tf.py

from load_cifar import *
import tensorflow as tf

def predict(X, parameters):
    
    x = tf.placeholder(tf.float32, [X.shape[0], None])
    
    z3 = forward_propagation_for_predict(x, parameters)
    p = tf.argmax(z3)
    
    sess = tf.Session()
    prediction = sess.run(p, feed_dict = {x: X})
        
    return prediction

def forward_propagation_for_predict(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'] 
                                                           # Numpy Equivalents:
    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

def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.0001,
          num_epochs = 1500, print_cost = True):
    
    (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 = tf.placeholder(tf.float32, [n_x, None])
    Y = tf.placeholder(tf.float32, [n_y, None])
    
    # Initialize parameters
    W1 = tf.get_variable("W1", [25,3072], initializer = tf.contrib.layers.xavier_initializer(seed = 1))
    b1 = tf.get_variable("b1", [25,1], initializer = tf.zeros_initializer())
    W2 = tf.get_variable("W2", [5,25], initializer = tf.contrib.layers.xavier_initializer(seed = 1))
    b2 = tf.get_variable("b2", [5,1], initializer = tf.zeros_initializer())
    W3 = tf.get_variable("W3", [1,5], initializer = tf.contrib.layers.xavier_initializer(seed = 1))
    b3 = tf.get_variable("b3", [1,1], initializer = tf.zeros_initializer())
    
    # Forward propagation: Build the forward propagation in the tensorflow graph
    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
    
    parameters = {"W1": W1,
                  "b1": b1,
                  "W2": W2,
                  "b2": b2,
                  "W3": W3,
                  "b3": b3}
    
    # Cost function: Add cost function to tensorflow graph
    logits = tf.transpose(Z3)
    labels = tf.transpose(Y)
    cost =  tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = logits,  labels = labels))
    
    # 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
                
            # Run the session to execute the "optimizer" and the "cost", the feedict should contain a minibatch for (X,Y).
            _ , epoch_cost = sess.run([optimizer, cost], feed_dict={X: X_train, Y: Y_train})

            # 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)
                
        # lets save the parameters in a variable
        parameters = sess.run(parameters)
        print ("Parameters have been trained!")

        return parameters

imagearray, labelarray = load_batch()
X_train, Y_train, X_test, Y_test = create_datasets(imagearray, labelarray)

parameters = model(X_train, Y_train, X_test, Y_test, num_epochs = 1500)
print("train accuracy: {} %".format(100 - np.mean(np.abs(predict(X_train, parameters) - Y_train)) * 100))
print("test accuracy: {} %".format(100 - np.mean(np.abs(predict(X_test, parameters) -  Y_test)) * 100))