.global_min
preprocessor.buffer = self.state.dataset.preprocessor.buffer
preprocessor.scale_range = self.state.dataset.preprocessor.scale_range
if self.state.dataset.type == 'Mnist':
dataset = Mnist(train_valid_test_ratio=self.state.dataset.
train_valid_test_ratio,
preprocessor=preprocessor,
noise=noise,
batch_size=self.state.dataset.batch_size,
num_batches=self.state.dataset.num_batches,
iter_class=self.state.dataset.iter_class,
rng=self.state.dataset.rng)
train = dataset.get_train()
dataset.set_train(train.y, train.y)
valid = dataset.get_valid()
dataset.set_valid(valid.y, valid.y)
test = dataset.get_test()
dataset.set_test(test.y, test.y)
elif self.state.dataset.type[:12] == 'Mnist_Blocks':
dataset = getattr(mnist, self.state.dataset.type)(
feature_size=self.state.dataset.feature_size,
target_size=self.state.dataset.feature_size,
train_valid_test_ratio=self.state.dataset.
train_valid_test_ratio,
preprocessor=preprocessor,
noise=noise,
batch_size=self.state.dataset.batch_size,
num_batches=self.state.dataset.num_batches,
iter_class=self.state.dataset.iter_class,
rng=self.state.dataset.rng)
elif self.state.dataset.type[:4] == 'P276':
dataset = getattr(spec, self.state.dataset.type)(
train_valid_test_ratio=self.state.dataset.
train_valid_test_ratio,
preprocessor=preprocessor,
noise=noise,
batch_size=self.state.dataset.batch_size,
num_batches=self.state.dataset.num_batches,
iter_class=self.state.dataset.iter_class,
rng=self.state.dataset.rng)
train = dataset.get_train()
dataset.set_train(train.X, train.X)
valid = dataset.get_valid()
dataset.set_valid(valid.X, valid.X)
test = dataset.get_test()
dataset.set_test(test.X, test.X)
elif self.state.dataset.type[:5] == 'Laura':
dataset = getattr(spec, self.state.dataset.type)(
feature_size=self.state.dataset.feature_size,
target_size=self.state.dataset.feature_size,
train_valid_test_ratio=self.state.dataset.
train_valid_test_ratio,
num_blocks=self.state.dataset.num_blocks,
preprocessor=preprocessor,
noise=noise,
batch_size=self.state.dataset.batch_size,
num_batches=self.state.dataset.num_batches,
iter_class=self.state.dataset.iter_class,
rng=self.state.dataset.rng)
elif self.state.dataset.type[:18] == 'TransFactor_Blocks':
dataset = getattr(tf, self.state.dataset.type)(
feature_size=self.state.dataset.feature_size,
target_size=self.state.dataset.feature_size,
one_hot=self.state.dataset.one_hot,
num_blocks=self.state.dataset.num_blocks,
train_valid_test_ratio=self.state.dataset.
train_valid_test_ratio,
preprocessor=preprocessor,
noise=noise,
batch_size=self.state.dataset.batch_size,
num_batches=self.state.dataset.num_batches,
iter_class=self.state.dataset.iter_class,
rng=self.state.dataset.rng)
elif self.state.dataset.type[:11] == 'TransFactor':
dataset = getattr(tf, self.state.dataset.type)(
# feature_size = self.state.dataset.feature_size,
# target_size = self.state.dataset.feature_size,
train_valid_test_ratio=self.state.dataset.
train_valid_test_ratio,
preprocessor=preprocessor,
noise=noise,
batch_size=self.state.dataset.batch_size,
num_batches=self.state.dataset.num_batches,
iter_class=self.state.dataset.iter_class,
rng=self.state.dataset.rng)
train = dataset.get_train()
dataset.set_train(train.X, train.X)
valid = dataset.get_valid()
dataset.set_valid(valid.X, valid.X)
test = dataset.get_test()
dataset.set_test(test.X, test.X)
elif self.state.dataset.type[:13] == 'I2R_Posterior':
dataset = getattr(i2r, self.state.dataset.type)(
train_valid_test_ratio=self.state.dataset.
