def __init__(self):
Dataset.__init__(self)
# Initialize the additional containers
self.vx = None
self.file_ids = None
def run(self):
###### Create the datasets
training_data_path = self.data_path + "train.h5"
testing_data_path = self.data_path + "test.h5"
# If files don't already exist, create them
if not os.path.isfile(training_data_path):
transform_mnist_to_h5()
prop_validation = 0.3 # Percentage of the training dataset that is used for validation (early stopping)
ds_training = Dataset.create_and_read(training_data_path)
ds_validation, ds_training = ds_training.split_dataset_proportions(
[prop_validation, 1 - prop_validation])
ds_testing = Dataset.create_and_read(testing_data_path)
# Few stats about the targets
analyse_classes(np.argmax(ds_training.outputs, axis=1),
"Training data")
# Scale the data
s = Scaler([slice(None, None)])
s.compute_parameters(ds_training.inputs)
s.scale(ds_training.inputs)
s.scale(ds_validation.inputs)
s.scale(ds_testing.inputs)
###### Create the network
net = NetworkMNIST()
net.init(28, 28, 10)
print net
###### Configure the trainer
# Cost function
cost_function = CostNegLL()
# Learning update
learning_rate = 0.13
momentum = 0.5
lr_update = LearningUpdateGDMomentum(learning_rate, momentum)
# Create monitors and add them to the trainer
err_training = MonitorErrorRate(1, "Training", ds_training)
err_testing = MonitorErrorRate(1, "Testing", ds_testing)
err_validation = MonitorErrorRate(1, "Validation", ds_validation)
# Create stopping criteria and add them to the trainer
max_epoch = MaxEpoch(300)
early_stopping = EarlyStopping(err_validation)
# Create the network selector
params_selector = ParamSelectorBestMonitoredValue(err_validation)
# Create the trainer object
batch_size = 200
t = Trainer(net, cost_function, params_selector,
[max_epoch, early_stopping], lr_update, ds_training,
batch_size, [err_training, err_testing, err_validation])
###### Train the network
t.train()
###### Plot the records
save_records_plot(self.path,
[err_training, err_testing, err_validation],
"errors", t.n_train_batches)
###### Save the network
net.save_parameters(self.path + "net.net")
def run(self):
###### Create the datasets
training_data_path = self.data_path + "train.h5"
testing_data_path = self.data_path + "test.h5"
# If files don't already exist, create them
if not os.path.isfile(training_data_path):
transform_mnist_to_h5()
prop_validation = 0.15 # Percentage of the training dataset that is used for validation (early stopping)
ds_training = Dataset.create_and_read(training_data_path)
ds_validation, ds_training = ds_training.split_dataset_proportions([prop_validation, 1-prop_validation])
ds_testing = Dataset.create_and_read(testing_data_path)
ds_training.outputs = ds_training.inputs
ds_validation.outputs = ds_validation.inputs
ds_testing.outputs = ds_testing.inputs
# Scale the data
# s = Scaler([slice(None, None)])
# s.compute_parameters(ds_training.inputs)
# s.scale(ds_training.inputs)
# s.scale(ds_validation.inputs)
# s.scale(ds_testing.inputs)
###### Create the network
# net = NetworkMNIST()
net = AutoEncoder()
net.init([28**2, 256, 28**2], dropout=True, dropout_p=[0.5], neuron_function=NeuronSigmoid())
print net
###### Configure the trainer
# Cost function
cost_function = CostMSE()
# Learning update
learning_rate = 0.01
momentum = 0.5
lr_update = LearningUpdateGDMomentum(learning_rate, momentum)
# Create monitors and add them to the trainer
err_training = MonitorMSE(1, "Training", ds_training)
err_testing = MonitorMSE(1, "Testing", ds_testing)
err_validation = MonitorMSE(1, "Validation", ds_validation)
# Create stopping criteria and add them to the trainer
max_epoch = MaxEpoch(50)
early_stopping = EarlyStopping(err_validation)
# Create the network selector
params_selector = ParamSelectorBestMonitoredValue(err_validation)
# Create the trainer object
batch_size = 20
t = Trainer(net, cost_function, params_selector, [max_epoch, early_stopping],
lr_update, ds_training, batch_size,
[err_training, err_testing, err_validation])
###### Train the network
t.train()
###### Plot the records
save_records_plot(self.path, [err_training, err_testing, err_validation], "errors", t.n_train_batches)
###### Save the network
net.save_parameters(self.path + "netdrop.net")
def run(self):
###### Create the datasets
training_data_path = self.data_path + "train.h5"
testing_data_path = self.data_path + "test.h5"
# If files don't already exist, create them
if not os.path.isfile(training_data_path):
transform_mnist_to_h5()
prop_validation = 0.15 # Percentage of the training dataset that is used for validation (early stopping)
ds_training = Dataset.create_and_read(training_data_path)
ds_validation, ds_training = ds_training.split_dataset_proportions(
[prop_validation, 1 - prop_validation])
ds_testing = Dataset.create_and_read(testing_data_path)
ds_training.outputs = ds_training.inputs
ds_validation.outputs = ds_validation.inputs
ds_testing.outputs = ds_testing.inputs
