01神经网络和深度学习-Deep Neural Network for Image Classification: Application-第四周编程作业2

àì夳堔傛蜴生んèń 2022-05-12 11:48 123阅读 0赞

## 一、两层神经网络 ##

模型：LINEAR->RELU->LINEAR->SIGMOID

#coding=utf-8
    import time
    import numpy as np
    import h5py
    import matplotlib.pyplot as plt
    import scipy
    from PIL import Image
    from scipy import ndimage
    from dnn_app_utils_v2 import *
    
    plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
    plt.rcParams['image.interpolation'] = 'nearest'
    plt.rcParams['image.cmap'] = 'gray'
    
    np.random.seed(1)
    
    def initialize_parameters(n_x, n_h, n_y):
        """
        Argument:
        n_x -- size of the input layer
        n_h -- size of the hidden layer
        n_y -- size of the output layer
        
        Returns:
        parameters -- python dictionary containing your parameters:
                        W1 -- weight matrix of shape (n_h, n_x)
                        b1 -- bias vector of shape (n_h, 1)
                        W2 -- weight matrix of shape (n_y, n_h)
                        b2 -- bias vector of shape (n_y, 1)
        """
        
        np.random.seed(1)
        
        ### START CODE HERE ### (≈ 4 lines of code)
        W1 = np.random.randn(n_h, n_x) * 0.01
        b1 = np.zeros((n_h, 1))
        W2 = np.random.randn(n_y, n_h) * 0.01
        b2 = np.zeros((n_y, 1))
        ### END CODE HERE ###
        
        assert(W1.shape == (n_h, n_x))
        assert(b1.shape == (n_h, 1))
        assert(W2.shape == (n_y, n_h))
        assert(b2.shape == (n_y, 1))
        
        parameters = {"W1": W1,
                      "b1": b1,
                      "W2": W2,
                      "b2": b2}
        
        return parameters 
        
    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":
            # Inputs: "A_prev, W, b". Outputs: "A, activation_cache".
            ### START CODE HERE ### (≈ 2 lines of code)
            Z, linear_cache = linear_forward(A_prev, W, b)
            A, activation_cache = sigmoid(Z)
            ### END CODE HERE ###
        
        elif activation == "relu":
            # Inputs: "A_prev, W, b". Outputs: "A, activation_cache".
            ### START CODE HERE ### (≈ 2 lines of code)
            Z, linear_cache = linear_forward(A_prev, W, b)
            A, activation_cache = relu(Z)
            ### END CODE HERE ###
        
        assert (A.shape == (W.shape[0], A_prev.shape[1]))
        cache = (linear_cache, activation_cache)
    
        return A, cache    
        
    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.
        ### START CODE HERE ### (≈ 1 lines of code)
        cost = - 1.0 / m * np.sum(Y * np.log(AL) + (1 - Y) * np.log(1 - AL) )
        ### END CODE HERE ###
        
        cost = np.squeeze(cost)      # 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_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":
            ### START CODE HERE ### (≈ 2 lines of code)
            dZ = relu_backward(dA, activation_cache)
            dA_prev, dW, db = linear_backward(dZ, linear_cache)
            ### END CODE HERE ###
            
        elif activation == "sigmoid":
            ### START CODE HERE ### (≈ 2 lines of code)
            dZ = sigmoid_backward(dA, activation_cache)
            dA_prev, dW, db = linear_backward(dZ, linear_cache)
            ### END CODE HERE ###
        
        return dA_prev, dW, db
        
    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.
        ### START CODE HERE ### (≈ 3 lines of code)
        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)]
        ### END CODE HERE ###
            
        return parameters
        
    # GRADED FUNCTION: two_layer_model
    
    def two_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):
        """
        Implements a two-layer neural network: LINEAR->RELU->LINEAR->SIGMOID.
        
