import tensorflow as tf
import numpy as np
from scipy import misc
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)


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')

x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
# Layer 1
W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variable([32])



# Layer 2
W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variable([64])


#Layer 3

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])



# Droupout

keep_prob = tf.placeholder(tf.float32)


# Output layer

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])




def conv_model(x):
    # Layer 1
    x_image = tf.reshape(x, [-1, 28, 28, 1])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)            # Convolution
    h_pool1 = max_pool_2x2(h_conv1)                                     # Pooling

    # Layer 2
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)            # Convolution
    h_pool2 = max_pool_2x2(h_conv2)                                     # Pooling

    # Layer 3
    # Fully connected layer
    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)


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

    # Output
    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

    return y_conv

def train_model():
    y_conv = conv_model(x)
    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=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, tf.float32))
    saver = tf.train.Saver()
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(10000):
            batch = mnist.train.next_batch(50)
            if i % 100 == 0:

                train_accuracy = accuracy.eval(feed_dict={
                                            x: batch[0], y_: batch[1], keep_prob: 1.0})
                print('step %d, training accuracy %g' % (i, train_accuracy))
            train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
        print('test accuracy %g' % accuracy.eval(feed_dict={
          x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
        save_path = saver.save(sess,'temp/mnist.ckpt')
        print('Model saved in file: %s' % save_path)

def use_model(input_data):
    saver = tf.train.Saver()
    prediction = conv_model(x)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        saver.restore(sess, "temp/mnist.ckpt")
        features = misc.imread(input_data)
        features = np.ravel(features[:,:,1]).astype('float')
        result = (sess.run(tf.argmax(prediction.eval(feed_dict={x: [features], keep_prob: 1.}), 1)))
        print(result)
