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()
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 MNIST 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)
# Generate image pairs
n_pairs = 100000
x1 = np.empty((n_pairs, 28 * 28), dtype=dp.float_)
x2 = np.empty_like(x1, dtype=dp.float_)
y = np.empty(n_pairs, dtype=dp.int_)
n_imgs = x_train.shape[0]
n = 0
while n < n_pairs:
i = random.randint(0, n_imgs - 1)
j = random.randint(0, n_imgs - 1)
if i == j:
continue
x1[n, ...] = x_train[i]
x2[n, ...] = x_train[j]
if y_train[i] == y_train[j]:
y[n] = 1
else:
y[n] = 0
n += 1
# Input to network
train_input = dp.SupervisedSiameseInput(x1, x2, y, batch_size=128)
test_input = dp.SupervisedInput(x_test, y_test)
# Setup network
net = dp.SiameseNetwork(
siamese_layers=[
dp.Dropout(),
dp.FullyConnected(
n_output=800,
weights=dp.Parameter(dp.AutoFiller(), weight_decay=0.00001),
),
dp.Activation('relu'),
dp.FullyConnected(
n_output=800,
weights=dp.Parameter(dp.AutoFiller(), weight_decay=0.00001),
),
dp.Activation('relu'),
dp.FullyConnected(
n_output=2,
weights=dp.Parameter(dp.AutoFiller(), weight_decay=0.00001),
),
],
loss_layer=dp.ContrastiveLoss(margin=0.5),
)
# Train network
trainer = dp.StochasticGradientDescent(
max_epochs=10,
learn_rule=dp.RMSProp(learn_rate=0.001),
)
trainer.train(net, train_input)
# Visualize feature space
feat = net.features(test_input)
colors = [
'tomato', 'lawngreen', 'royalblue', 'gold', 'saddlebrown', 'violet',
'turquoise', 'mediumpurple', 'darkorange', 'darkgray'
]
plt.figure()
for i in range(10):
plt.scatter(feat[y_test == i, 0],
feat[y_test == i, 1],
s=3,
c=colors[i],
linewidths=0)
plt.legend([str(i) for i in range(10)], scatterpoints=1, markerscale=4)
if not os.path.exists('mnist'):
os.mkdirs('mnist')
plt.savefig(os.path.join('mnist', 'siamese_dists.png'), dpi=200)
numberOfAugmentation = 100
for i in range(numberOfAugmentation):
for j in range(len(test_index)):
features[i * 864 + test_index[j], :] = np.NAN
targets[0, i * 864 + test_index[j]] = np.NAN
x_train = np.delete(features, test_index, axis=0)
y_train = np.delete(targets, test_index)
x_train = features[~np.all(np.isnan(features), axis=1)]
y_train = targets[~np.isnan(targets)]
y_train = y_train.astype('int')
# Normalize pixel intensities
scaler = dp.UniformScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
# Shufflinig
ind = range(len(y_train))
np.random.shuffle(ind)
y_train = y_train[ind]
x_train = x_train[ind, :]
# Prepare autoencoder input
batch_size = 50
train_input = dp.Input(x_train, batch_size=batch_size)
# Setup autoencoders
sae = dp.StackedAutoencoder(layers=[
def run():
# Prepare data
dataset = dp.datasets.CIFAR10()
x, y = dataset.data()
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(feature_wise=True)
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, batch_size=batch_size)
# Setup neural network
pool_kwargs = {
'win_shape': (3, 3),
'strides': (2, 2),
'border_mode': 'same',
'method': 'max',
}
net = dp.NeuralNetwork(
layers=[
dp.Convolutional(
n_filters=32,
filter_shape=(5, 5),
border_mode='same',
weights=dp.Parameter(dp.NormalFiller(sigma=0.0001),
weight_decay=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),
weight_decay=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),
weight_decay=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),
weight_decay=0.004, monitor=True),
),
dp.Activation('relu'),
dp.FullyConnected(
n_output=dataset.n_classes,
weights=dp.Parameter(dp.NormalFiller(sigma=0.1),
weight_decay=0.004, monitor=True),
),
dp.MultinomialLogReg(),
],
)
# Train neural network
def val_error():
return net.error(test_input)
n_epochs = [8, 8]
learn_rate = 0.001
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(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('cifar10', '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)
