# -*- coding: utf-8 -*-
"""
Created on Mon Jan 21 21:56:37 2019

@author: Dcm
"""
"""TensorFlow Tutorial"""
import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict

np.random.seed(1)

# =============================================================================
# yhat = tf.constant(36, name='yhat')
# y = tf.constant(39, name='y')
# loss = tf.Variable((y - yhat) **2, name='loss')
# init = tf.global_variables_initializer()
# with tf.Session() as session:
#     session.run(init)
#     print(session.run(loss))
# 
# sess = tf.Session()
# x = tf.placeholder(tf.int64, name = 'x')
# print(sess.run(2 * x, feed_dict = {x: 3}))
# sess.close()
# 
# =============================================================================

"""1.1 - Linear function"""
def linear_function():
    np.random.seed(1)
    X = tf.constant(np.random.randn(3,1), name='X')
    W = tf.constant(np.random.randn(4,3), name='W')
    b = tf.constant(np.random.randn(4,1), name='b')
    Y = tf.add(tf.matmul(W, X), b)

    sess = tf.Session()
    result = sess.run(Y)
    
    session.close()
    return result

"""1.2 - Computing the sigmoid"""
def sigmoid(z):
    x = tf.placeholder(tf.float32, name='x')
    sigmoid = tf.sigmoid(x)
    with tf.Session() as sess:
        result = sess.run(sigmoid, feed_dict={x:z})
        
    return result

"""1.3 - Computing the Cost"""
def cost(logits, labels):
    z = tf.placeholder(tf.float32, name='z')
    y = tf.placeholder(tf.float32, name='y')
    cost = tf.nn.sigmoid_cross_entropy_with_logits(logits=z, labels=y)
    with tf.Session() as sess:
        result = sess.run(cost, feed_dict={z:logits, y:labels})
    return result

"""1.4 - Using One Hot encodings"""
def one_hot_matrix(labels, C):
    C = tf.constant(value=C, name='C')
    one_hot = tf.one_hot(labels, C, axis=0)
    with tf.Session() as sess:
        result = sess.run(one_hot)
    return result

"""1.5 - Initialize with zeros and ones"""
def ones(shape):
    ones = tf.ones(shape)
    with tf.Session() as sess:
        result = sess.run(ones)
    return result

""" 2- Building your first neural network in tensorflow"""
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()
# Flatten the training and test images
X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T
X_test_flatten = X_test_orig.reshape(X_test_orig.shape[0], -1).T
# Normalize image vectors
X_train = X_train_flatten/255.
X_test = X_test_flatten/255.
# Convert training and test labels to one hot matrices
Y_train = convert_to_one_hot(Y_train_orig, 6)
Y_test = convert_to_one_hot(Y_test_orig, 6)

"""2.1 - Create placeholders"""
def create_placeholders(n_x, n_y):
    X = tf.placeholder(tf.float32, shape=[n_x, None])
    Y = tf.placeholder(tf.float32, shape=[n_y, None])
    return X, Y

"""2.2 - Initializing the parameters"""
def initialize_parameters():
    tf.set_random_seed(1)
    W1 = tf.get_variable("W1", [25,12288], initializer = tf.contrib.layers.xavier_initializer(seed = 1))
    b1 = tf.get_variable("b1", [25,1], initializer = tf.zeros_initializer())
    W2 = tf.get_variable("W2", [12,25], initializer = tf.contrib.layers.xavier_initializer(seed = 1))
    b2 = tf.get_variable("b2", [12,1], initializer = tf.zeros_initializer())
    W3 = tf.get_variable("W3",[6,12], initializer = tf.contrib.layers.xavier_initializer(seed = 1))
    b3 = tf.get_variable("b3", [6,1], initializer = tf.zeros_initializer())
    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2,
                  "W3": W3,
                  "b3": b3}

    return parameters

"""2.3 - Forward propagation in tensorflow"""
def forward_propagation(X, parameters):
    W1 = parameters['W1']
    b1 = parameters['b1']
    W2 = parameters['W2']
    b2 = parameters['b2']
    W3 = parameters['W3']
    b3 = parameters['b3']
    Z1 = tf.add(tf.matmul(W1, X), b1)                      # Z1 = np.dot(W1, X) + b1
    A1 = tf.nn.relu(Z1)                                    # A1 = relu(Z1)
    Z2 = tf.add(tf.matmul(W2, A1), b2)                     # Z2 = np.dot(W2, a1) + b2
    A2 = tf.nn.relu(Z2)                                    # A2 = relu(Z2)
    Z3 = tf.add(tf.matmul(W3, A2), b3)                     # Z3 = np.dot(W3,Z2) + b3
    return Z3

"""2.4 Compute cost"""
def compute_cost(Z3, Y):
    logits = tf.transpose(Z3)
    labels = tf.transpose(Y)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = labels))
    return cost

"""2.5 - Backward propagation & parameter updates"""
"""2.6 - Building the model"""
def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,
          num_epochs = 1500, minibatch_size = 32, print_cost = True):
    ops.reset_default_graph()                         # to be able to rerun the model without overwriting tf variables
    tf.set_random_seed(1)                             # to keep consistent results
    seed = 3                                          # to keep consistent results
    (n_x, m) = X_train.shape                          # (n_x: input size, m : number of examples in the train set)
    n_y = Y_train.shape[0]                            # n_y : output size
    costs = []
    
    X, Y = create_placeholders(n_x, n_y)
    parameters = initialize_parameters()
    Z3 = forward_propagation(X, parameters)
    cost = compute_cost(Z3, Y)
    # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.
    optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
    
    init = tf.global_variables_initializer()
    
    with tf.Session() as sess:
        sess.run(init)
        for epoch in range(num_epochs):
            epoch_cost = 0.                       # Defines a cost related to an epoch
            num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set
            seed = seed + 1
            minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)

            for minibatch in minibatches:
                (minibatch_X, minibatch_Y) = minibatch
                # Run the session to execute the "optimizer" and the "cost", the feedict should contain a minibatch for (X,Y).
                _ , minibatch_cost = sess.run([optimizer, cost], feed_dict = {X: minibatch_X, Y: minibatch_Y})
                epoch_cost += minibatch_cost / num_minibatches
            # Print the cost every epoch
            if print_cost == True and epoch % 100 == 0:
                print ("Cost after epoch %i: %f" % (epoch, epoch_cost))
            if print_cost == True and epoch % 5 == 0:
                costs.append(epoch_cost)

        # plot the cost
        plt.plot(np.squeeze(costs))
        plt.ylabel('cost')
        plt.xlabel('iterations (per tens)')
        plt.title("Learning rate =" + str(learning_rate))
        plt.show()
        
        # lets save the parameters in a variable
        parameters = sess.run(parameters)
        print ("Parameters have been trained!")

        # Calculate the correct predictions
        correct_prediction = tf.equal(tf.argmax(Z3), tf.argmax(Y))

        # Calculate accuracy on the test set
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

        print ("Train Accuracy:", accuracy.eval({X: X_train, Y: Y_train}))
        print ("Test Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))

        return parameters
                

parameters = model(X_train, Y_train, X_test, Y_test)
