import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import matplotlib.pyplot as plt
import numpy as np
import math

#Import 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')

def getActivations(layer,stimuli):
    units = sess.run(layer,feed_dict={x:np.reshape(stimuli,[1,784], order='F'),keep_prob:1.0})
    plotNNFilter(units)

def plotNNFilter(units):
    filters = units.shape[3]
    plt.figure(1, figsize=(20,20))
    n_columns = 6
    n_rows = math.ceil(filters / n_columns) + 1
    for i in range(filters):
        plt.subplot(n_rows, n_columns, i+1)
        plt.title('Filter ' + str(i))
        plt.imshow(units[0,:,:,i], interpolation="nearest", cmap="gray")
        plt.show()

# Create the model
def create_model():
    x = tf.placeholder(tf.float32, [None, 784])
    #norm_x = tf.layers.batch_normalization(x, axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer=tf.zeros_initializer(),
    #gamma_initializer=tf.ones_initializer(), moving_mean_initializer=tf.zeros_initializer(), moving_variance_initializer=tf.ones_initializer(), beta_regularizer=None,
    #gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, training=False, trainable=True, name=None, reuse=None, renorm=False,
    #renorm_clipping=None, renorm_momentum=0.99, fused=None)

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

    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])

    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

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

    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    W_conv3 = weight_variable([5, 5, 64, 64])   #extra
    b_conv3 = bias_variable([64]))   #extra

    h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3))   #extra
    h_pool3 = max_pool_2x2(h_conv3))   #extra

    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(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

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

    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
    return x, keep_prob, h_conv3, y_conv

x, keep_prob, h_conv, y_conv = create_model()

# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, 10])
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))
loss = tf.reduce_mean(cross_entropy)

# Add ops to save and restore all the variables.
saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(20000):
        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})
            train_loss = loss.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
            print('step: (%d), training accuracy: (%g) training loss: (%g)' % (i, train_accuracy, train_loss))
            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}))
    print('test loss: (%g)' % loss.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
    
    save_path = saver.save(sess, "my_models_tmp/model.ckpt")
    print("Model saved in file: %s" % save_path)
    
    saver.restore(sess, "my_models_tmp/model.ckpt")
    print("Model restored.")



    print('---------------------')
    print("Test sample:")

    #Get 28x28 image
    img_to_use = mnist.test.images[47]
    sample_1 = img_to_use.reshape(28,28)

    # Get corresponding integer label from one-hot encoded data
    sample_label_1 = np.where(mnist.test.labels[47] == 1)[0][0]

    # Plot sample
    plt.imshow(sample_1, cmap='Greys')
    plt.title('label = {}'.format(sample_label_1))
    plt.show()
    predicted_value = tf.argmax(y_conv, 1)
    print('is classified as: {}'.format(predicted_value.eval(session= sess, feed_dict={x:img_to_use.reshape(1,784),keep_prob: 1.0})))
    print("conv layer:")
    getActivations(h_conv,img_to_use)
