import tensorflow.examples.tutorials.mnist.input_data as input_data

import tensorflow as tf

from load_data import LoadData

import numpy as np

def weight_variable(shape):

initial = tf.truncated_normal(shape, stddev=0.1)

return tf.Variable(initial)

def bias_variable(shape):

initial = tf.constant(0.1, shape=shape)

return tf.Variable(initial)

def conv2d(x, W):

return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):

return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],

strides=[1, 2, 2, 1], padding='SAME')

# Load the Digit DataSet

load_data = LoadData()

train_set_x, train_set_y = load_data.load_train_data("/home/darshan/Documents/DigitRecognizer/MNIST_data/",

"train.csv")

#mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder(tf.float32, [None, 784])

x_image = tf.reshape(x, [-1, 28, 28, 1])

y_ = tf.placeholder(tf.float32, [None, 10])

# First Layer of Convnet

W_conv1 = weight_variable([5, 5, 1, 32])

b_conv1 = bias_variable([32])

# 28 x 28 -> 24 x 24

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

# 24 x 24 -> 12 x 12

h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 32, 64])

b_conv2 = bias_variable([64])

# 12 x 12 -> 8 x 8

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

#8 x 8 -> 4 x 4

h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7 * 7 * 64, 1024])

b_fc1 = bias_variable([1024])

h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])

h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder("float")

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])

b_fc2 = bias_variable([10])

y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# Loss Function : Cross Entropy

cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

sess = tf.InteractiveSession()

sess.run(tf.initialize_all_variables())

for i in range(20000):

batch_xs, batch_ys = load_data.get_train_batch(50)

if i % 100 == 0:

train_accuracy = accuracy.eval(feed_dict={

x: batch_xs, y_: batch_ys, keep_prob: 1.0})

print "step %d, training accuracy %g" % (i, train_accuracy)

train_step.run(feed_dict={x: batch_xs, y_: batch_ys, keep_prob: 0.5})

#print "test accuracy %g"%accuracy.eval(feed_dict={

# x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})

test_set_x = load_data.load_test_data("/home/darshan/Documents/DigitRecognizer/MNIST_data/",

"test.csv")

print(test_set_x.shape)

nbr_of_test_batches = 10

batch_size = load_data.nbr_of_test_dp / nbr_of_test_batches

for j in xrange(nbr_of_test_batches):

test_batch = load_data.get_test_batch(batch_size)

if test_batch is not None:

y_predict = tf.argmax(y_conv, 1)

result_value = sess.run(y_predict, feed_dict={x: test_batch, keep_prob: 1.0})

result_label = xrange((batch_size * j) + 1, (batch_size * (j + 1)) + 1)

z = np.array(zip(result_label, result_value), dtype=[('ImageId', int), ('Label', int)])

np.savetxt('result_cnn' + str(j) + '.csv', z, fmt='%i,%i')

sess.close()