DL_waste_biodegradable_accuracy-78.py

#imports
import os
import numpy as np
import cv2
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from sklearn.model_selection import train_test_split
from math import floor
IMG_S1,IMG_S2 = 80,80
X=[]
Y=[]
epsilon = 1e-3
def imgread_scale_resize(PATH,img):
    img_array = cv2.imread(os.path.join(PATH,img),cv2.IMREAD_COLOR)
    img_array = cv2.resize(img_array , (IMG_S1,IMG_S2))
    img_array = cv2.GaussianBlur(img_array,(3,3),0)
    return img_array

def createTrainingData():
    datadir = "C:\\Users\\Piggy\\Downloads\\Compressed\\waste-classification-data\\DATASET\\TRAIN"
    CATEGORIES = ["O","R"]
    for ele in CATEGORIES:
        a = 0
        PATH = os.path.join(datadir,ele)
        class_num = CATEGORIES.index(ele)
        for img in os.listdir(PATH):
            try:
                img_array = imgread_scale_resize(PATH,img)
                X.append(list(img_array))
                Y.append(class_num)
                print(a)
            except Exception as e:
                pass
            a+=1
            if(a>10000): break
createTrainingData()

#Reshape and flatten X and Y
X = np.array(X)
Y = np.array(Y)

X = X.reshape(X.shape[0],-1).T
Y = Y.T
X = X/255 #normalized

#Output config C: DEPth
def one_hottie(labels,C):
    sess = tf.Session()
    C = tf.constant(C) #four shapes
    One_hot_matrix = tf.one_hot(labels,C,axis=0)
    Y = sess.run(One_hot_matrix)
    sess.close()
    return Y
Y = one_hottie(Y,2)

#splitting
X_train, X_test, y_train, y_test = train_test_split(X.T, Y.T, test_size = 0.13, random_state = 0)
del X
del Y
X_train, X_test, y_train, y_test = X_train.T, X_test.T, y_train.T, y_test.T
#creating placeholders
def create_placeholders(n_x,n_y):
    """
    Creates two placeholder of shape (n_x,none) and (n_y,none)
    n_x : shape of 1 image vector - height * width * colors(RGB or grayscale etc)
    n_y : no. of classes
    """
    X = tf.placeholder(dtype = tf.float32, shape = (n_x,None),name = "X")
    Y = tf.placeholder(dtype = tf.float32, shape = (n_y,None),name = "Y")
    return X,Y

# SHAPE OF NET : LINEAR(Z1) => RELU(A1) => LINEAR(z2) => RELU(a2) => LINEAR(z3) => SOFTMAX(y)

#model functions
def initialize_parameters():
    """
        W1 : [n[1], shape of 1 image vector]
        b1 : [n[1], 1]
        W2 : [n[2], n[1]]
        b2 : [n[2], 1]
        W3 : [y[1], n[2]]
        b3 : [y[1], 1]
    Returns:
    parameters -- a dictionary of tensors containing W1, b1, W2, b2, W3, b3
    """ 
    W1 = tf.get_variable(name= "W1", shape= [20,19200], initializer = tf.contrib.layers.xavier_initializer())
    b1 = tf.get_variable(name= "b1", shape= [20,1], initializer = tf.zeros_initializer())
    W2 = tf.get_variable(name= "W2", shape= [9,20], initializer = tf.contrib.layers.xavier_initializer())
    b1 = tf.get_variable(name= "b2", shape= [9,1], initializer = tf.zeros_initializer())
    W3 = tf.get_variable(name= "W3", shape= [2,9], initializer = tf.contrib.layers.xavier_initializer())
    b1 = tf.get_variable(name= "b3", shape= [2,1], initializer = tf.zeros_initializer())

    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2":b2,
                  "W3": W3,
                 "b3":b3,}
    return parameters

#forward prop 
def forward_propagation(X, parameters):
    """
    Arguments:
    X : placeholder of shape (input size, number of examples)
    parameters : dict of W and b

    Returns:
    Z3 -- the output of the last LINEAR unit
    """
     
    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)
    A1 = tf.nn.relu(Z1)
    Z2 = tf.add(tf.matmul(W2,A1),b2)
    A2 = tf.nn.relu(Z2)            
    Z3 = tf.add(tf.matmul(W3,A2),b3)
    
    return Z3  #Linear Z3

def compute_cost(Z3, Y):
    """
    Computes the cost
    
    Arguments:
    Z3 : Linear unit output of forward prop ...of shape (6, number of examples)
    Y : True vales placeholder, same shape as Z3
    
