# -*- coding: utf-8 -*-

"""

Created on Sun Sep 1 14:59:45 2019

@author: jeong

"""

def testTheano():

from theano import function, config, shared, sandbox

import theano.tensor as T

import numpy

import time

print("Testing Theano library...")

vlen = 10 * 30 * 768 # 10 x #cores x # threads per core

iters = 1000

rng = numpy.random.RandomState(22)

x = shared(numpy.asarray(rng.rand(vlen), config.floatX))

f = function([], T.exp(x))

print(f.maker.fgraph.toposort())

t0 = time.time()

for i in range(iters):

r = f()

t1 = time.time()

print("Looping %d times took %f seconds" % (iters, t1 - t0))

print("Result is %s" % (r,))

if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):

print('Used the cpu')

else:

print('Used the gpu')

# Perform check:

#testTheano()

# ----------------------

# - network3.py example:

import network3

from network3 import Network, ConvPoolLayer, FullyConnectedLayer, SoftmaxLayer # softmax plus log-likelihood cost is more common in modern image classification networks.

# read data:

training_data, validation_data, test_data = network3.load_data_shared()

# mini-batch size:

mini_batch_size = 10

# chapter 6 - rectified linear units and some l2 regularization (lmbda=0.1) => even better accuracy

from network3 import ReLU

net = Network([

ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28),

filter_shape=(20, 1, 5, 5),

poolsize=(2, 2),

activation_fn=ReLU),

ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12),

filter_shape=(40, 20, 5, 5),

poolsize=(2, 2),

activation_fn=ReLU),

FullyConnectedLayer(n_in=40*4*4, n_out=100, activation_fn=ReLU),

SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)

net.SGD(training_data, 60, mini_batch_size, 0.03, validation_data, test_data, lmbda=0.1)