def run():
# Prepare data
dataset = dp.datasets.MNIST()
x, y = dataset.data(flat=True)
x = x.astype(dp.float_)
y = y.astype(dp.int_)
train_idx, test_idx = dataset.split()
x_train = x[train_idx]
y_train = y[train_idx]
x_test = x[test_idx]
y_test = y[test_idx]
scaler = dp.UniformScaler(high=255.)
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
batch_size = 128
train_input = dp.SupervisedInput(x_train, y_train, batch_size=batch_size)
test_input = dp.SupervisedInput(x_test, y_test)
# Setup neural network
net = dp.NeuralNetwork(
layers=[
dp.FullyConnected(
n_output=800,
weights=dp.Parameter(dp.AutoFiller(), weight_decay=0.0001),
),
dp.Activation('relu'),
dp.FullyConnected(
n_output=800,
weights=dp.Parameter(dp.AutoFiller(), weight_decay=0.0001),
),
dp.Activation('relu'),
dp.FullyConnected(
n_output=dataset.n_classes,
weights=dp.Parameter(dp.AutoFiller(), weight_decay=0.0001),
),
dp.MultinomialLogReg(),
],
)
# Train neural network
def val_error():
return net.error(test_input)
trainer = dp.StochasticGradientDescent(
max_epochs=25,
learn_rule=dp.Momentum(learn_rate=0.1, momentum=0.9),
)
trainer.train(net, train_input, val_error)
# Visualize weights from first layer
W = next(np.array(layer.params()[0].array) for layer in net.layers
if isinstance(layer, dp.FullyConnected))
W = np.reshape(W.T, (-1, 28, 28))
filepath = os.path.join('mnist', 'mlp_weights.png')
dp.misc.img_save(dp.misc.img_tile(dp.misc.img_stretch(W)), filepath)
# Evaluate on test data
error = net.error(test_input)
print('Test error rate: %.4f' % error)
def run():
# Prepare data
dataset = dp.datasets.MNIST()
x, y = dataset.data(flat=True)
x = x.astype(dp.float_)/255.0
y = y.astype(dp.int_)
train_idx, test_idx = dataset.split()
x_train = x[train_idx]
y_train = y[train_idx]
x_test = x[test_idx]
y_test = y[test_idx]
train_input = dp.SupervisedInput(x_train, y_train, batch_size=128)
test_input = dp.SupervisedInput(x_test, y_test)
# Setup neural network
nn = dp.NeuralNetwork(
layers=[
dp.Dropout(0.2),
dp.DropoutFullyConnected(
n_output=800,
dropout=0.5,
weights=dp.Parameter(dp.NormalFiller(sigma=0.01),
penalty=('l2', 0.00001), monitor=True),
),
dp.Activation('relu'),
dp.DropoutFullyConnected(
n_output=800,
dropout=0.5,
weights=dp.Parameter(dp.NormalFiller(sigma=0.01),
penalty=('l2', 0.00001), monitor=True),
),
dp.Activation('relu'),
dp.DropoutFullyConnected(
n_output=dataset.n_classes,
weights=dp.Parameter(dp.NormalFiller(sigma=0.01),
penalty=('l2', 0.00001), monitor=True),
),
dp.MultinomialLogReg(),
],
)
# Train neural network
def valid_error():
return nn.error(test_input)
trainer = dp.StochasticGradientDescent(
max_epochs=50,
learn_rule=dp.Momentum(learn_rate=0.1, momentum=0.9),
)
trainer.train(nn, train_input, valid_error)
# Visualize weights from first layer
W = next(np.array(layer.params()[0].values) for layer in nn.layers
if isinstance(layer, dp.FullyConnected))
W = np.reshape(W.T, (-1, 28, 28))
dp.misc.img_save(dp.misc.img_tile(dp.misc.img_stretch(W)),
os.path.join('mnist', 'mlp_dropout_weights.png'))
# Evaluate on test data
error = nn.error(test_input)
print('Test error rate: %.4f' % error)
def run():
# Prepare data
dataset = dp.datasets.MNIST()
x, y = dataset.data()
x = x.astype(dp.float_)[:, np.newaxis, :, :]
y = y.astype(dp.int_)
train_idx, test_idx = dataset.split()
x_train = x[train_idx]
y_train = y[train_idx]
x_test = x[test_idx]
y_test = y[test_idx]
scaler = dp.UniformScaler(high=255.)
