def main(num_epochs=1000):
# MODEL
# defining the data
x = tensor.matrix('features')
y = tensor.lmatrix('targets')
# defining the model
softmax_regressor = SoftmaxRegressor(input_dim=784, n_classes=10)
# defining the cost to learn on
probs = softmax_regressor.get_probs(features=x)
cost = softmax_regressor.get_cost(probs=probs, targets=y).mean()
cost.name = 'cost'
# defining the cost to monitor
misclassification = softmax_regressor.get_misclassification(
probs=probs, targets=y
).mean()
misclassification.name = 'misclassification'
# DATASETS
# defining the datasets
train_dataset = MNIST('train')
test_dataset = MNIST('test')
# TRAINING ALGORITHM
# defining the algorithm
params = softmax_regressor.get_params()
algorithm = GradientDescent(
cost=cost,
params=params,
step_rule=Momentum(learning_rate=0.1,
momentum=0.1))
# defining the data stream
# how the dataset is read
train_data_stream = ForceFloatX(
data_stream=DataStream(
dataset=train_dataset,
iteration_scheme=ShuffledScheme(
examples=train_dataset.num_examples,
batch_size=100,
)
)
)
test_data_stream = ForceFloatX(
data_stream=DataStream(
dataset=test_dataset,
iteration_scheme=SequentialScheme(
examples=test_dataset.num_examples,
batch_size=1000,
)
)
)
# MONITORING
# defining the extensions
extensions = []
# timing the training and each epoch
extensions.append(Timing())
# ending the training after a certain number of epochs
extensions.append(FinishAfter(after_n_epochs=num_epochs))
# monitoring the test set
extensions.append(DataStreamMonitoring(
[cost, misclassification],
test_data_stream,
prefix='test'))
# monitoring the training set while training
extensions.append(TrainingDataMonitoring(
[cost, misclassification],
prefix='train',
after_every_epoch=True))
# printing quantities
extensions.append(Printing())
# MERGING IT TOGETHER
# defining the model
model = Model(cost)
# defining the training main loop
main_loop = MainLoop(model=model,
data_stream=train_data_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()
def main(num_epochs=1000):
x = tensor.matrix('features')
y = tensor.lmatrix('targets')
softmax_regressor = SoftmaxRegressor(input_dim=784, n_classes=10)
probs = softmax_regressor.get_probs(features=x)
params = softmax_regressor.get_params()
weights = softmax_regressor.get_weights()
cost = softmax_regressor.get_cost(probs=probs, targets=y).mean()
cost.name = 'cost'
misclassification = softmax_regressor.get_misclassification(
probs=probs, targets=y
).mean()
misclassification.name = 'misclassification'
train_dataset = MNIST('train')
test_dataset = MNIST('test')
algorithm = GradientDescent(
cost=cost,
params=params,
step_rule=Momentum(learning_rate=0.1,
momentum=0.1))
train_data_stream = ForceFloatX(
data_stream=DataStream(
dataset=train_dataset,
iteration_scheme=ShuffledScheme(
examples=train_dataset.num_examples,
batch_size=100,
)
)
)
test_data_stream = ForceFloatX(
data_stream=DataStream(
dataset=test_dataset,
iteration_scheme=SequentialScheme(
examples=test_dataset.num_examples,
batch_size=1000,
)
)
)
model = Model(cost)
extensions = []
extensions.append(Timing())
extensions.append(FinishAfter(after_n_epochs=num_epochs))
extensions.append(DataStreamMonitoring(
[cost, misclassification],
test_data_stream,
prefix='test'))
extensions.append(TrainingDataMonitoring(
[cost, misclassification],
prefix='train',
after_epoch=True))
plotters = []
plotters.append(Plotter(
channels=[['test_cost', 'test_misclassification',
'train_cost', 'train_misclassification']],
titles=['Costs']))
display_train = ImageDataStreamDisplay(
data_stream=copy.deepcopy(train_data_stream),
image_shape=(28, 28, 1),
axes=(0, 1, 'c'),
shift=-0.5,
rescale=2.,
)
weight_display = WeightDisplay(
weights=weights,
transpose=(1, 0),
image_shape=(28, 28, 1),
axes=(0, 1, 'c'),
shift=-0.5,
rescale=2.,
grid_shape=(1, 10)
)
images_displayer = DisplayImage(
image_getters=[display_train, weight_display],
titles=['Training examples', 'Softmax weights']
)
plotters.append(images_displayer)
extensions.append(PlotManager('MNIST softmax examples', plotters=plotters,
after_epoch=False,
every_n_epochs=10,
after_training=True))
extensions.append(Printing())
main_loop = MainLoop(model=model,
data_stream=train_data_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()
