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)
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
learn_rule = dp.RMSProp(learn_rate)
trainer = dp.GradientDescent(net, train_feed, learn_rule)
trainer.train_epochs(n_epochs=15)
# Prepare network inputs
train_input = dp.SupervisedSiameseInput(x1, x2, y, batch_size=128)
# Setup network
w_gain = 1.5
w_decay = 1e-4
net = dp.SiameseNetwork(
siamese_layers=[
dp.FullyConnected(
n_out=1024,
weights=dp.Parameter(dp.AutoFiller(w_gain), weight_decay=w_decay),
),
dp.Activation('relu'),
dp.FullyConnected(
n_out=1024,
weights=dp.Parameter(dp.AutoFiller(w_gain), weight_decay=w_decay),
),
dp.Activation('relu'),
dp.FullyConnected(
n_out=2,
weights=dp.Parameter(dp.AutoFiller(w_gain)),
),
],
loss=dp.ContrastiveLoss(margin=1.0),
)
# Train network
trainer = dp.StochasticGradientDescent(
max_epochs=15,
learn_rule=dp.RMSProp(learn_rate=0.01),
)