model_1.py

import numpy as np
import glob
from sklearn.model_selection import train_test_split
import sys
import matplotlib.pyplot as plt

plt.rcParams['figure.figsize'] = (5.0, 4.0)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'

def sigmoid(Z):
    """
    Implements the sigmoid activation in numpy
    Arguments:
    Z -- numpy array of any shape
    Returns:
    A -- output of sigmoid(z), same shape as Z
    cache -- returns Z as well, useful during backpropagation
    """
    A = 1/(1+np.exp(-Z))
    cache = Z 
    return A, cache

def relu(Z):
    """
    Implement the RELU function.
    Arguments:
    Z -- Output of the linear layer, of any shape
    Returns:
    A -- Post-activation parameter, of the same shape as Z
    cache -- a python dictionary containing "A" ; stored for computing the backward pass efficiently
    """
    A = np.maximum(0,Z)
    assert(A.shape == Z.shape)
    cache = Z 
    return A, cache

def relu_backward(dA, cache):
    """
    Implement the backward propagation for a single RELU unit.
    Arguments:
    dA -- post-activation gradient, of any shape
    cache -- 'Z' where we store for computing backward propagation efficiently
    Returns:
    dZ -- Gradient of the cost with respect to Z
    """
    Z = cache
    dZ = np.array(dA, copy=True) # just converting dz to a correct object.
    # When z <= 0, you should set dz to 0 as well. 
    dZ[Z <= 0] = 0
    assert (dZ.shape == Z.shape)
    return dZ

def sigmoid_backward(dA, cache):
    """
    Implement the backward propagation for a single SIGMOID unit.
    Arguments:
    dA -- post-activation gradient, of any shape
    cache -- 'Z' where we store for computing backward propagation efficiently
    Returns:
    dZ -- Gradient of the cost with respect to Z
    """
    Z = cache
    s = 1/(1+np.exp(-Z))
    dZ = dA * s * (1-s)
    assert (dZ.shape == Z.shape)
    return dZ

def load_data():
    print("Loading data set...")
    image_array = np.zeros((1, 1800), 'float')
    label_array = np.zeros((1, 7), 'float')
    # Retrieve a list of pathname that matches the below expr
    data_set = glob.glob("training_data/*.npz")
    if not data_set:
        print("No data set in directory, exiting!")
        sys.exit()
    for single_npz in data_set:
        with np.load(single_npz) as data:
            temp_images = data["train"]
            temp_labels = data["train_labels"]
            temp_images=((temp_images-np.mean(temp_images))/np.std(temp_images))
        image_array = np.vstack((image_array, temp_images))
        label_array = np.vstack((label_array, temp_labels))
    X = np.float32(image_array[1:, :])
    Y = np.float32(label_array[1:, :])
    print("Image array shape: {0}".format(X.shape))
    print("Label array shape: {0}".format(Y.shape))
    classes = np.identity(7)    
    train_X, test_X, train_Y, test_Y = train_test_split(X, Y, test_size=0.2)
    return train_X.T, train_Y.T, test_X.T, test_Y.T, classes

def initialize_parameters_deep(layer_dims):
    """
    Arguments:
    layer_dims -- python array (list) containing the dimensions of each layer in our network
    
    Returns:
    parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL":
                    Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])
                    bl -- bias vector of shape (layer_dims[l], 1)
    """
    parameters = {}
    L = len(layer_dims) - 1
    for l in range(1, L+1):
        parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1])*np.sqrt(2./layer_dims[l-1])
        parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
        assert(parameters['W' + str(l)].shape == (layer_dims[l], layer_dims[l - 1]))
        assert(parameters['b' + str(l)].shape == (layer_dims[l], 1))
    return parameters

def linear_forward(A, W, b):
    """
    Implement the linear part of a layer's forward propagation.

