def main():
# data
train_x, test_x, train_y, test_y = dataloader.load('mnist')
# train_x, train_y = dataloader.load('xor', split=False)
# use a random batch
BATCH_SIZE = 32
used_batch = np.random.permutation(len(train_x))[:BATCH_SIZE]
batch_x = train_x[used_batch]
batch_y = train_y[used_batch]
nn = NeuralNetwork(layers=[
layers.Dense(512, input_shape=(784, )),
layers.Activation('tanh'),
layers.Dense(256),
layers.Activation('tanh'),
layers.Dense(10),
layers.Activation('softmax')
],
optimizer='rmsprop',
initializer='he-et-al')
trainer = Trainer(nn, loss='cross-entropy', metrics=['accuracy'])
# Training
gc = GradientChecker(nn, trainer)
# Check for N steps
N = 10000
for _ in range(N):
gc.check(batch_x, batch_y)
trainer.batch_train(batch_x, batch_y)
print(trainer.predict(batch_x))
def main():
# data
train_x, train_y = dataloader.load('xor', split=False)
# use a random batch
BATCH_SIZE = 4
used_batch = np.random.permutation(len(train_x))[:BATCH_SIZE]
batch_x = train_x[used_batch]
batch_y = train_y[used_batch]
# Network implementation
nn = NeuralNetwork(layers=[
layers.Dense(300, input_shape=(2, )),
layers.Activation('tanh'),
layers.Dense(200),
layers.Activation('leaky-relu'),
layers.Dense(1)
],
optimizer='rmsprop')
# Training
trainer = Trainer(nn, loss='mean-square', print_step_mod=1000)
gc = GradientChecker(nn, trainer)
# Check for N steps
N = 10000
for _ in range(N):
gc.check(batch_x, batch_y)
trainer.batch_train(batch_x, batch_y)
def main():
# data
train_x, test_x, train_y, test_y = dataloader.load('mnist')
# net
nn = NeuralNetwork(layers=[
layers.Dense(500, input_shape=(784, )),
layers.Activation('relu'),
layers.Dense(200),
layers.Activation('relu'),
layers.Dense(500),
layers.Activation('relu'),
layers.Dense(784)
],
optimizer='rmsprop',
initializer='he-et-al')
trainer = Trainer(nn, loss='mean-square')
epochs = 10
for i in range(epochs):
trainer.train(train_x, train_x, epochs=1)
plt.imshow(test_x[0].reshape(28, 28))
plt.show()
plt.imshow(trainer.predict(np.array([test_x[0]])).reshape(28, 28))
plt.show()
def main():
# data
train_x, test_x, train_y, test_y = dataloader.load('mnist')
# net
nn = NeuralNetwork(
layers=[
layers.Dense(512, input_shape=(784, )),
layers.Activation('relu'),
layers.Dense(256),
layers.Activation('relu'),
layers.Dense(10),
layers.Activation('softmax')
], optimizer='rmsprop', initializer='he-et-al')
trainer = Trainer(nn, loss='cross-entropy', metrics=['accuracy'])
trainer.train(train_x, train_y, epochs=10, test_size=0.1)
print(trainer.eval(test_x, test_y))
def main():
# Getting data
train_x, train_y = dataloader.load('xor', split=False)
# Network implementation
nn = NeuralNetwork(
layers=[
layers.Dense(4, input_shape=(2, )),
layers.Activation('tanh'),
layers.Dense(1)
],
optimizer='rmsprop',
)
# Training
trainer = Trainer(nn, loss='mean-square', print_step_mod=1000)
trainer.train(train_x, train_y, epochs=5000, batch_size=-1)
print(trainer.predict(train_x))
def main():
# Generating fake data: Class1 around (0, 0) and Class2 around (10, 10)
train_x, train_y = make_blobs(n_samples=200,
n_features=2,
cluster_std=1.0,
centers=[(0, 0), (10, 10)],
shuffle=False,
random_state=42)
one_hot = OneHotEncoder(sparse=False)
print(train_x)
train_y_one_hot = one_hot.fit_transform(train_y.reshape(len(train_y), 1))
plt.scatter(train_x[:, 0], train_x[:, 1], alpha=0.5, c=train_y)
plt.show()
# Network implementation
nn = NeuralNetwork(layers=[
layers.Dense(2, input_shape=(2, )),
layers.Activation('softmax')
],
optimizer='sgd')
# Training
trainer = Trainer(nn,
loss='cross-entropy',
print_step_mod=100,
metrics=['accuracy'])
trainer.train(train_x, train_y_one_hot, epochs=45, batch_size=20)
print('Learned parameters:')
print('weights:', nn.layers[0].W)
print('biases:', nn.layers[0].b)
eval_x = np.random.uniform(low=-3, high=13, size=(200, 2))
plt.scatter(train_x[:, 0], train_x[:, 1], alpha=0.5)
plt.scatter(eval_x[:, 0],
eval_x[:, 1],
alpha=0.5,
c=(np.argmax(nn.forward(eval_x), axis=1) + 2))
plt.show()