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
batch_size = 128
train_input = dp.SupervisedInput(x_train, y_train, batch_size=batch_size)
test_input = dp.SupervisedInput(x_test, y_test)
# Setup neural network
net = dp.NeuralNetwork(layers=[
dp.Convolutional(
n_filters=32,
filter_shape=(5, 5),
weights=dp.Parameter(dp.AutoFiller(), weight_decay=0.0001),
),
dp.Activation('relu'),
dp.Pool(
win_shape=(3, 3),
strides=(2, 2),
method='max',
),
dp.Convolutional(
n_filters=64,
filter_shape=(5, 5),
weights=dp.Parameter(dp.AutoFiller(), weight_decay=0.0001),
),
dp.Activation('relu'),
dp.Pool(
win_shape=(3, 3),
strides=(2, 2),
method='max',
),
dp.Flatten(),
dp.FullyConnected(
n_output=128,
weights=dp.Parameter(dp.AutoFiller()),
),
dp.FullyConnected(
n_output=dataset.n_classes,
weights=dp.Parameter(dp.AutoFiller()),
),
dp.MultinomialLogReg(),
], )
# Train neural network
def val_error():
return net.error(test_input)
trainer = dp.StochasticGradientDescent(
max_epochs=15,
learn_rule=dp.Momentum(learn_rate=0.01, momentum=0.9),
)
trainer.train(net, train_input, val_error)
# Visualize convolutional filters to disk
for l, layer in enumerate(net.layers):
if not isinstance(layer, dp.Convolutional):
continue
W = np.array(layer.params()[0].array)
filepath = os.path.join('mnist', 'conv_layer_%i.png' % l)
dp.misc.img_save(dp.misc.conv_filter_tile(W), filepath)
# Evaluate on test data
error = net.error(test_input)
print('Test error rate: %.4f' % error)
def run():
# Prepare data
batch_size = 128
dataset = dp.datasets.CIFAR10()
x, y = dataset.data()
y = y.astype(dp.int_)
train_idx, test_idx = dataset.split()
x_train = preprocess_imgs(x[train_idx])
y_train = y[train_idx]
train_input = dp.SupervisedInput(x_train, y_train, batch_size=batch_size)
# Setup neural network
pool_kwargs = {
'win_shape': (3, 3),
'strides': (2, 2),
'border_mode': 'same',
'method': 'max',
}
nn = dp.NeuralNetwork(layers=[
dp.Convolutional(
n_filters=32,
filter_shape=(5, 5),
border_mode='same',
weights=dp.Parameter(dp.NormalFiller(sigma=0.0001),
penalty=('l2', 0.004),
monitor=True),
),
dp.Activation('relu'),
dp.Pool(**pool_kwargs),
dp.Convolutional(
n_filters=32,
filter_shape=(5, 5),
border_mode='same',
weights=dp.Parameter(dp.NormalFiller(sigma=0.01),
penalty=('l2', 0.004),
monitor=True),
),
dp.Activation('relu'),
dp.Pool(**pool_kwargs),
dp.Convolutional(
n_filters=64,
filter_shape=(5, 5),
border_mode='same',
weights=dp.Parameter(dp.NormalFiller(sigma=0.01),
penalty=('l2', 0.004),
monitor=True),
),
dp.Activation('relu'),
dp.Pool(**pool_kwargs),
dp.Flatten(),
dp.FullyConnected(
n_output=64,
weights=dp.Parameter(dp.NormalFiller(sigma=0.1),
penalty=('l2', 0.03)),
),
dp.Activation('relu'),
dp.FullyConnected(
n_output=dataset.n_classes,
weights=dp.Parameter(dp.NormalFiller(sigma=0.1),
penalty=('l2', 0.03)),
),
dp.MultinomialLogReg(),
], )
dp.misc.profile(nn, train_input)
def run():
# Prepare data
batch_size = 128
dataset = dp.datasets.CIFAR10()
x, y = dataset.data()
y = y.astype(dp.int_)
train_idx, test_idx = dataset.split()
x_train = preprocess_imgs(x[train_idx])
y_train = y[train_idx]
x_test = preprocess_imgs(x[test_idx])
y_test = y[test_idx]
train_input = dp.SupervisedInput(x_train, y_train, batch_size=batch_size)
test_input = dp.SupervisedInput(x_test, y_test, batch_size=batch_size)
# Setup neural network
pool_kwargs = {
'win_shape': (3, 3),
'strides': (2, 2),
'border_mode': 'same',
'method': 'max',
}
nn = dp.NeuralNetwork(layers=[
dp.Convolutional(
n_filters=32,
filter_shape=(5, 5),
border_mode='same',
weights=dp.Parameter(dp.NormalFiller(sigma=0.0001),
penalty=('l2', 0.004),
monitor=True),
),
dp.Activation('relu'),
dp.Pool(**pool_kwargs),