    Returns:
    cost - Tensor of the cost function
    """
    
    # 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 random_mini_batches(X_train, y_train, minibatch_size, seed):
    m = X_train.shape[1]                  # number of training examples
    mini_batches = []
    np.random.seed(seed)
    num_complete_minibatches = floor(m/minibatch_size) # number of mini batches of size minibatch_size in your partitionning
    for k in range(0, num_complete_minibatches):
        mini_batch_X = X_train[:, k * minibatch_size : k * minibatch_size + minibatch_size]
        mini_batch_Y = y_train[:, k * minibatch_size : k * minibatch_size + minibatch_size]
        mini_batch = (mini_batch_X, mini_batch_Y)
        mini_batches.append(mini_batch)
    
    # Handling the end case (last mini-batch < minibatch_size)
    if m % minibatch_size != 0:
        mini_batch_X = X_train[:, num_complete_minibatches * minibatch_size : m]
        mini_batch_Y = y_train[:, num_complete_minibatches * minibatch_size : m]
        mini_batch = (mini_batch_X, mini_batch_Y)
        mini_batches.append(mini_batch)
    
    return mini_batches


def model(X_train, y_train, X_test, y_test, learning_rate = 0.001,
          num_epochs = 1000, minibatch_size = 100, print_cost = True):
    """
    Arguments:
    X_train -- training set, of shape (input size = 12288, number of training examples = 1080)
    y_train -- test set, of shape (output size = 6, number of training examples = 1080)
    X_test -- training set, of shape (input size = 12288, number of training examples = 120)
    y_test -- test set, of shape (output size = 6, number of test examples = 120)
    learning_rate -- learning rate of the optimization
    num_epochs -- number of epochs of the optimization loop
    minibatch_size -- size of a minibatch
    print_cost -- True to print the cost every 100 epochs
    
    Returns:
    parameters -- parameters learnt by the model. They can then be used to predict.
    """
    
    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
    
    X, Y = create_placeholders(n_x,n_y)
    parameters = initialize_parameters()
    
    Z3 = forward_propagation(X,parameters)

    cost = compute_cost(Z3,Y)
    
    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
                _ , minibatch_cost = sess.run([optimizer,cost],feed_dict={X:minibatch_X, Y:minibatch_Y})
                
                epoch_cost += minibatch_cost / num_minibatches
                
            if print_cost == True and epoch % 25 == 0:
                print ("Cost after epoch %i: %f" % (epoch, epoch_cost))
            if print_cost == True and epoch % 5 == 0:
                costs.append(epoch_cost)
                
        plt.plot(np.squeeze(costs))
        plt.ylabel('cost')
        plt.xlabel('iterations (per fives)')
        plt.title("Learning rate =" + str(learning_rate))
        plt.show()
        
        parameters = sess.run(parameters)
        print ("Parameters have been trained!")

        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,num_epochs=1000,minibatch_size = 100,learning_rate = 0.0001)
#model ready







#def predict(X, parameters):
#    
#    W1 = tf.convert_to_tensor(parameters["W1"])
#    b1 = tf.convert_to_tensor(parameters["b1"])
#    W2 = tf.convert_to_tensor(parameters["W2"])
#    b2 = tf.convert_to_tensor(parameters["b2"])
#    W3 = tf.convert_to_tensor(parameters["W3"])
#    b3 = tf.convert_to_tensor(parameters["b3"])
#    
#    params = {"W1": W1,
#              "b1": b1,
#              "W2": W2,
#              "b2": b2,
#              "W3": W3,
#              "b3": b3}
#    
#    x = tf.placeholder("float", [6400, 1947])
#    
#    z3 = forward_propagation_for_predict(x, params)
#    p = tf.argmax(z3)
#    
#    sess = tf.Session()
#    prediction = sess.run(p, feed_dict = {x:X})
#        
#    return prediction
#
#def forward_propagation_for_predict(X, parameters):
#    """
#    Implements the forward propagation for the model: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SOFTMAX
#    
#    Arguments:
#    X -- input dataset placeholder, of shape (input size, number of examples)
#    parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3"
#                  the shapes are given in initialize_parameters
#
#    Returns:
#    Z3 -- the output of the last LINEAR unit
#    """
#    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)
#    A1 = tf.nn.relu(Z1)
#    Z2 = tf.add(tf.matmul(W2, A1), b2)
#    A2 = tf.nn.relu(Z2)
#    Z3 = tf.add(tf.matmul(W3, A2), b3)
#    
#    return Z3
#
#
#predictions = predict(X_test,parameters)