train_valid_test_ratio,
preprocessor=preprocessor,
noise=noise,
batch_size=self.state.dataset.batch_size,
num_batches=self.state.dataset.num_batches,
iter_class=self.state.dataset.iter_class,
rng=self.state.dataset.rng)
return dataset
def build_dataset(self):
dataset = None
preprocessor = None if self.state.dataset.preprocessor.type is None else \
getattr(preproc, self.state.dataset.preprocessor.type)()
# if self.state.dataset.noise.type == 'BlackOut' or self.state.dataset.noise.type == 'MaskOut':
# noise = None if self.state.dataset.noise.type is None else \
# getattr(noisy, self.state.dataset.noise.type)(ratio=self.state.dataset.noise.ratio)
# else:
# noise = getattr(noisy, self.state.dataset.noise.type)()
noise = None if self.state.dataset.dataset_noise.type is None else \
getattr(noisy, self.state.dataset.dataset_noise.type)()
if self.state.dataset.preprocessor.type == 'Scale':
preprocessor.max = self.state.dataset.preprocessor.global_max
preprocessor.min = self.state.dataset.preprocessor.global_min
preprocessor.buffer = self.state.dataset.preprocessor.buffer
preprocessor.scale_range = self.state.dataset.preprocessor.scale_range
if self.state.dataset.type == 'Mnist':
dataset = Mnist(train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
train = dataset.get_train()
dataset.set_train(train.X, train.X)
valid = dataset.get_valid()
dataset.set_valid(valid.X, valid.X)
test = dataset.get_test()
dataset.set_test(test.X, test.X)
elif self.state.dataset.type[:12] == 'Mnist_Blocks':
dataset = getattr(mnist, self.state.dataset.type)(
feature_size = self.state.dataset.feature_size,
target_size = self.state.dataset.feature_size,
train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
elif self.state.dataset.type[:4] == 'P276':
dataset = getattr(spec, self.state.dataset.type)(
train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
train = dataset.get_train()
dataset.set_train(train.X, train.X)
valid = dataset.get_valid()
dataset.set_valid(valid.X, valid.X)
test = dataset.get_test()
dataset.set_test(test.X, test.X)
elif self.state.dataset.type[:5] == 'Laura':
dataset = getattr(spec, self.state.dataset.type)(
feature_size = self.state.dataset.feature_size,
target_size = self.state.dataset.feature_size,
train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
elif self.state.dataset.type[:18] == 'TransFactor_Blocks':
dataset = getattr(tf, self.state.dataset.type)(
feature_size = self.state.dataset.feature_size,
target_size = self.state.dataset.feature_size,
train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
elif self.state.dataset.type[:11] == 'TransFactor':
dataset = getattr(tf, self.state.dataset.type)(
# feature_size = self.state.dataset.feature_size,
# target_size = self.state.dataset.feature_size,
train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
train = dataset.get_train()
dataset.set_train(train.X, train.X)
valid = dataset.get_valid()
dataset.set_valid(valid.X, valid.X)
test = dataset.get_test()
dataset.set_test(test.X, test.X)
return dataset
def build_dataset(self):
dataset = None
preprocessor = None if self.state.dataset.preprocessor.type is None else \
getattr(preproc, self.state.dataset.preprocessor.type)()
noise = None if self.state.dataset.dataset_noise.type is None else \
getattr(data_noise, self.state.dataset.dataset_noise.type)()
if self.state.dataset.dataset_noise.type == 'Gaussian':
noise.std = self.state.dataset.dataset_noise.std
if self.state.dataset.preprocessor.type == 'Scale':
preprocessor.max = self.state.dataset.preprocessor.global_max
preprocessor.min = self.state.dataset.preprocessor.global_min
preprocessor.buffer = self.state.dataset.preprocessor.buffer