    Arguments:
    A -- activations from previous layer (or input data): (size of previous layer, number of examples)
    W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)
    b -- bias vector, numpy array of shape (size of the current layer, 1)

    Returns:
    Z -- the input of the activation function, also called pre-activation parameter 
    cache -- a python dictionary containing "A", "W" and "b" ; stored for computing the backward pass efficiently
    """
    Z = np.dot(W, A) + b
    #assert(Z.shape == (W.shape[0], A.shape[1]))
    cache = (A, W, b)
    return Z, cache

def linear_activation_forward(A_prev, W, b, activation):
    """
    Implement the forward propagation for the LINEAR->ACTIVATION layer

    Arguments:
    A_prev -- activations from previous layer (or input data): (size of previous layer, number of examples)
    W -- weights matrix: numpy array of shape (size of current layer, size of previous layer)
    b -- bias vector, numpy array of shape (size of the current layer, 1)
    activation -- the activation to be used in this layer, stored as a text string: "sigmoid" or "relu"

    Returns:
    A -- the output of the activation function, also called the post-activation value 
    cache -- a python dictionary containing "linear_cache" and "activation_cache";
             stored for computing the backward pass efficiently
    """
    if activation == "sigmoid":
        Z, linear_cache = linear_forward(A_prev, W, b)
        A, activation_cache = sigmoid(Z)
    elif activation == "relu":
        Z, linear_cache = linear_forward(A_prev, W, b)
        A, activation_cache = relu(Z)
    assert (A.shape == (W.shape[0], A_prev.shape[1]))
    cache = (linear_cache, activation_cache)
    return A, cache

def L_model_forward(X, parameters):
    """
    Implement forward propagation for the [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID computation
    
    Arguments:
    X -- data, numpy array of shape (input size, number of examples)
    parameters -- output of initialize_parameters_deep()
    
    Returns:
    AL -- last post-activation value
    caches -- list of caches containing:
                every cache of linear_relu_forward() (there are L-1 of them, indexed from 0 to L-2)
                the cache of linear_sigmoid_forward() (there is one, indexed L-1)
    """
    caches = []
    A = X
    L = len(parameters) // 2                  # number of layers in the neural network
    # Implement [LINEAR -> RELU]*(L-1). Add "cache" to the "caches" list.
    for l in range(1, L):
        A_prev = A 
        A, cache = linear_activation_forward(A_prev, 
                                             parameters['W' + str(l)], 
                                             parameters['b' + str(l)], 
                                             activation='relu')
        caches.append(cache)
    # Implement LINEAR -> SIGMOID. Add "cache" to the "caches" list.
    AL, cache = linear_activation_forward(A, 
                                          parameters['W' + str(L)], 
                                          parameters['b' + str(L)], 
                                          activation='sigmoid')
    caches.append(cache)
    #assert(AL.shape == (1, X.shape[1]))
    #print(AL.shape)
    return AL, caches

def compute_cost(AL, Y):
    """
    Implement the cost function defined by equation (7).

    Arguments:
    AL -- probability vector corresponding to your label predictions, shape (1, number of examples)
    Y -- true "label" vector (for example: containing 0 if non-cat, 1 if cat), shape (1, number of examples)

    Returns:
    cost -- cross-entropy cost
    """
    m = Y.shape[1]
    # Compute loss from aL and y.
    #cost = (-1 / m) * np.sum(np.multiply(Y, np.log(AL)) + np.multiply(1 - Y, np.log(1 - AL)))
    cost = (-1/m)*np.nansum(np.multiply(Y, np.log(AL)) + np.multiply(1 - Y, np.log(1 - AL)))
    cost = np.squeeze(cost) 
    #print(cost.shape)     # To make sure your cost's shape is what we expect (e.g. this turns [[17]] into 17).
    assert(cost.shape == ())
    return cost

def linear_backward(dZ, cache):
    """
    Implement the linear portion of backward propagation for a single layer (layer l)

    Arguments:
    dZ -- Gradient of the cost with respect to the linear output (of current layer l)
    cache -- tuple of values (A_prev, W, b) coming from the forward propagation in the current layer

    Returns:
    dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev
    dW -- Gradient of the cost with respect to W (current layer l), same shape as W
    db -- Gradient of the cost with respect to b (current layer l), same shape as b
    """
    A_prev, W, b = cache
    m = A_prev.shape[1]

    dW = 1./m*np.dot(dZ, A_prev.T)
    db = 1./m*np.sum(dZ, axis = 1, keepdims = True)
    dA_prev = np.dot(W.T, dZ)
    assert (dA_prev.shape == A_prev.shape)
    assert (dW.shape == W.shape)
    assert (db.shape == b.shape)
    return dA_prev, dW, db

def linear_activation_backward(dA, cache, activation):
    """
    Implement the backward propagation for the LINEAR->ACTIVATION layer.
    
