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

#%matplotlib inline
np.random.seed(1)

# Loading the dataset
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()

# Example of a picture
index = 37
plt.imshow(X_train_orig[index])
print ("y = " + str(np.squeeze(Y_train_orig[:, index])))

# 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)

print ("number of training examples = " + str(X_train.shape[1]))
print ("number of test examples = " + str(X_test.shape[1]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))




#  create_placeholders

def create_placeholders(n_x, n_y):
    
    
    X = tf.placeholder(tf.float32,[n_x,None] ,name = "x")
    Y = tf.placeholder(tf.float32 ,[n_y,None] ,name ="y")
    
    
    return X, Y

# initialize_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



# forward_propagation

def forward_propagation(X, parameters):
    
    
    # Retrieve the parameters from the dictionary "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




# compute_cost 

def compute_cost(Z3, Y):
    
    
    # to fit the tensorflow requirement for tf.nn.softmax_cross_entropy_with_logits(...,...)
    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




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 = []                                        # To keep track of the cost
    
    # Create Placeholders of shape (n_x, n_y)
    
    X, Y = create_placeholders(n_x,n_y)
    
    
    # Initialize parameters
    
    parameters = initialize_parameters()
    
    
    # Forward propagation: Build the forward propagation in the tensorflow graph
    
    Z3 = forward_propagation(X,parameters)
    
    
    # Cost function: Add cost function to tensorflow graph
    
    cost = compute_cost(Z3 ,Y)
    
    
    # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.
    
    optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
    
    
    # Initialize all the variables
    init = tf.global_variables_initializer()

    # Start the session to compute the tensorflow graph
    with tf.Session() as sess:
        
        # Run the initialization
        sess.run(init)
        
        # Do the training loop
        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:

                # Select a minibatch
                (minibatch_X, minibatch_Y) = minibatch
                
                # IMPORTANT: The line that runs the graph on a 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)


# test on your own image

import scipy
from PIL import Image
from scipy import ndimage

## START CODE HERE ## (PUT YOUR IMAGE NAME) 
my_image = "thumbs_up.jpg"
## END CODE HERE ##

# We preprocess your image to fit your algorithm.
fname = "images/" + my_image
image = np.array(ndimage.imread(fname, flatten=False))
my_image = scipy.misc.imresize(image, size=(64,64)).reshape((1, 64*64*3)).T
my_image_prediction = predict(my_image, parameters)

plt.imshow(image)
print("Your algorithm predicts: y = " + str(np.squeeze(my_image_prediction)))
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

#%matplotlib inline
np.random.seed(1)

# Loading the dataset
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()

# Example of a picture
index = 37
plt.imshow(X_train_orig[index])
print ("y = " + str(np.squeeze(Y_train_orig[:, index])))

# 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)

print ("number of training examples = " + str(X_train.shape[1]))
print ("number of test examples = " + str(X_test.shape[1]))
print ("X_train shape: " + str(X_train.shape))
print ("Y_train shape: " + str(Y_train.shape))
print ("X_test shape: " + str(X_test.shape))
print ("Y_test shape: " + str(Y_test.shape))




#  create_placeholders

def create_placeholders(n_x, n_y):
    
    
    X = tf.placeholder(tf.float32,[n_x,None] ,name = "x")
    Y = tf.placeholder(tf.float32 ,[n_y,None] ,name ="y")
    
    
    return X, Y

# initialize_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



# forward_propagation

def forward_propagation(X, parameters):
    
    
    # Retrieve the parameters from the dictionary "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




# compute_cost 

def compute_cost(Z3, Y):
    
    
    # to fit the tensorflow requirement for tf.nn.softmax_cross_entropy_with_logits(...,...)
    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




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 = []                                        # To keep track of the cost
    
    # Create Placeholders of shape (n_x, n_y)
    
    X, Y = create_placeholders(n_x,n_y)
    
    
    # Initialize parameters
    
    parameters = initialize_parameters()
    
    
    # Forward propagation: Build the forward propagation in the tensorflow graph
    
    Z3 = forward_propagation(X,parameters)
    
    
    # Cost function: Add cost function to tensorflow graph
    
    cost = compute_cost(Z3 ,Y)
    
    
    # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer.
    
    optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)
    
    
    # Initialize all the variables
    init = tf.global_variables_initializer()

    # Start the session to compute the tensorflow graph
    with tf.Session() as sess:
        
        # Run the initialization
        sess.run(init)
        
        # Do the training loop
        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:

                # Select a minibatch
                (minibatch_X, minibatch_Y) = minibatch
                
                # IMPORTANT: The line that runs the graph on a 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)


# test on your own image

import scipy
from PIL import Image
from scipy import ndimage

## START CODE HERE ## (PUT YOUR IMAGE NAME) 
my_image = "thumbs_up.jpg"
## END CODE HERE ##

# We preprocess your image to fit your algorithm.
fname = "images/" + my_image
image = np.array(ndimage.imread(fname, flatten=False))
my_image = scipy.misc.imresize(image, size=(64,64)).reshape((1, 64*64*3)).T
my_image_prediction = predict(my_image, parameters)

plt.imshow(image)
print("Your algorithm predicts: y = " + str(np.squeeze(my_image_prediction)))
#%matplotlib inline # Loading the dataset # Example of a picture # Flatten the training and test images # Normalize image vectors # Convert training and test labels to one hot matrices #  create_placeholders # initialize_parameters # forward_propagation # Retrieve the parameters from the dictionary "parameters" # Z1 = np.dot(W1, X) + b1 # A1 = relu(Z1) # Z2 = np.dot(W2, a1) + b2 # A2 = relu(Z2) # Z3 = np.dot(W3,Z2) + b3 # compute_cost # to fit the tensorflow requirement for tf.nn.softmax_cross_entropy_with_logits(...,...) # to be able to rerun the model without overwriting tf variables # to keep consistent results # to keep consistent results # (n_x: input size, m : number of examples in the train set) # n_y : output size # To keep track of the cost # Create Placeholders of shape (n_x, n_y) # Initialize parameters # Forward propagation: Build the forward propagation in the tensorflow graph # Cost function: Add cost function to tensorflow graph # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer. # Initialize all the variables # Start the session to compute the tensorflow graph # Run the initialization # Do the training loop # Defines a cost related to an epoch # number of minibatches of size minibatch_size in the train set # Select a minibatch # IMPORTANT: The line that runs the graph on a minibatch. # Run the session to execute the "optimizer" and the "cost", the feedict should contain a minibatch for (X,Y). # Print the cost every epoch # plot the cost # lets save the parameters in a variable # Calculate the correct predictions # Calculate accuracy on the test set # test on your own image ## START CODE HERE ## (PUT YOUR IMAGE NAME) ## END CODE HERE ## # We preprocess your image to fit your algorithm.