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(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 _initialize_impl(self, X, y=None):
assert not self.is_initialized,\
"This neural network has already been initialized."
self._create_specs(X, y)
self._create_mlp()
if y is None:
return
if self.valid_size > 0.0:
assert self.valid_set is None, "Can't specify valid_size and valid_set together."
X, X_v, y, y_v = sklearn.cross_validation.train_test_split(
X, y,
test_size=self.valid_size,
random_state=self.random_state)
self.valid_set = X_v, y_v
self.train_set = X, y
self.trainer = MomentumTrainer(self.mlp)
self.controllers = [
self,
LearningRateAnnealer(self.trainer, patience=self.n_stable, anneal_times=0)]
def _initialize(self, X, y=None):
assert not self.is_initialized,\
"This neural network has already been initialized."
self._create_specs(X, y)
self._create_mlp()
if y is None:
return
if self.valid_size > 0.0:
assert self.valid_set is None, "Can't specify valid_size and valid_set together."
X, X_v, y, y_v = sklearn.cross_validation.train_test_split(
X, y,
test_size=self.valid_size,
random_state=self.random_state)
self.valid_set = X_v, y_v
self.train_set = X, y
self.trainer = MomentumTrainer(self.mlp)
self.controllers = [
self,
LearningRateAnnealer(self.trainer, patience=self.n_stable, anneal_times=0)]
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)
decoding_output = self.decoder.output(internal_variable)
classification_output = self.classifier.output(internal_variable)
auto_encoder_cost = AutoEncoderCost(decoding_output, x).get()
classification_cost = CrossEntropyCost(classification_output,
self.target_input).get()
final_cost = 0.01 * auto_encoder_cost + classification_cost
error_rate = ErrorRateCost(classification_output,
self.target_input).get()
self.register_monitors(("err", error_rate),
("encoder_cost", auto_encoder_cost),
("classify_cost", classification_cost))
return final_cost
if __name__ == '__main__':
model = BasicNetwork(input_dim=28 * 28, model=MyJointTrainingModel())
mnist = MiniBatches(MnistDataset(), batch_size=20)
trainer = MomentumTrainer(model, {'weight_l2': 0.0001})
trainer.run(mnist, controllers=[LearningRateAnnealer(trainer)])
model.save_params(model_path)
L2NORM_LIMIT = 1.9365
EPSILON = 1e-7
def clip_param_norm():
for param in model.parameters:
if param.name.startswith("W"):
l2_norms = np.sqrt(np.sum(param.get_value() ** 2, axis=0, keepdims=True))
desired_norms = np.clip(l2_norms, 0, L2NORM_LIMIT)
scale = (desired_norms + EPSILON) / (l2_norms + EPSILON)
param.set_value(param.get_value() * scale)
if __name__ == '__main__':
model = NeuralClassifier(input_dim=28 * 28)
model.training_callbacks.append(clip_param_norm)
model.stack(Dropout(0.2),
Maxout(240, num_pieces=5, init=UniformInitializer(.005)),
Maxout(240, num_pieces=5, init=UniformInitializer(.005)),
Dense(10, 'linear', init=UniformInitializer(.005)),
Softmax())
trainer = MomentumTrainer(model, {"learning_rate": shared_scalar(0.01),
"momentum": 0.5})
annealer = ExponentialLearningRateAnnealer(trainer, debug=True)
mnist = MiniBatches(MnistDataset(), batch_size=100)
trainer.run(mnist, controllers=[annealer])
model.save_params(default_model)
decoding_output = self.decoder.output(internal_variable)
classification_output = self.classifier.output(internal_variable)
auto_encoder_cost = AutoEncoderCost(decoding_output, x).get()
classification_cost = CrossEntropyCost(classification_output, self.target_input).get()
final_cost = 0.01 * auto_encoder_cost + classification_cost
error_rate = ErrorRateCost(classification_output, self.target_input).get()
self.register_monitors(
("err", error_rate), ("encoder_cost", auto_encoder_cost), ("classify_cost", classification_cost)
)
return final_cost
if __name__ == "__main__":
model = BasicNetwork(input_dim=28 * 28, model=MyJointTrainingModel())
mnist = MiniBatches(MnistDataset(), batch_size=20)
trainer = MomentumTrainer(model, {"weight_l2": 0.0001})
trainer.run(mnist, controllers=[LearningRateAnnealer(trainer)])
model.save_params(model_path)
class MultiLayerPerceptron(NeuralNetwork):
"""
Abstract base class for wrapping the multi-layer perceptron functionality
from ``deepy``.
