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
================
"""
import random
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import offsetbox
import deeppy as dp
# Fetch MNIST data
dataset = dp.dataset.MNIST()
x_train, y_train, x_test, y_test = dataset.arrays(flat=True, dp_dtypes=True)
# Normalize pixel intensities
scaler = dp.StandardScaler()
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
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