        Arguments:
        X -- input data, of shape (n_x, number of examples)
        Y -- true "label" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)
        layers_dims -- dimensions of the layers (n_x, n_h, n_y)
        num_iterations -- number of iterations of the optimization loop
        learning_rate -- learning rate of the gradient descent update rule
        print_cost -- If set to True, this will print the cost every 100 iterations 
        
        Returns:
        parameters -- a dictionary containing W1, W2, b1, and b2
        """
        
        np.random.seed(1)
        grads = {}
        costs = []                              # to keep track of the cost
        m = X.shape[1]                           # number of examples
        (n_x, n_h, n_y) = layers_dims
        
        # Initialize parameters dictionary, by calling one of the functions you'd previously implemented
        ### START CODE HERE ### (≈ 1 line of code)
        parameters = initialize_parameters(n_x, n_h, n_y)
        ### END CODE HERE ###
        
        # Get W1, b1, W2 and b2 from the dictionary parameters.
        W1 = parameters["W1"]
        b1 = parameters["b1"]
        W2 = parameters["W2"]
        b2 = parameters["b2"]
        
        # Loop (gradient descent)
    
        for i in range(0, num_iterations):
    
            # Forward propagation: LINEAR -> RELU -> LINEAR -> SIGMOID. Inputs: "X, W1, b1". Output: "A1, cache1, A2, cache2".
            ### START CODE HERE ### (≈ 2 lines of code)
            A1, cache1 = linear_activation_forward(X, W1, b1, 'relu')
            A2, cache2 = linear_activation_forward(A1, W2, b2, 'sigmoid')
            ### END CODE HERE ###
            
            # Compute cost
            ### START CODE HERE ### (≈ 1 line of code)
            cost = compute_cost(A2, Y)
            ### END CODE HERE ###
            
            # Initializing backward propagation
            dA2 = - (np.divide(Y, A2) - np.divide(1 - Y, 1 - A2))
            
            # Backward propagation. Inputs: "dA2, cache2, cache1". Outputs: "dA1, dW2, db2; also dA0 (not used), dW1, db1".
            ### START CODE HERE ### (≈ 2 lines of code)
            dA1, dW2, db2 = linear_activation_backward(dA2, cache2, 'sigmoid')
            dA0, dW1, db1 = linear_activation_backward(dA1, cache1, 'relu')
            ### END CODE HERE ###
            
            # Set grads['dWl'] to dW1, grads['db1'] to db1, grads['dW2'] to dW2, grads['db2'] to db2
            grads['dW1'] = dW1
            grads['db1'] = db1
            grads['dW2'] = dW2
            grads['db2'] = db2
            
            # Update parameters.
            ### START CODE HERE ### (approx. 1 line of code)
            parameters = update_parameters(parameters, grads, learning_rate)
            ### END CODE HERE ###
    
            # Retrieve W1, b1, W2, b2 from parameters
            W1 = parameters["W1"]
            b1 = parameters["b1"]
            W2 = parameters["W2"]
            b2 = parameters["b2"]
            
            # Print the cost every 100 training example
            if print_cost and i % 100 == 0:
                print("Cost after iteration {}: {}".format(i, np.squeeze(cost)))
            if print_cost and i % 100 == 0:
                costs.append(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
    
    
    if __name__=='__main__':  
    
        train_x_orig, train_y, test_x_orig, test_y, classes = load_data()
        
        # Example of a picture
        index = 7
        plt.imshow(train_x_orig[index])
        print ("y = " + str(train_y[0,index]) + ". It's a " + classes[train_y[0,index]].decode("utf-8") +  " picture.")
        plt.show()
        
        # Explore your dataset 
        m_train = train_x_orig.shape[0]
        num_px = train_x_orig.shape[1]
        m_test = test_x_orig.shape[0]
    
        print ("Number of training examples: " + str(m_train))
        print ("Number of testing examples: " + str(m_test))
        print ("Each image is of size: (" + str(num_px) + ", " + str(num_px) + ", 3)")
        print ("train_x_orig shape: " + str(train_x_orig.shape))
        print ("train_y shape: " + str(train_y.shape))
        print ("test_x_orig shape: " + str(test_x_orig.shape))
        print ("test_y shape: " + str(test_y.shape))
        
        # Reshape the training and test examples 
        train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T   # The "-1" makes reshape flatten the remaining dimensions
        test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T
    
        # Standardize data to have feature values between 0 and 1.
        train_x = train_x_flatten/255.
        test_x = test_x_flatten/255.
    
        print ("train_x's shape: " + str(train_x.shape))
        print ("test_x's shape: " + str(test_x.shape))
    
        ### CONSTANTS DEFINING THE MODEL ####
        n_x = 12288     # num_px * num_px * 3
        n_h = 7
        n_y = 1
        layers_dims = (n_x, n_h, n_y)
    
        parameters = two_layer_model(train_x, train_y, layers_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost=True)
    
        predictions_train = predict(train_x, train_y, parameters)
     
        predictions_test = predict(test_x, test_y, parameters)

运行结果：

![70][]

![70 1][]

![70 2][]

【注意】在文件**dnn\_app\_utils\_v2.py**中

修改第415行：**print("Accuracy: " + str(np.sum((p == y)/m)))**  
为： **print("Accuracy: " + str(np.sum((p == y))\*1.0/m))**

不然Accuracy输出为0.