"""
def _setup(self):
self.iterations = 0
self.trainer = None
self.mlp = None
@property
def is_convolution(self):
return False
def _create_mlp_trainer(self, dataset):
# Aggregate all the dropout parameters into shared dictionaries.
dropout_probs, dropout_scales = {}, {}
for l in [l for l in self.layers if l.dropout is not None]:
incl = 1.0 - l.dropout
dropout_probs[l.name] = incl
dropout_scales[l.name] = 1.0 / incl
assert len(dropout_probs) == 0 or self.regularize in ('dropout', None)
if self.regularize == 'dropout' or len(dropout_probs) > 0:
# Use the globally specified dropout rate when there are no layer-specific ones.
incl = 1.0 - (self.dropout_rate or 0.5)
default_prob, default_scale = incl, 1.0 / incl
self.regularize = 'dropout'
# Pass all the parameters to pylearn2 as a custom cost function.
self.cost = dropout.Dropout(
default_input_include_prob=default_prob,
default_input_scale=default_scale,
input_include_probs=dropout_probs, input_scales=dropout_scales)
# Aggregate all regularization parameters into common dictionaries.
layer_decay = {}
if self.regularize in ('L1', 'L2') or any(l.weight_decay for l in self.layers):
wd = self.weight_decay or 0.0001
for l in self.layers:
layer_decay[l.name] = l.weight_decay or wd
assert len(layer_decay) == 0 or self.regularize in ('L1', 'L2', None)
if len(layer_decay) > 0:
mlp_default_cost = self.mlp.get_default_cost()
if self.regularize == 'L1':
l1 = mlp_cost.L1WeightDecay(layer_decay)
self.cost = cost.SumOfCosts([mlp_default_cost,l1])
else: # Default is 'L2'.
self.regularize = 'L2'
l2 = mlp_cost.WeightDecay(layer_decay)
self.cost = cost.SumOfCosts([mlp_default_cost,l2])
return self._create_trainer(dataset, self.cost)
def _create_mlp(self):
model = NeuralRegressor(input_dim=self.unit_counts[0])
for l, n in zip(self.layers, self.unit_counts[1:]):
t = 'relu'
if l.type == 'Rectifier': t = 'relu'
if l.type == 'Linear': t = 'linear'
model.stack_layer(Dense(n, t))
model.stack_layer(Softmax())
self.mlp = model
def _initialize(self, X, y=None):
assert not self.is_initialized,\
"This neural network has already been initialized."
self._create_specs(X, y)
self._create_mlp()
if y is None:
return
if self.valid_size > 0.0:
assert self.valid_set is None, "Can't specify valid_size and valid_set together."
X, X_v, y, y_v = sklearn.cross_validation.train_test_split(
X, y,
test_size=self.valid_size,
random_state=self.random_state)
self.valid_set = X_v, y_v
self.train_set = X, y
self.trainer = MomentumTrainer(self.mlp)
self.controllers = [
self,
LearningRateAnnealer(self.trainer, patience=self.n_stable, anneal_times=0)]
def invoke(self):
"""Controller interface for deepy's trainer.
"""
self.iterations += 1
return bool(self.iterations >= self.n_iter)
@property
def is_initialized(self):
"""Check if the neural network was setup already.
"""
return self.trainer is not None
def _reshape(self, X, y=None):
# TODO: Common for all backends.
if y is not None and y.ndim == 1:
y = y.reshape((y.shape[0], 1))
if self.is_convolution and X.ndim == 3:
X = X.reshape((X.shape[0], X.shape[1], X.shape[2], 1))
if self.is_convolution and X.ndim == 2:
size = math.sqrt(X.shape[1])
assert size.is_integer(),\
"Input array is not in image shape, and could not assume a square."
X = X.reshape((X.shape[0], int(size), int(size), 1))
if not self.is_convolution and X.ndim > 2:
X = X.reshape((X.shape[0], numpy.product(X.shape[1:])))
return X, y
def _train_impl(self, X, y):
self.iterations = 0
data = zip(X, y)
self.dataset = SequentialDataset(data)
minibatches = MiniBatches(self.dataset, batch_size=20)
self.trainer.run(minibatches, controllers=self.controllers)
return self
def _predict_impl(self, X):
return self.mlp.compute(X)
def _mlp_to_array(self):
return []
def _array_to_mlp(self, array):
pass
#!/usr/bin/env python
# -*- coding: utf-8 -*-
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
default_model = os.path.join(os.path.dirname(__file__), "models", "mlp1.gz")
if __name__ == '__main__':
model = NeuralClassifier(input_dim=28 * 28)
model.stack(Dense(256, 'relu'), Dense(256, 'relu'), Dense(10, 'linear'),
Softmax())
trainer = MomentumTrainer(model, {"weight_l2": 0.001})
annealer = LearningRateAnnealer(trainer)
mnist = MiniBatches(MnistDataset(), batch_size=20)
trainer.run(mnist, controllers=[annealer])
model.save_params(default_model)
def clip_param_norm():
for param in model.parameters:
