def __init__(self):
self.gamma = 0.9
self.lr = 0.01
self.epsilon = 0.1
self.action_size = 4
self.qnet = QNet()
self.optimizer = optimizers.SGD(self.lr)
self.optimizer.setup(self.qnet)
def test_MNIST(self):
max_epoch = 5
batch_size = 100
hidden_size = 1000
train_set = dezero.datasets.MNIST(train=True)
test_set = dezero.datasets.MNIST(train=False)
train_loader = DataLoader(train_set, batch_size)
test_loader = DataLoader(test_set, batch_size, shuffle=False)
#model = MLP((hidden_size, 10))
model = MLP((hidden_size, hidden_size, 10), activation=F.relu)
optimizer = optimizers.SGD().setup(model)
if dezero.cuda.gpu_enable:
train_loader.to_gpu()
model.to_gpu()
for epoch in range(max_epoch):
sum_loss, sum_acc = 0, 0
for x, t in train_loader:
y = model(x)
loss = F.softmax_cross_entropy(y, t)
acc = F.accuracy(y, t)
model.cleargrads()
loss.backward()
optimizer.update()
sum_loss += float(loss.data) * len(t)
sum_acc += float(acc.data) * len(t)
print('epoch: {}'.format(epoch + 1))
print('train loss: {:.4f}, accuracy: {:.4f}'.format(
sum_loss / len(train_set), sum_acc / len(train_set)))
sum_loss, sum_acc = 0, 0
with dezero.no_grad():
for x, t in test_loader:
y = model(x)
loss = F.softmax_cross_entropy(y, t)
acc = F.accuracy(y, t)
sum_loss += float(loss.data) * len(t)
sum_acc += float(acc.data) * len(t)
print('test loss: {:.4f}, accuracy: {:.4f}'.format(
sum_loss / len(test_set), sum_acc / len(test_set)))
def test_SDG(self):
x = np.random.rand(100, 1)
y = np.sin(2 * np.pi * x) + np.random.rand(100, 1)
hidden_size = 10
model = MLP((hidden_size, 1))
optimizer = optimizers.SGD(lr)
optimizer.setup(model)
for i in range(max_iters):
y_pred = model(x)
loss = F.mean_squared_error(y, y_pred)
model.cleargrads()
loss.backward()
optimizer.update()
assert True
def test_SoftmaxCrossEntorpy(self):
max_epoch = 0
batch_size = 30
hidden_size = 10
lr = 1.0
train_set = Spiral(train=True)
test_set = Spiral(train=False)
train_loader = DataLoader(train_set, batch_size)
test_loader = DataLoader(test_set, batch_size, shuffle=False)
model = MLP((hidden_size, hidden_size, hidden_size, 3))
optimizer = optimizers.SGD(lr).setup(model)
for epoch in range(max_epoch):
sum_loss, sum_acc = 0, 0
for x, t in train_loader:
y = model(x)
loss = F.softmax_cross_entropy(y, t)
acc = F.accuracy(y, t)
model.cleargrads()
loss.backward()
optimizer.update()
sum_loss += float(loss.data) * len(t)
sum_acc += float(acc.data) * len(t)
print('epoch: {}'.format(epoch + 1))
print('train loss: {:.4f}, accuracy: {:.4f}'.format(
sum_loss / len(train_set), sum_acc / len(train_set)))
sum_loss, sum_acc = 0, 0
with dezero.no_grad():
for x, t in test_loader:
y = model(x)
loss = F.softmax_cross_entropy(y, t)
acc = F.accuracy(y, t)
sum_loss += float(loss.data) * len(t)
sum_acc += float(acc.data) * len(t)
print('test loss: {:.4f}, accuracy: {:.4f}'.format(
sum_loss / len(test_set), sum_acc / len(test_set)))
def test_sgd(self):
np.random.seed(0)
x = np.random.rand(100, 1)
y = np.sin(2 * np.pi * x) + np.random.rand(100, 1)
lr = 0.2
max_iter = 10000
hidden_size = 10
model = models.MLP((hidden_size, 1))
optimizer = optimizers.SGD(lr)
optimizer.setup(model)
for i in range(max_iter):
y_pred = model(x)
loss = F.mean_squared_error(y, y_pred)
model.cleargrads()
loss.backward()
optimizer.update()
if i % 1000 == 0:
print(loss)
import matplotlib.pyplot as plt
import numpy as np
import dezero
import dezero.functions as F
from dezero import optimizers
from dezero.models import MLP
max_epoch = 300
batch_size = 30
hidden_size = 10
lr = 1.0
x, t = dezero.datasets.get_spiral(train=True)
model = MLP((hidden_size, 3))
optimizer = optimizers.SGD(lr).setup(model)
data_size = len(x)
max_iter = math.ceil(data_size / batch_size)
for epoch in range(max_epoch):
index = np.random.permutation(data_size)
sum_loss = 0
for i in range(max_iter):
batch_index = index[i * batch_size:(i + 1) * batch_size]
batch_x = x[batch_index]
batch_t = t[batch_index]
y = model(batch_x)
loss = F.softmax_cross_entropy(y, batch_t)
import dezero.functions as F
from dezero import Variable, optimizers
from dezero.models import MLP
np.random.rand(0)
x = np.random.rand(100, 1)
y = np.sin(2 * np.pi * x) + np.random.rand(100, 1)
x, y = Variable(x), Variable(y)
lr = 0.2
iters = 10000
hidden_size = 10
model = MLP((hidden_size, 1))
optimizer = optimizers.SGD(lr)
optimizer.setup(model)
for i in range(iters):
y_pred = model(x)
loss = F.mean_squared_error(y, y_pred)
model.clear_grads()
loss.backward()
optimizer.update()
if i % 1000 == 0:
print(loss)
# Plot
plt.scatter(x.data, y.data, s=10)