    Arguments:
    dA -- post-activation gradient for current layer l 
    cache -- tuple of values (linear_cache, activation_cache) we store for computing backward propagation efficiently
    activation -- the activation to be used in this layer, stored as a text string: "sigmoid" or "relu"
    
    Returns:
    dA_prev -- Gradient of the cost with respect to the activation (of the previous layer l-1), same shape as A_prev
    dW -- Gradient of the cost with respect to W (current layer l), same shape as W
    db -- Gradient of the cost with respect to b (current layer l), same shape as b
    """
    linear_cache, activation_cache = cache
    if activation == "relu":
        dZ = relu_backward(dA, activation_cache)
        dA_prev, dW, db = linear_backward(dZ, linear_cache)
    elif activation == "sigmoid":
        dZ = sigmoid_backward(dA, activation_cache)
        dA_prev, dW, db = linear_backward(dZ, linear_cache)
    return dA_prev, dW, db

def L_model_backward(AL, Y, caches):
    """
    Implement the backward propagation for the [LINEAR->RELU] * (L-1) -> LINEAR -> SIGMOID group
    
    Arguments:
    AL -- probability vector, output of the forward propagation (L_model_forward())
    Y -- true "label" vector (containing 0 if non-cat, 1 if cat)
    caches -- list of caches containing:
                every cache of linear_activation_forward() with "relu" (there are (L-1) or them, indexes from 0 to L-2)
                the cache of linear_activation_forward() with "sigmoid" (there is one, index L-1)
    Returns:
    grads -- A dictionary with the gradients
             grads["dA" + str(l)] = ... 
             grads["dW" + str(l)] = ...
             grads["db" + str(l)] = ... 
    """
    grads = {}
    L = len(caches) # the number of layers
    m = AL.shape[1]
    Y = Y.reshape(AL.shape) # after this line, Y is the same shape as AL
    # Initializing the backpropagation
    dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
    # Lth layer (SIGMOID -> LINEAR) gradients. Inputs: "AL, Y, caches". Outputs: "grads["dAL"], grads["dWL"], grads["dbL"]
    current_cache = caches[L-1]
    grads["dA" + str(L)], grads["dW" + str(L)], grads["db" + str(L)] = linear_activation_backward(dAL, current_cache, activation = "sigmoid")
    for l in reversed(range(L-1)):
        # lth layer: (RELU -> LINEAR) gradients.
        current_cache = caches[l]
        dA_prev_temp, dW_temp, db_temp = linear_activation_backward(grads["dA" + str(l + 2)], current_cache, activation = "relu")
        grads["dA" + str(l + 1)] = dA_prev_temp
        grads["dW" + str(l + 1)] = dW_temp
        grads["db" + str(l + 1)] = db_temp
    return grads

def update_parameters(parameters, grads, learning_rate):
    """
    Update parameters using gradient descent
    
    Arguments:
    parameters -- python dictionary containing your parameters 
    grads -- python dictionary containing your gradients, output of L_model_backward
    
    Returns:
    parameters -- python dictionary containing your updated parameters 
                  parameters["W" + str(l)] = ... 
                  parameters["b" + str(l)] = ...
    """
    L = len(parameters) // 2 # number of layers in the neural network
    # Update rule for each parameter. Use a for loop.
    for l in range(L):
        parameters["W" + str(l + 1)] = parameters["W" + str(l + 1)] - learning_rate * grads["dW" + str(l + 1)]
        parameters["b" + str(l + 1)] = parameters["b" + str(l + 1)] - learning_rate * grads["db" + str(l + 1)]
    return parameters


def predict(X, y, parameters):
    """
    This function is used to predict the results of a  L-layer neural network.
    