preprocessor.scale_range = self.state.dataset.preprocessor.scale_range
if self.state.dataset.type == 'Mnist':
dataset = Mnist(train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
train = dataset.get_train()
dataset.set_train(train.y, train.y)
valid = dataset.get_valid()
dataset.set_valid(valid.y, valid.y)
test = dataset.get_test()
dataset.set_test(test.y, test.y)
elif self.state.dataset.type[:12] == 'Mnist_Blocks':
dataset = getattr(mnist, self.state.dataset.type)(
feature_size = self.state.dataset.feature_size,
target_size = self.state.dataset.feature_size,
train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
elif self.state.dataset.type[:4] == 'P276':
dataset = getattr(spec, self.state.dataset.type)(
train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
train = dataset.get_train()
dataset.set_train(train.X, train.X)
valid = dataset.get_valid()
dataset.set_valid(valid.X, valid.X)
test = dataset.get_test()
dataset.set_test(test.X, test.X)
elif self.state.dataset.type[:5] == 'Laura':
dataset = getattr(spec, self.state.dataset.type)(
feature_size = self.state.dataset.feature_size,
target_size = self.state.dataset.feature_size,
train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
num_blocks = self.state.dataset.num_blocks,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
elif self.state.dataset.type[:18] == 'TransFactor_Blocks':
dataset = getattr(tf, self.state.dataset.type)(
feature_size = self.state.dataset.feature_size,
target_size = self.state.dataset.feature_size,
one_hot = self.state.dataset.one_hot,
num_blocks = self.state.dataset.num_blocks,
train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
elif self.state.dataset.type[:11] == 'TransFactor':
dataset = getattr(tf, self.state.dataset.type)(
# feature_size = self.state.dataset.feature_size,
# target_size = self.state.dataset.feature_size,
train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
train = dataset.get_train()
dataset.set_train(train.X, train.X)
valid = dataset.get_valid()
dataset.set_valid(valid.X, valid.X)
test = dataset.get_test()
dataset.set_test(test.X, test.X)
elif self.state.dataset.type[:13] == 'I2R_Posterior':
dataset = getattr(i2r, self.state.dataset.type)(
train_valid_test_ratio = self.state.dataset.train_valid_test_ratio,
preprocessor = preprocessor,
noise = noise,
batch_size = self.state.dataset.batch_size,
num_batches = self.state.dataset.num_batches,
iter_class = self.state.dataset.iter_class,
rng = self.state.dataset.rng)
return dataset
def mlp():
# build dataset
data = Mnist(preprocessor=None, train_valid_test_ratio=[5, 1, 1])
# build mlp
mlp = MLP(input_dim=data.feature_size())
W1 = GaussianWeight(prev_dim=mlp.input_dim, this_dim=1000)
hidden1 = PRELU(dim=1000,
name='h1_layer',
W=W1(mean=0, std=0.1),
b=None,
dropout_below=None)
mlp.add_layer(hidden1)
W2 = XavierWeight(prev_dim=hidden1.dim, this_dim=data.target_size())
output = Softmax(dim=data.target_size(),
name='output_layer',
W=W2(),
b=None,
dropout_below=None)
mlp.add_layer(output)
# build learning method
learning_method = AdaGrad(learning_rate=0.1, momentum=0.9)
# set the learning rules
learning_rule = LearningRule(max_col_norm=10,
L1_lambda=None,
L2_lambda=None,
training_cost=Cost(type='mse'),
learning_rate_decay_factor=None,
stopping_criteria={
'max_epoch': 300,
'epoch_look_back': 10,
'cost': Cost(type='error'),
'percent_decrease': 0.01
})
# (optional) build the logging object
log = Log(experiment_name='mnist',
description='This is tutorial example',
save_outputs=True,
save_learning_rule=True,
save_model=True,
save_epoch_error=True,
save_to_database={
'name': 'Example.db',
'records': {
