net.push(Relu())
net.push(Maxpooling(2, 2)) # 6x24x24 -> 6x12x12
net.push(Conv2d(5, 5, 6, 16)) # 6x12x12 -> 16x8x8
net.push(Relu())
net.push(Maxpooling(2, 2)) # 16x8x8 -> 16x4x4
net.push(Reshape((256)))
net.push(Linear(256, 84))
net.push(Relu())
net.push(Softmax(84, 10))
# Data
data = DataProvider()
n = 10000
data.train_input(x[:n], y[:n])
data.test_input(xt, yt)
data.batch_size(16)
lr = 0.0009
gamma = 0.9
for epoch in xrange(50):
print 'Epoch: ', epoch
# Training (Mini-batch)
now = time.time()
for _ in xrange(data.batch_run()):
net.input(data.next_batch())
net.forward()
net.backward(lr)
t = time.time() - now
acc, loss = net.get_record()
print 'Acc: ', np.array(acc).mean()
net = Net()
net.push(Conv2d(5, 5, 3, 20)) # 3x32 -> 10x28
net.push(Relu())
net.push(BatchNorm())
net.push(Maxpooling(4, 4)) # 10x28 -> 10x7
net.push(Reshape((980)))
net.push(Linear(980, 200))
net.push(Relu())
net.push(BatchNorm())
net.push(Softmax(200, 10))
# Data
data = DataProvider()
data.train_input(x, y)
data.test_input(xt, yt)
data.batch_size(32)
data.batch_size_test(1000)
lr = 1e-3
gamma = 1
beta_1 = 0.9
beta_2 = 0.999
total_epoch = 100
loss_cache = 10
for epoch in xrange(1, total_epoch + 1):
print 'Epoch: {}/{}'.format(epoch, total_epoch)
# Training (Mini-batch)
now = time.time()
data.shuffle()