    Arguments:
    X -- data set of examples you would like to label
    parameters -- parameters of the trained model
    
    Returns:
    p -- predictions for the given dataset X
    """
    count=0
    m = X.shape[1]
    n = len(parameters) // 2 # number of layers in the neural network
    p = np.zeros((7,m))
    #print(probas.shape[1])
    # Forward propagation
    probas, caches = L_model_forward(X, parameters)
    #print(probas.shape[1])
    print(probas)
    # convert probas to 0/1 predictions
    for j in range(0, probas.shape[1]):
        index = np.argmax(probas[:,j])
        p[index,j] = 1
        if y[index,j] == p[index,j]:
            count+=1
    #print ("predictions: " + str(p))
    #print ("true labels: " + str(y))
    result=(count*100)/m
    print("Accuracy: "  + str(result))    
    return p

def print_mislabeled_images(classes, X, y, p):
    """
    Plots images where predictions and truth were different.
    X -- dataset
    y -- true labels
    p -- predictions
    """
    a = p + y
    mislabeled_indices = np.asarray(np.where(a == 1))
    plt.rcParams['figure.figsize'] = (40.0, 40.0) # set default size of plots
    num_images = len(mislabeled_indices[0])
    for i in range(num_images):
        index = mislabeled_indices[1][i]
        plt.subplot(2, num_images, i + 1)
        plt.imshow(X[:,index].reshape(64,64,3), interpolation='nearest')
        plt.axis('off')
        plt.title("Prediction: " + classes[int(p[0,index])].decode("utf-8") + " \n Class: " + classes[y[0,index]].decode("utf-8"))


train_x, train_y, test_x, test_y, classes = load_data()        
##train_x_orig, train_y, test_x_orig, test_y, classes = load_data()
### Standardize data to have feature values between 0 and 1.
##train_x = train_x_orig/255.
##test_x = test_x_orig/255.


print ("train_x's shape: " + str(train_x.shape))
print ("test_x's shape: " + str(test_x.shape))
print("y_size " + str(train_y.shape))
#print(train_x)
layers_dims = [1800, 100, 7] #  5-layer model



def L_layer_model(X, Y, layers_dims, learning_rate_init = 0.0625, learning_rate_final = 0.00625, num_iterations = 1000, print_cost=False):#lr was 0.009
    """
    Implements a L-layer neural network: [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID.
    Arguments:
    X -- data, numpy array of shape (number of examples, num_px * num_px * 3)
    Y -- true "label" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)
    layers_dims -- list containing the input size and each layer size, of length (number of layers + 1).
    learning_rate -- learning rate of the gradient descent update rule
    num_iterations -- number of iterations of the optimization loop
    print_cost -- if True, it prints the cost every 100 steps
    Returns:
    parameters -- parameters learnt by the model. They can then be used to predict.
    """
    
    costs = []                         # keep track of cost
    # Parameters initialization.
    parameters = initialize_parameters_deep(layers_dims)
    # Loop (gradient descent)
    k=(learning_rate_final-learning_rate_init)/num_iterations
    learning_rate=learning_rate_init
    for i in range(0, num_iterations):
        # Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID.
        AL, caches = L_model_forward(X, parameters)
        # Compute cost.
        cost = compute_cost(AL, Y)
        # Backward propagation.
        grads = L_model_backward(AL, Y, caches)
        # Update parameters.
        parameters = update_parameters(parameters, grads, learning_rate)    
        # Print the cost every 100 training example
        if((i+1)%20==0):
            learning_rate=(k*i)+learning_rate_init
            
            #print ("Cost after iteration %i: %f" %(i, cost))
            #print (learning_rate)
        if((i+1)%1==0):
            costs.append(cost)
            print ("Cost after iteration %i: %f" %(i, 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()
    return parameters

parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations = 1000, print_cost = True)
pred_train = predict(train_x, train_y, parameters)
pred_test = predict(test_x, test_y, parameters)
np.savez('newparameters.npz', Weights1=parameters["W1"], Weights2=parameters["W2"], Bias1=parameters["b1"], Bias2=parameters["b2"])