'Dataset':
data.__class__.__name__,
'max_col_norm':
learning_rule.max_col_norm,
'Weight_Init_Seed':
mlp.rand_seed,
'Dropout_Below':
str([layer.dropout_below for layer in mlp.layers]),
'Batch_Size':
data.batch_size,
'Layer_Dim':
str([layer.dim for layer in mlp.layers]),
'Layer_Types':
str([layer.__class__.__name__ for layer in mlp.layers]),
'Preprocessor':
data.preprocessor.__class__.__name__,
'Learning_Rate':
learning_method.learning_rate,
'Momentum':
learning_method.momentum,
'Training_Cost':
learning_rule.cost.type,
'Stopping_Cost':
learning_rule.stopping_criteria['cost'].type
}
}) # end log
# put everything into the train object
train_object = TrainObject(model=mlp,
dataset=data,
learning_rule=learning_rule,
learning_method=learning_method,
log=log)
# finally run the code
train_object.run()
def stacked_autoencoder():
name = "Stacked_AE"
# =====[ Train First layer of stack autoencoder ]=====#
print("Start training First Layer of AutoEncoder")
# building dataset, batch_size and preprocessor
data = Mnist(train_valid_test_ratio=[8, 1, 1], batch_size=100)
# for AutoEncoder, the inputs and outputs must be the same
train = data.get_train()
data.set_train(train.X, train.X)
valid = data.get_valid()
data.set_valid(valid.X, valid.X)
test = data.get_test()
data.set_test(test.X, test.X)
# building autoencoder
ae = AutoEncoder(input_dim=data.feature_size(), rand_seed=123)
h1_layer = RELU(dim=500, name="h1_layer", W=None, b=None)
# adding encoding layer
ae.add_encode_layer(h1_layer)
# mirror layer has W = h1_layer.W.T
h1_mirror = RELU(dim=ae.input_dim, name="h1_mirror", W=h1_layer.W.T, b=None)
# adding decoding mirror layer
ae.add_decode_layer(h1_mirror)
# build learning method
learning_method = SGD(learning_rate=0.001, momentum=0.9)
# set the learning rules
learning_rule = LearningRule(
max_col_norm=10,
L1_lambda=None,
L2_lambda=None,
training_cost=Cost(type="mse"),
learning_rate_decay_factor=None,
stopping_criteria={"max_epoch": 3, "epoch_look_back": 1, "cost": Cost(type="error"), "percent_decrease": 0.01},
)
# put all the components into a TrainObject
train_object = TrainObject(model=ae, dataset=data, learning_rule=learning_rule, learning_method=learning_method)
# finally run the training
train_object.run()
# =====[ Train Second Layer of autoencoder ]=====#
print("Start training Second Layer of AutoEncoder")
# fprop == forward propagation
reduced_train_X = ae.encode(train.X)
reduced_valid_X = ae.encode(valid.X)
reduced_test_X = ae.encode(test.X)
data.set_train(X=reduced_train_X, y=reduced_train_X)
data.set_valid(X=reduced_valid_X, y=reduced_valid_X)
data.set_test(X=reduced_test_X, y=reduced_test_X)
# create a new mlp taking inputs from the encoded outputs of first autoencoder
ae2 = AutoEncoder(input_dim=data.feature_size(), rand_seed=None)
h2_layer = RELU(dim=100, name="h2_layer", W=None, b=None)
ae2.add_encode_layer(h2_layer)
h2_mirror = RELU(dim=h1_layer.dim, name="h2_mirror", W=h2_layer.W.T, b=None)
ae2.add_decode_layer(h2_mirror)
train_object = TrainObject(model=ae2, dataset=data, learning_rule=learning_rule, learning_method=learning_method)
train_object.run()
# =====[ Fine Tuning ]=====#
print("Fine Tuning")
data = Mnist()
train = data.get_train()
data.set_train(train.X, train.X)
valid = data.get_valid()
data.set_valid(valid.X, valid.X)
test = data.get_test()
data.set_test(test.X, test.X)
ae3 = AutoEncoder(input_dim=data.feature_size(), rand_seed=None)
ae3.add_encode_layer(h1_layer)
ae3.add_encode_layer(h2_layer)
ae3.add_decode_layer(h2_mirror)
ae3.add_decode_layer(h1_mirror)
train_object = TrainObject(model=ae3, dataset=data, learning_rule=learning_rule, learning_method=learning_method)