if param.name.startswith("W"):
l2_norms = np.sqrt(
np.sum(param.get_value()**2, axis=0, keepdims=True))
desired_norms = np.clip(l2_norms, 0, L2NORM_LIMIT)
scale = (desired_norms + EPSILON) / (l2_norms + EPSILON)
param.set_value(param.get_value() * scale)
if __name__ == '__main__':
model = NeuralClassifier(input_dim=28 * 28)
model.training_callbacks.append(clip_param_norm)
model.stack(Dropout(0.2),
Maxout(240, num_pieces=5, init=UniformInitializer(.005)),
Maxout(240, num_pieces=5, init=UniformInitializer(.005)),
Dense(10, 'linear', init=UniformInitializer(.005)), Softmax())
trainer = MomentumTrainer(model, {
"learning_rate": graph.shared(0.01),
"momentum": 0.5
})
annealer = ExponentialLearningRateAnnealer(debug=True)
mnist = MiniBatches(MnistDataset(), batch_size=100)
trainer.run(mnist, epoch_controllers=[annealer])
model.save_params(default_model)
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)
import os
logging.basicConfig(level=logging.INFO)
from deepy.dataset import MiniBatches,BasicDataset
from deepy.networks import NeuralClassifier
from deepy.layers import Dense, Softmax
from deepy.trainers import MomentumTrainer, LearningRateAnnealer
from SynthDataset import SynthDataset
model_path = os.path.join(os.path.dirname(__file__), "models", "model_10000_op.gz")
if __name__ == '__main__':
model = NeuralClassifier(input_dim=32*32)
model.stack(Dense(400, 'tanh'),
Dense(100, 'tanh'),
Dense(3, 'linear'),
Softmax())
#trainer = MomentumTrainer(model, {"weight_l2": 0.01})
trainer = MomentumTrainer(model, {"learning_rate": LearningRateAnnealer.learning_rate(0.001)})
annealer = LearningRateAnnealer(trainer)
mlp_synthDataSet = MiniBatches(SynthDataset())
trainer.run(mlp_synthDataSet, controllers=[annealer])
model.save_params(model_path)
class MultiLayerPerceptronBackend(BaseBackend):
"""
Abstract base class for wrapping the multi-layer perceptron functionality
from ``deepy``.
"""
def __init__(self, spec):
super(MultiLayerPerceptronBackend, self).__init__(spec)
self.iterations = 0
self.trainer = None
self.mlp = None
l = logging.getLogger('deepy')
l.setLevel(logging.WARNING)
@property
def is_convolution(self):
return False
def _create_mlp(self):
model = NeuralRegressor(input_dim=self.unit_counts[0])
initializer = UniformInitializer(seed=self.random_state)
if self.spec.is_convolution:
model.stack_layer(layers.DimShuffle((0, 'x', 1, 2)))
for l, n in zip(self.layers, self.unit_counts[1:]):
t = None
if l.type in ('Tanh', 'Sigmoid'): t = l.type.lower()
if l.type in ('Rectifier'): t = 'relu'
if l.type in ('Linear', 'Softmax'): t = 'linear'
assert t is not None, "Unknown activation type `%s`." % l.type
if isinstance(l, Layer):
# self._check_layer(l, ['units'])
model.stack_layer(layers.Dense(n, t, init=initializer))
if l.type == 'Softmax':
model.stack_layer(layers.Softmax())
if isinstance(l, layers.Convolution):
# self._check_layer(l, ['channel', 'kernel_shape'])
model.stack_layer(layers.Convolution(
activation=t,
filter_shape=(l.channels, l.kernel_shape[0], l.kernel_shape[1]),
pool_size=l.pool_shape,
border_mode=l.border_mode,
init=initializer))
self.mlp = model
def _initialize_impl(self, X, y=None):
assert not self.is_initialized,\
"This neural network has already been initialized."
self._create_specs(X, y)
self._create_mlp()
if y is None:
return
if self.valid_size > 0.0:
assert self.valid_set is None, "Can't specify valid_size and valid_set together."
X, X_v, y, y_v = sklearn.cross_validation.train_test_split(
X, y,
test_size=self.valid_size,
random_state=self.random_state)
self.valid_set = X_v, y_v
self.train_set = X, y
self.trainer = MomentumTrainer(self.mlp)
self.controllers = [
self,
LearningRateAnnealer(self.trainer, patience=self.n_stable, anneal_times=0)]
def invoke(self):
"""Controller interface for deepy's trainer.
"""
self.iterations += 1
return bool(self.iterations >= self.n_iter)
@property
def is_initialized(self):
"""Check if the neural network was setup already.
"""
return self.trainer is not None
def _train_impl(self, X, y):
if self.spec.is_convolution:
X = X.reshape(X.shape[:3])
self.iterations = 0
data = zip(X, y)
self.dataset = SequentialDataset(data)
minibatches = MiniBatches(self.dataset, batch_size=20)
self.trainer.run(minibatches, controllers=self.controllers)
return self
def _predict_impl(self, X):
return self.mlp.compute(X)
def _mlp_to_array(self):
return []
def _array_to_mlp(self, array):
pass