# Scale the data
# s = Scaler([slice(None, None)])
# s.compute_parameters(ds_training.inputs)
# s.scale(ds_training.inputs)
# s.scale(ds_validation.inputs)
# s.scale(ds_testing.inputs)
###### Create the network
# net = NetworkMNIST()
net = AutoEncoder()
net.init([28**2, 256, 28**2],
dropout=True,
dropout_p=[0.5],
neuron_function=NeuronSigmoid())
print net
###### Configure the trainer
# Cost function
cost_function = CostMSE()
# Learning update
learning_rate = 0.01
momentum = 0.5
lr_update = LearningUpdateGDMomentum(learning_rate, momentum)
# Create monitors and add them to the trainer
err_training = MonitorMSE(1, "Training", ds_training)
err_testing = MonitorMSE(1, "Testing", ds_testing)
err_validation = MonitorMSE(1, "Validation", ds_validation)
# Create stopping criteria and add them to the trainer
max_epoch = MaxEpoch(50)
early_stopping = EarlyStopping(err_validation)
# Create the network selector
params_selector = ParamSelectorBestMonitoredValue(err_validation)
# Create the trainer object
batch_size = 20
t = Trainer(net, cost_function, params_selector,
[max_epoch, early_stopping], lr_update, ds_training,
batch_size, [err_training, err_testing, err_validation])
###### Train the network
t.train()
###### Plot the records
save_records_plot(self.path,
[err_training, err_testing, err_validation],
"errors", t.n_train_batches)
###### Save the network
net.save_parameters(self.path + "netdrop.net")
from spynet.models.network import *
from spynet.models.neuron_type import *
from spynet.data.dataset import Dataset
from spynet.utils.utilities import open_h5file, tile_raster_images, MSE
if __name__ == '__main__':
mode = "drop"
experiment_path = "./experiments/mnist_example/"
data_path = "./datasets/mnist/"
testing_data_path = data_path + "test.h5"
ds_testing = Dataset.create_and_read(testing_data_path)
# Load the network
net = AutoEncoder()
net.init([28**2, 256, 28**2], dropout=True, dropout_p=[0.5], neuron_function=NeuronSigmoid())
net.load_parameters(open_h5file(experiment_path + "netdrop.net"))
i = ds_testing.inputs[0:10,:]
e = net.predict(i, 10)
print ""
print MSE(e,i)
image = PIL.Image.fromarray(tile_raster_images(X=net.ls_layers[0].ls_layer_blocks[0].w.get_value(borrow=True).T,
img_shape=(28, 28), tile_shape=(16, 16),
tile_spacing=(1, 1)))
def __init__(self):
Dataset.__init__(self)
# Initialize the additional containers
self.vx = None
self.file_ids = None
import PIL
from spynet.models.network import *
from spynet.models.neuron_type import *
from spynet.data.dataset import Dataset
from spynet.utils.utilities import open_h5file, tile_raster_images, MSE
if __name__ == '__main__':
mode = "drop"
experiment_path = "./experiments/mnist_example/"
data_path = "./datasets/mnist/"
testing_data_path = data_path + "test.h5"
ds_testing = Dataset.create_and_read(testing_data_path)
# Load the network
net = AutoEncoder()
net.init([28**2, 256, 28**2],
dropout=True,
dropout_p=[0.5],
neuron_function=NeuronSigmoid())
net.load_parameters(open_h5file(experiment_path + "netdrop.net"))
i = ds_testing.inputs[0:10, :]
e = net.predict(i, 10)
print ""
print MSE(e, i)
def run(self):
###### Create the datasets
training_data_path = self.data_path + "train.h5"
testing_data_path = self.data_path + "test.h5"
# If files don't already exist, create them
if not os.path.isfile(training_data_path):
transform_mnist_to_h5()
prop_validation = 0.3 # Percentage of the training dataset that is used for validation (early stopping)
ds_training = Dataset.create_and_read(training_data_path)
ds_validation, ds_training = ds_training.split_dataset_proportions([prop_validation, 1-prop_validation])
ds_testing = Dataset.create_and_read(testing_data_path)
# Few stats about the targets
analyse_classes(np.argmax(ds_training.outputs, axis=1), "Training data")
# Scale the data
s = Scaler([slice(None, None)])
s.compute_parameters(ds_training.inputs)
s.scale(ds_training.inputs)
s.scale(ds_validation.inputs)
s.scale(ds_testing.inputs)
###### Create the network
net = NetworkMNIST()
net.init(28, 28, 10)
print net
###### Configure the trainer
# Cost function
cost_function = CostNegLL()
# Learning update
learning_rate = 0.13
momentum = 0.5
lr_update = LearningUpdateGDMomentum(learning_rate, momentum)
# Create monitors and add them to the trainer
err_training = MonitorErrorRate(1, "Training", ds_training)
err_testing = MonitorErrorRate(1, "Testing", ds_testing)
err_validation = MonitorErrorRate(1, "Validation", ds_validation)
# Create stopping criteria and add them to the trainer
max_epoch = MaxEpoch(300)
early_stopping = EarlyStopping(err_validation)
# Create the network selector
params_selector = ParamSelectorBestMonitoredValue(err_validation)
# Create the trainer object
batch_size = 200
t = Trainer(net, cost_function, params_selector, [max_epoch, early_stopping],
lr_update, ds_training, batch_size,
[err_training, err_testing, err_validation])
###### Train the network
t.train()
###### Plot the records
save_records_plot(self.path, [err_training, err_testing, err_validation], "errors", t.n_train_batches)
###### Save the network
net.save_parameters(self.path + "net.net")