train_object.run()
print("Training Done")
def autoencoder():
# building dataset, batch_size and preprocessor
data = Mnist(train_valid_test_ratio=[8, 1, 1], batch_size=100, preprocessor=GCN())
# for AutoEncoder, the inputs and outputs must be the same
train = data.get_train()
data.set_train(train.X, train.X)
valid = data.get_valid()
data.set_valid(valid.X, valid.X)
test = data.get_test()
data.set_test(test.X, test.X)
# building autoencoder
ae = AutoEncoder(input_dim=data.feature_size(), rand_seed=123)
h1_layer = Tanh(dim=500, name="h1_layer", W=None, b=None)
# adding encoding layer
ae.add_encode_layer(h1_layer)
# mirror layer has W = h1_layer.W.T
h1_mirror = Tanh(dim=ae.input_dim, name="h1_mirror", W=h1_layer.W.T, b=None)
# adding decoding mirror layer
ae.add_decode_layer(h1_mirror)
# build learning method
learning_method = AdaGrad(learning_rate=0.1, momentum=0.9)
# set the learning rules
learning_rule = LearningRule(
max_col_norm=10,
L1_lambda=None,
L2_lambda=None,
training_cost=Cost(type="mse"),
learning_rate_decay_factor=None,
stopping_criteria={
"max_epoch": 300,
"epoch_look_back": 10,
"cost": Cost(type="error"),
"percent_decrease": 0.01,
},
)
# put all the components into a TrainObject
train_object = TrainObject(model=ae, dataset=data, learning_rule=learning_rule, learning_method=learning_method)
# finally run the training
train_object.run()
import os
os.environ[
'PYNET_DATA_PATH'] = '/Volumes/Storage/Dropbox/CodingProjects/pynet/data/'
from pynet.datasets.mnist import Mnist
from pynet.datasets.preprocessor import GCN
data = Mnist()
train = data.get_train()
proc = GCN()
out = proc.apply(train.X)
inv = proc.invert(out)
def mlp():
# build dataset
data = Mnist(preprocessor=None, train_valid_test_ratio=[5,1,1])
# build mlp
mlp = MLP(input_dim = data.feature_size())
W1 = GaussianWeight(prev_dim=mlp.input_dim, this_dim=1000)
hidden1 = PRELU(dim=1000, name='h1_layer', W=W1(mean=0, std=0.1),
b=None, dropout_below=None)
mlp.add_layer(hidden1)
W2 = XavierWeight(prev_dim=hidden1.dim, this_dim=data.target_size())
output = Softmax(dim=data.target_size(), name='output_layer', W=W2(),
b=None, dropout_below=None)
mlp.add_layer(output)
# build learning method
learning_method = AdaGrad(learning_rate=0.1, momentum=0.9)
# set the learning rules
learning_rule = LearningRule(max_col_norm = 10,
L1_lambda = None,
L2_lambda = None,
training_cost = Cost(type='mse'),
learning_rate_decay_factor = None,
stopping_criteria = {'max_epoch' : 300,
'epoch_look_back' : 10,
'cost' : Cost(type='error'),
'percent_decrease' : 0.01}
)
# (optional) build the logging object
log = Log(experiment_name = 'mnist',
description = 'This is tutorial example',
save_outputs = True,
save_learning_rule = True,
save_model = True,
save_epoch_error = True,
save_to_database = {'name': 'Example.db',
'records' : {'Dataset' : data.__class__.__name__,
'max_col_norm' : learning_rule.max_col_norm,
'Weight_Init_Seed' : mlp.rand_seed,
'Dropout_Below' : str([layer.dropout_below for layer in mlp.layers]),
'Batch_Size' : data.batch_size,
'Layer_Dim' : str([layer.dim for layer in mlp.layers]),
'Layer_Types' : str([layer.__class__.__name__ for layer in mlp.layers]),
'Preprocessor' : data.preprocessor.__class__.__name__,
'Learning_Rate' : learning_method.learning_rate,
'Momentum' : learning_method.momentum,
'Training_Cost' : learning_rule.cost.type,
'Stopping_Cost' : learning_rule.stopping_criteria['cost'].type}}
) # end log
# put everything into the train object
train_object = TrainObject(model = mlp,
dataset = data,
learning_rule = learning_rule,
learning_method = learning_method,
log = log)
# finally run the code
train_object.run()
