def run(method, model_path):
model = NeuralClassifier(input_dim=28 * 28)
model.stack(Dense(128, 'relu'), Dense(128, 'relu'), Dense(10, 'linear'),
Softmax())
trainer = ScipyTrainer(model, method)
annealer = LearningRateAnnealer()
mnist = MiniBatches(MnistDataset(), batch_size=100)
trainer.run(mnist, epoch_controllers=[annealer])
model.save_params(model_path)
def run(initializer, model_path):
model = NeuralClassifier(input_dim=28 * 28)
for _ in range(6):
model.stack(Dense(128, 'relu', init=initializer))
model.stack(Dense(10, 'linear'), Softmax())
trainer = MomentumTrainer(model)
annealer = LearningRateAnnealer(trainer)
mnist = MiniBatches(MnistDataset(), batch_size=20)
trainer.run(mnist, controllers=[annealer])
model.save_params(model_path)
def run(method, model_path):
model = NeuralClassifier(input_dim=28 * 28)
model.stack(Dense(128, 'relu'),
Dense(128, 'relu'),
Dense(10, 'linear'),
Softmax())
trainer = ScipyTrainer(model, method)
annealer = LearningRateAnnealer()
mnist = MiniBatches(MnistDataset(), batch_size=100)
trainer.run(mnist, controllers=[annealer])
model.save_params(model_path)
def run(initializer, model_path):
model = NeuralClassifier(input_dim=28 * 28)
for _ in range(6):
model.stack(Dense(128, 'relu', init=initializer))
model.stack(Dense(10, 'linear'),
Softmax())
trainer = MomentumTrainer(model)
annealer = LearningRateAnnealer(trainer)
mnist = MiniBatches(MnistDataset(), batch_size=20)
trainer.run(mnist, controllers=[annealer])
model.save_params(model_path)
expanded_train_set = []
for img, label in mnist.train_set():
expanded_train_set.append((img, label))
original_img = (img * 256).reshape((28, 28))
transformed_img = (elastic_distortion(original_img) / 256).flatten()
expanded_train_set.append((transformed_img, label))
global_rand.shuffle(expanded_train_set)
expanded_mnist = BasicDataset(train=expanded_train_set, valid=mnist.valid_set(), test=mnist.test_set())
logging.info("expanded training data size: %d" % len(expanded_train_set))
if __name__ == '__main__':
model = NeuralClassifier(input_dim=28 * 28)
model.stack(Dense(256, 'relu'),
Dense(256, 'relu'),
Dense(10, 'linear'),
Softmax())
trainer = MomentumTrainer(model)
annealer = LearningRateAnnealer()
mnist = MiniBatches(expanded_mnist, batch_size=20)
trainer.run(mnist, controllers=[annealer])
model.save_params(default_model)
default_model = os.path.join(os.path.dirname(__file__), "models",
"deep_conv.gz")
if __name__ == '__main__':
model = NeuralClassifier(input_dim=28 * 28)
model.stack( # Reshape to 3D tensor
Reshape((-1, 28, 28)),
# Add a new dimension for convolution
DimShuffle((0, 'x', 1, 2)),
Convolution((4, 1, 5, 5), activation="relu"),
Dropout(0.15),
Convolution((8, 4, 5, 5), activation="relu"),
Dropout(0.1),
Convolution((16, 8, 3, 3), activation="relu"),
Flatten(),
Dropout(0.1),
# As dimension information was lost, reveal it to the pipe line
RevealDimension(16),
Dense(10, 'linear'),
Softmax())
trainer = MomentumTrainer(model)
annealer = LearningRateAnnealer()
mnist = MiniBatches(MnistDataset(), batch_size=20)
trainer.run(mnist, controllers=[annealer])
model.save_params(default_model)
Classify MNIST digits using a very deep think network.
Plain deep networks are very hard to be trained, as shown in this case.
But we should notice that if highway layers just learn to pass information forward,
in other words, just be transparent layers, then they would be meaningless.
"""
import logging, os
logging.basicConfig(level=logging.INFO)
from deepy.dataset import MnistDataset, MiniBatches
from deepy.networks import NeuralClassifier
from deepy.layers import Dense, Softmax
from deepy.trainers import MomentumTrainer, LearningRateAnnealer
model_path = os.path.join(os.path.dirname(__file__), "models", "baseline1.gz")
if __name__ == '__main__':
model = NeuralClassifier(input_dim=28 * 28)
for _ in range(20):
model.stack(Dense(71, 'relu'))
model.stack(Dense(10, 'linear'), Softmax())
trainer = MomentumTrainer(model)
mnist = MiniBatches(MnistDataset(), batch_size=20)
trainer.run(mnist, controllers=[LearningRateAnnealer()])
model.save_params(model_path)
import logging, os
logging.basicConfig(level=logging.INFO)
# MNIST Multi-layer model with dropout.
from deepy.dataset import MnistDataset, MiniBatches
from deepy.networks import NeuralClassifier
from deepy.layers import Dense, Softmax, Dropout
from deepy.trainers import MomentumTrainer, LearningRateAnnealer
model_path = os.path.join(os.path.dirname(__file__), "models", "tutorial1.gz")
if __name__ == '__main__':
model = NeuralClassifier(input_dim=28 * 28)
model.stack(Dense(256, 'relu'),
Dropout(0.2),
Dense(256, 'relu'),
Dropout(0.2),
Dense(10, 'linear'),
Softmax())
mnist = MiniBatches(MnistDataset(), batch_size=20)
trainer = MomentumTrainer(model, {"learning_rate": LearningRateAnnealer.learning_rate(0.01)})
annealer = LearningRateAnnealer(trainer)
trainer.run(mnist, controllers=[annealer])
model.save_params(model_path)
