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digits_tf.py
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digits_tf.py
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'''This project was mediated through Michael Guerzhoy and is not
to be copy and used for educational purposes which can lead to Academic Integrity'''
# Import all modules from pylab
from pylab import *
# Numpy Modules
import numpy as np
from numpy import random
# Matplotlib Modules
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
import matplotlib.image as mpimg
from matplotlib.font_manager import FontProperties
# Scipy modules
from scipy.misc import imread
from scipy.misc import imresize
import scipy.stats
from scipy.ndimage import filters
from scipy.io import loadmat
# Modules for reading data
import urllib
import cPickle
import os
import timeit
import time
t = int(time.time())
#t = 1454219613
print "t=", t
random.seed(t)
M = loadmat("mnist_all.mat")
import tensorflow as tf
def get_train_batch(M, N):
n = N/10
batch_xs = zeros((0, 28*28))
batch_y_s = zeros( (0, 10))
train_k = ["train"+str(i) for i in range(10)]
train_size = len(M[train_k[0]])
#train_size = 5000
for k in range(10):
train_size = len(M[train_k[k]])
idx = array(random.permutation(train_size)[:n])
batch_xs = vstack((batch_xs, ((array(M[train_k[k]])[idx])/255.) ))
one_hot = zeros(10)
one_hot[k] = 1
batch_y_s = vstack((batch_y_s, tile(one_hot, (n, 1)) ))
return batch_xs, batch_y_s
def get_test(M):
batch_xs = zeros((0, 28*28))
batch_y_s = zeros( (0, 10))
test_k = ["test"+str(i) for i in range(10)]
for k in range(10):
batch_xs = vstack((batch_xs, ((array(M[test_k[k]])[:])/255.) ))
one_hot = zeros(10)
one_hot[k] = 1
batch_y_s = vstack((batch_y_s, tile(one_hot, (len(M[test_k[k]]), 1)) ))
return batch_xs, batch_y_s
def get_train(M):
batch_xs = zeros((0, 28*28))
batch_y_s = zeros( (0, 10))
train_k = ["train"+str(i) for i in range(10)]
for k in range(10):
batch_xs = vstack((batch_xs, ((array(M[train_k[k]])[:])/255.) ))
one_hot = zeros(10)
one_hot[k] = 1
batch_y_s = vstack((batch_y_s, tile(one_hot, (len(M[train_k[k]]), 1)) ))
return batch_xs, batch_y_s
x = tf.placeholder(tf.float32, [None, 784])
nhid = 300
W0 = tf.Variable(tf.random_normal([784, nhid], stddev=0.01))
b0 = tf.Variable(tf.random_normal([nhid], stddev=0.01))
W1 = tf.Variable(tf.random_normal([nhid, 10], stddev=0.01))
b1 = tf.Variable(tf.random_normal([10], stddev=0.01))
snapshot = cPickle.load(open("snapshot50.pkl"))
W0 = tf.Variable(snapshot["W0"])
b0 = tf.Variable(snapshot["b0"])
W1 = tf.Variable(snapshot["W1"])
b1 = tf.Variable(snapshot["b1"])
layer1 = tf.nn.tanh(tf.matmul(x, W0)+b0)
layer2 = tf.matmul(layer1, W1)+b1
y = tf.nn.softmax(layer2)
y_ = tf.placeholder(tf.float32, [None, 10])
lam = 0.00000
decay_penalty =lam*tf.reduce_sum(tf.square(W0))+lam*tf.reduce_sum(tf.square(W1))
reg_NLL = -tf.reduce_sum(y_*tf.log(y))+decay_penalty
train_step = tf.train.AdamOptimizer(0.0005).minimize(reg_NLL)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
test_x, test_y = get_test(M)
for i in range(5000):
#print i
batch_xs, batch_ys = get_train_batch(M, 500)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
if i % 1 == 0:
print "i=",i
print "Test:", sess.run(accuracy, feed_dict={x: test_x, y_: test_y})
batch_xs, batch_ys = get_train(M)
print "Train:", sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys})
print "Penalty:", sess.run(decay_penalty)
snapshot = {}
snapshot["W0"] = sess.run(W0)
snapshot["W1"] = sess.run(W1)
snapshot["b0"] = sess.run(b0)
snapshot["b1"] = sess.run(b1)
cPickle.dump(snapshot, open("new_snapshot"+str(i)+".pkl", "w"))