def main(num_epochs=1000):
x = tensor.matrix('features')
y = tensor.lmatrix('targets')
softmax_regressor = SoftmaxRegressor(input_dim=784, n_classes=10)
probs = softmax_regressor.get_probs(features=x)
params = softmax_regressor.get_params()
weights = softmax_regressor.get_weights()
cost = softmax_regressor.get_cost(probs=probs, targets=y).mean()
cost.name = 'cost'
misclassification = softmax_regressor.get_misclassification(
probs=probs, targets=y).mean()
misclassification.name = 'misclassification'
train_dataset = MNIST('train')
test_dataset = MNIST('test')
algorithm = GradientDescent(cost=cost,
params=params,
step_rule=Momentum(learning_rate=0.1,
momentum=0.1))
train_data_stream = ForceFloatX(
data_stream=DataStream(dataset=train_dataset,
iteration_scheme=ShuffledScheme(
examples=train_dataset.num_examples,
batch_size=100,
)))
test_data_stream = ForceFloatX(
data_stream=DataStream(dataset=test_dataset,
iteration_scheme=SequentialScheme(
examples=test_dataset.num_examples,
batch_size=1000,
)))
model = Model(cost)
extensions = []
extensions.append(Timing())
extensions.append(FinishAfter(after_n_epochs=num_epochs))
extensions.append(
DataStreamMonitoring([cost, misclassification],
test_data_stream,
prefix='test'))
extensions.append(
TrainingDataMonitoring([cost, misclassification],
prefix='train',
after_epoch=True))
plotters = []
plotters.append(
Plotter(channels=[[
'test_cost', 'test_misclassification', 'train_cost',
'train_misclassification'
]],
titles=['Costs']))
display_train = ImageDataStreamDisplay(
data_stream=copy.deepcopy(train_data_stream),
image_shape=(28, 28, 1),
axes=(0, 1, 'c'),
shift=-0.5,
rescale=2.,
)
weight_display = WeightDisplay(weights=weights,
transpose=(1, 0),
image_shape=(28, 28, 1),
axes=(0, 1, 'c'),
shift=-0.5,
rescale=2.,
grid_shape=(1, 10))
images_displayer = DisplayImage(
image_getters=[display_train, weight_display],
titles=['Training examples', 'Softmax weights'])
plotters.append(images_displayer)
extensions.append(
PlotManager('MNIST softmax examples',
plotters=plotters,
after_epoch=False,
every_n_epochs=10,
after_training=True))
extensions.append(Printing())
main_loop = MainLoop(model=model,
data_stream=train_data_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()
def main(num_epochs=1000):
# MODEL
# defining the data
x = tensor.matrix('features')
y = tensor.lmatrix('targets')
# defining the model
softmax_regressor = SoftmaxRegressor(input_dim=784, n_classes=10)
# defining the cost to learn on
probs = softmax_regressor.get_probs(features=x)
cost = softmax_regressor.get_cost(probs=probs, targets=y).mean()
cost.name = 'cost'
# defining the cost to monitor
misclassification = softmax_regressor.get_misclassification(
probs=probs, targets=y).mean()
misclassification.name = 'misclassification'
# DATASETS
# defining the datasets
train_dataset = MNIST('train')
test_dataset = MNIST('test')
# TRAINING ALGORITHM
# defining the algorithm
params = softmax_regressor.get_params()
algorithm = GradientDescent(cost=cost,
params=params,
step_rule=Momentum(learning_rate=0.1,
momentum=0.1))
# defining the data stream
# how the dataset is read
train_data_stream = ForceFloatX(
data_stream=DataStream(dataset=train_dataset,
iteration_scheme=ShuffledScheme(
examples=train_dataset.num_examples,
batch_size=100,
)))
test_data_stream = ForceFloatX(
data_stream=DataStream(dataset=test_dataset,
iteration_scheme=SequentialScheme(
examples=test_dataset.num_examples,
batch_size=1000,
)))
# MONITORING
# defining the extensions
extensions = []
# timing the training and each epoch
extensions.append(Timing())
# ending the training after a certain number of epochs
extensions.append(FinishAfter(after_n_epochs=num_epochs))
# monitoring the test set
extensions.append(
DataStreamMonitoring([cost, misclassification],
test_data_stream,
prefix='test'))
# monitoring the training set while training
extensions.append(
TrainingDataMonitoring([cost, misclassification],
prefix='train',
after_every_epoch=True))
# printing quantities
extensions.append(Printing())
# MERGING IT TOGETHER
# defining the model
model = Model(cost)
# defining the training main loop
main_loop = MainLoop(model=model,
data_stream=train_data_stream,
algorithm=algorithm,
extensions=extensions)
main_loop.run()