def test_classification():
# Make dataset
n_classes = 2
n_samples = 1000
n_features = 48
x, y = make_classification(n_samples=n_samples,
n_features=n_features,
n_classes=n_classes,
n_informative=n_classes * 2,
random_state=1)
x = x.astype(dp.float_)
y = y.astype(dp.int_)
n_train = int(0.8 * n_samples)
x_train = x[:n_train]
y_train = y[:n_train]
x_test = x[n_train:]
y_test = y[n_train:]
scaler = dp.StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)
# Setup feeds
batch_size = 16
train_feed = dp.SupervisedFeed(x_train, y_train, batch_size=batch_size)
test_feed = dp.Feed(x_test)
# Setup neural network
weight_decay = 1e-03
net = dp.NeuralNetwork(
layers=[
dp.Affine(
n_out=32,
weights=dp.Parameter(dp.AutoFiller(),
weight_decay=weight_decay),
),
dp.ReLU(),
dp.Affine(
n_out=64,
weights=dp.Parameter(dp.AutoFiller(),
weight_decay=weight_decay),
),
dp.ReLU(),
dp.Affine(
n_out=n_classes,
weights=dp.Parameter(dp.AutoFiller()),
),
],
loss=dp.SoftmaxCrossEntropy(),
)
# Train neural network
learn_rule = dp.Momentum(learn_rate=0.01 / batch_size, momentum=0.9)
trainer = dp.GradientDescent(net, train_feed, learn_rule)
trainer.train_epochs(n_epochs=10)
# Evaluate on test data
error = np.mean(net.predict(test_feed) != y_test)
print('Test error rate: %.4f' % error)
assert error < 0.2
def vgg_net(path, pool_method='max', border_mode='same'):
matconvnet = scipy.io.loadmat(path)
img_mean = matconvnet['meta'][0][0][2][0][0][2]
vgg_layers = matconvnet['layers'][0]
layers = []
for layer in vgg_layers:
layer = layer[0][0]
layer_type = layer[1][0]
if layer_type == 'conv':
params = layer[2][0]
weights = params[0]
bias = params[1]
weights = np.transpose(weights, (3, 2, 0, 1)).astype(dp.float_)
bias = np.reshape(bias, (1, bias.size, 1, 1)).astype(dp.float_)
layers.append(conv_layer(weights, bias, border_mode))
elif layer_type == 'pool':
layers.append(pool_layer(pool_method, border_mode))
elif layer_type == 'relu':
layers.append(dp.ReLU())
elif layer_type == 'softmax':
pass
else:
raise ValueError('invalid layer type: %s' % layer_type)
return layers, img_mean
n += 1
# Prepare network feeds
batch_size = 128
train_feed = dp.SupervisedSiameseFeed(x1, x2, y, batch_size=batch_size)
# Setup network
w_gain = 1.5
w_decay = 1e-4
net = dp.SiameseNetwork(
siamese_layers=[
dp.Affine(
n_out=1024,
weights=dp.Parameter(dp.AutoFiller(w_gain), weight_decay=w_decay),
),
dp.ReLU(),
dp.Affine(
n_out=1024,
weights=dp.Parameter(dp.AutoFiller(w_gain), weight_decay=w_decay),
),
dp.ReLU(),
dp.Affine(
n_out=2,
weights=dp.Parameter(dp.AutoFiller(w_gain)),
),
],
loss=dp.ContrastiveLoss(margin=1.0),
)
# Train network
learn_rate = 0.01 / batch_size
def test_classification():
# Make dataset
n_classes = 2
n_samples = 1000
n_features = 48
x, y = make_classification(
n_samples=n_samples, n_features=n_features, n_classes=n_classes,
n_informative=n_classes*2, random_state=1
)
n_train = int(0.8 * n_samples)
n_val = int(0.5 * (n_samples - n_train))
x_train = x[:n_train]
y_train = y[:n_train]
x_val = x[n_train:n_train+n_val]
y_val = y[n_train:n_train+n_val]
x_test = x[n_train+n_val:]
y_test = y[n_train+n_val:]
scaler = dp.StandardScaler()
x_train = scaler.fit_transform(x_train)
x_val = scaler.transform(x_val)
x_test = scaler.transform(x_test)
# Setup input
batch_size = 16
train_input = dp.SupervisedInput(x_train, y_train, batch_size=batch_size)
val_input = dp.Input(x_val)
test_input = dp.Input(x_test)
# Setup neural network
weight_decay = 1e-03
net = dp.NeuralNetwork(
layers=[
dp.Affine(
n_out=32,
weights=dp.Parameter(dp.AutoFiller(),
weight_decay=weight_decay),
),
dp.ReLU(),
dp.Affine(
n_out=64,
weights=dp.Parameter(dp.AutoFiller(),
weight_decay=weight_decay),
),
dp.ReLU(),
dp.Affine(
n_out=n_classes,
weights=dp.Parameter(dp.AutoFiller()),
),
],
loss=dp.SoftmaxCrossEntropy(),
)
# Train neural network
def val_error():
return np.mean(net.predict(val_input) != y_val)
trainer = dp.GradientDescent(
min_epochs=10, learn_rule=dp.Momentum(learn_rate=0.01, momentum=0.9),
)
trainer.train(net, train_input, val_error)
# Evaluate on test data
error = np.mean(net.predict(test_input) != y_test)
print('Test error rate: %.4f' % error)
assert error < 0.2
