from layers import Conv2d, Linear, Maxpooling, Relu, LeakyRelu, AbsRelu, Softmax, Reshape, BatchNorm

from data import DataProvider

from net import Net

import numpy as np

import time

from keras.datasets import cifar10

from progressive.bar import Bar

if __name__ == '__main__':

(x, y), (xt, yt) = cifar10.load_data()

def preprocessing(x):

return x

x = preprocessing(x)

xt = preprocessing(xt)

print x.mean()

print x.std()

x = x.reshape(50000, 32, 32, 3).transpose(0,3,1,2)

xt = xt.reshape(10000, 32, 32, 3).transpose(0,3,1,2)

y = y.ravel()

yt = yt.ravel()

n = 50000

nt = 10000

x = x[:n]

y = y[:n]

xt = xt[:nt]

yt = yt[:nt]

# Model

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()

bar = Bar(max_value=n)

bar.cursor.clear_lines(2) # Make some room

bar.cursor.save() # Mark starting line

for _ in xrange(data.batch_run()):

net.input(data.next_batch())

net.forward()

net.backward(lr, beta_1, beta_2, epoch)

bar.cursor.restore() # Return cursor to start

bar.draw(value=data.get_count())

t = time.time() - now

acc, loss = net.get_record()

loss_avg = np.array(loss).mean()

loss_diff = loss_avg - loss_cache

loss_cache = loss_avg

print 'Acc: ', np.array(acc).mean()

print 'Loss: ', loss_avg

print 'Time: ', t

f, b = net.get_profile()

net.clear_record()

bar_t = Bar(max_value=nt)

bar_t.cursor.clear_lines(2) # Make some room

bar_t.cursor.save() # Mark starting line

for _ in xrange(data.batch_run_test()):

net.input(data.next_batch_test())

net.forward()

bar_t.cursor.restore() # Return cursor to start

bar_t.draw(value=data.get_count_test())

acc, loss = net.get_record()

print 'Val: ', np.array(acc).mean()

print 'Loss: ', np.array(loss).mean()

net.clear_record()

lr *= gamma

print lr, loss_diff

print

# Profile

print 'Forward Time: ', sum(f)

print f

print 'Backward Time: ', sum(b)

print b

print