## 二、L层神经网络 ##

模型：\[LINEAR->RELU\]\*(L-1)->LINEAR->SIGMOID

因为需要的函数都在文件**dnn\_app\_utils\_v2.py**中了，所以可以直接调用。

将待测试图片：

![70 3][]

命名为“my\_image.jpg”保存在文件夹"image"中。

运行结果：

![70 4][]

![70 5][]

![70 6][]

【函数 代码分析】

（1）**def initialize\_parameters\_deep(layer\_dims):**

①改变随机数种子： np.random.seed(3) 为： **np.random.seed(1)**

②改变W的初始化方法：parameters\['W' + str(l)\] = np.random.randn(layer\_dims\[l\], layer\_dims\[l-1\]) **/ np.sqrt(layer\_dims\[l-1\])**

用来避免**梯度消失**；

（2）**def compute\_cost(AL, Y):**

改变cost的计算方法：cost = - 1.0 / m \* np.sum(Y \* np.log(AL) + (1 - Y) \* np.log(1 - AL) )

为：**cost = 1.0 / m \* (-np.dot(Y,np.log(AL).T) - np.dot(1-Y, np.log(1-AL).T))**

整合如下：

#coding=utf-8
    import time
    import numpy as np
    import h5py
    import matplotlib.pyplot as plt
    import scipy
    from PIL import Image
    from scipy import ndimage
    from dnn_app_utils_v2 import *
    
    plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
    plt.rcParams['image.interpolation'] = 'nearest'
    plt.rcParams['image.cmap'] = 'gray'
    
    np.random.seed(1)
    
    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)
        """
        
        np.random.seed(1)
        parameters = {}
        L = len(layer_dims)            # number of layers in the network
     
        for l in range(1, L):
            ### START CODE HERE ### (≈ 2 lines of code)
            parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1]) / np.sqrt(layer_dims[l-1])#避免梯度消失
            parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))
            ### END CODE HERE ###
            
            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_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":
            # Inputs: "A_prev, W, b". Outputs: "A, activation_cache".
            ### START CODE HERE ### (≈ 2 lines of code)
            Z, linear_cache = linear_forward(A_prev, W, b)
            A, activation_cache = sigmoid(Z)
            ### END CODE HERE ###
        
        elif activation == "relu":
            # Inputs: "A_prev, W, b". Outputs: "A, activation_cache".
            ### START CODE HERE ### (≈ 2 lines of code)
            Z, linear_cache = linear_forward(A_prev, W, b)
            A, activation_cache = relu(Z)
            ### END CODE HERE ###
        
        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 
            ### START CODE HERE ### (≈ 2 lines of code)
            A, cache = linear_activation_forward(A_prev, parameters['W' + str(l)], parameters['b' + str(l)], 'relu')
            caches.append(cache)
            ### END CODE HERE ###
        
        # Implement LINEAR -> SIGMOID. Add "cache" to the "caches" list.
        ### START CODE HERE ### (≈ 2 lines of code)
        AL, cache = linear_activation_forward(A, parameters['W' + str(L)], parameters['b' + str(L)], 'sigmoid')
        caches.append(cache)
        ### END CODE HERE ###
        
        assert(AL.shape == (1, X.shape[1]))
                