dp.Convolutional(
n_filters=32,
filter_shape=(5, 5),
border_mode='same',
weights=dp.Parameter(dp.NormalFiller(sigma=0.01),
penalty=('l2', 0.004),
monitor=True),
),
dp.Activation('relu'),
dp.Pool(**pool_kwargs),
dp.Convolutional(
n_filters=64,
filter_shape=(5, 5),
border_mode='same',
weights=dp.Parameter(dp.NormalFiller(sigma=0.01),
penalty=('l2', 0.004),
monitor=True),
),
dp.Activation('relu'),
dp.Pool(**pool_kwargs),
dp.Flatten(),
dp.FullyConnected(
n_output=64,
weights=dp.Parameter(dp.NormalFiller(sigma=0.1),
penalty=('l2', 0.03)),
),
dp.Activation('relu'),
dp.FullyConnected(
n_output=dataset.n_classes,
weights=dp.Parameter(dp.NormalFiller(sigma=0.1),
penalty=('l2', 0.03)),
),
dp.MultinomialLogReg(),
], )
# Train neural network
n_epochs = [8, 8]
learn_rate = 0.001
def valid_error():
return nn.error(test_input)
for i, max_epochs in enumerate(n_epochs):
lr = learn_rate / 10**i
trainer = dp.StochasticGradientDescent(
max_epochs=max_epochs,
learn_rule=dp.Momentum(learn_rate=lr, momentum=0.9),
)
trainer.train(nn, train_input, valid_error)
# Visualize convolutional filters to disk
for l, layer in enumerate(nn.layers):
if not isinstance(layer, dp.Convolutional):
continue
W = np.array(layer.params()[0].values)
dp.misc.img_save(dp.misc.conv_filter_tile(W),
os.path.join('cifar10', 'convnet_layer_%i.png' % l))
# Evaluate on test data
error = nn.error(test_input)
print('Test error rate: %.4f' % error)
def run():
# Prepare data
dataset = dp.datasets.MNIST()
x, y = dataset.data()
x = x[:, np.newaxis, :, :].astype(dp.float_) / 255.0 - 0.5
y = y.astype(dp.int_)
train_idx, test_idx = dataset.split()
x_train = x[train_idx]
y_train = y[train_idx]
x_test = x[test_idx]
y_test = y[test_idx]
train_input = dp.SupervisedInput(x_train, y_train, batch_size=128)
test_input = dp.SupervisedInput(x_test, y_test)
# Setup neural network
nn = dp.NeuralNetwork(layers=[
dp.Convolutional(
n_filters=20,
filter_shape=(5, 5),
weights=dp.Parameter(dp.NormalFiller(sigma=0.1),
penalty=('l2', 0.00001)),
),
dp.Activation('relu'),
dp.Pool(
win_shape=(2, 2),
strides=(2, 2),
method='max',
),
dp.Convolutional(
n_filters=50,
filter_shape=(5, 5),
weights=dp.Parameter(dp.NormalFiller(sigma=0.1),
penalty=('l2', 0.00001)),
),
dp.Activation('relu'),
dp.Pool(
win_shape=(2, 2),
strides=(2, 2),
method='max',
),
dp.Flatten(),
dp.FullyConnected(
n_output=500,
weights=dp.NormalFiller(sigma=0.01),
),
dp.FullyConnected(
n_output=dataset.n_classes,
weights=dp.NormalFiller(sigma=0.01),
),
dp.MultinomialLogReg(),
], )
# Train neural network
def valid_error():
return nn.error(test_input)
trainer = dp.StochasticGradientDescent(
max_epochs=15,
learn_rule=dp.Momentum(learn_rate=0.1, momentum=0.9),
)
trainer.train(nn, train_input, valid_error)
# Visualize convolutional filters to disk
for layer_idx, layer in enumerate(nn.layers):
if not isinstance(layer, dp.Convolutional):
continue
W = np.array(layer.params()[0].values)
dp.misc.img_save(
dp.misc.conv_filter_tile(W),
os.path.join('mnist', 'convnet_layer_%i.png' % layer_idx))
# Evaluate on test data
error = nn.error(test_input)
print('Test error rate: %.4f' % error)
filter_shape=(5, 5),
weights=dp.Parameter(dp.AutoFiller(), weight_decay=0.0001),
),
dp.Activation('relu'),
dp.Pool(
win_shape=(3, 3),
strides=(2, 2),
method='max',
),
dp.Convolutional(
n_filters=64,
filter_shape=(5, 5),
weights=dp.Parameter(dp.AutoFiller(), weight_decay=0.0001),
),
dp.Activation('relu'),
dp.Pool(
win_shape=(3, 3),
strides=(2, 2),
method='max',
),
dp.Flatten(),
dp.FullyConnected(
n_output=128,
weights=dp.Parameter(dp.AutoFiller()),
),
dp.FullyConnected(
n_output=6,
weights=dp.Parameter(dp.AutoFiller()),
),
dp.MultinomialLogReg(),
], )