        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.
        ### START CODE HERE ### (≈ 1 lines of code)
        #cost = - 1.0 / m * np.sum(Y * np.log(AL) + (1 - Y) * np.log(1 - AL) )
        cost =   1.0 / m * (-np.dot(Y,np.log(AL).T) - np.dot(1-Y, np.log(1-AL).T))
        ### END CODE HERE ###
        
        cost = np.squeeze(cost)      # 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_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":
            ### START CODE HERE ### (≈ 2 lines of code)
            dZ = relu_backward(dA, activation_cache)
            dA_prev, dW, db = linear_backward(dZ, linear_cache)
            ### END CODE HERE ###
            
        elif activation == "sigmoid":
            ### START CODE HERE ### (≈ 2 lines of code)
            dZ = sigmoid_backward(dA, activation_cache)
            dA_prev, dW, db = linear_backward(dZ, linear_cache)
            ### END CODE HERE ###
        
        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" (it's caches[l], for l in range(L-1) i.e l = 0...L-2)
                    the cache of linear_activation_forward() with "sigmoid" (it's caches[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
        ### START CODE HERE ### (1 line of code)
        dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))
        ### END CODE HERE ###
        
        # Lth layer (SIGMOID -> LINEAR) gradients. Inputs: "AL, Y, caches". Outputs: "grads["dAL"], grads["dWL"], grads["dbL"]
        ### START CODE HERE ### (approx. 2 lines)
        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")
        ### END CODE HERE ###
        
        for l in reversed(range(L - 1)):
            # lth layer: (RELU -> LINEAR) gradients.
            # Inputs: "grads["dA" + str(l + 2)], caches". Outputs: "grads["dA" + str(l + 1)] , grads["dW" + str(l + 1)] , grads["db" + str(l + 1)] 
            ### START CODE HERE ### (approx. 5 lines)
            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
            ### END CODE HERE ###
     
        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.
        ### START CODE HERE ### (≈ 3 lines of code)
        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)]
        ### END CODE HERE ###
            
        return parameters
    
    # GRADED FUNCTION: L_layer_model
    
    def L_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, 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.
        """
    
        np.random.seed(1)
        costs = []                         # keep track of cost
        
        # Parameters initialization.
        ### START CODE HERE ###
        parameters = initialize_parameters_deep(layers_dims)
        ### END CODE HERE ###
        
        # Loop (gradient descent)
        for i in range(0, num_iterations):
    
            # Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID.
            ### START CODE HERE ### (≈ 1 line of code)
            AL, caches = L_model_forward(X, parameters)
            ### END CODE HERE ###
            
            # Compute cost.
            ### START CODE HERE ### (≈ 1 line of code)
            cost = compute_cost(AL, Y)
            ### END CODE HERE ###
        
            # Backward propagation.
            ### START CODE HERE ### (≈ 1 line of code)
            grads = L_model_backward(AL, Y, caches)
            ### END CODE HERE ###
     
            # Update parameters.
            ### START CODE HERE ### (≈ 1 line of code)
            parameters = update_parameters(parameters, grads, learning_rate)
            ### END CODE HERE ###
                    
            # Print the cost every 100 training example
            if print_cost and i % 100 == 0:
                print ("Cost after iteration %i: %f" %(i, cost))
            if print_cost and i % 100 == 0:
                costs.append(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
        
    if __name__=='__main__':  
    
        train_x_orig, train_y, test_x_orig, test_y, classes = load_data()
        
        # Example of a picture
        '''index = 7
        plt.imshow(train_x_orig[index])
        print ("y = " + str(train_y[0,index]) + ". It's a " + classes[train_y[0,index]].decode("utf-8") +  " picture.")
        #plt.show()'''
        
        # Explore your dataset 
        m_train = train_x_orig.shape[0]
        num_px = train_x_orig.shape[1]
        m_test = test_x_orig.shape[0]
    
        print ("Number of training examples: " + str(m_train))
        print ("Number of testing examples: " + str(m_test))
        print ("Each image is of size: (" + str(num_px) + ", " + str(num_px) + ", 3)")
        print ("train_x_orig shape: " + str(train_x_orig.shape))
        print ("train_y shape: " + str(train_y.shape))
        print ("test_x_orig shape: " + str(test_x_orig.shape))
        print ("test_y shape: " + str(test_y.shape))
        
        # Reshape the training and test examples 
        train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T   # The "-1" makes reshape flatten the remaining dimensions
        test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T
    
        # Standardize data to have feature values between 0 and 1.
        train_x = train_x_flatten/255.
        test_x = test_x_flatten/255.
    
        print ("train_x's shape: " + str(train_x.shape))
        print ("test_x's shape: " + str(test_x.shape))
    
        '''### CONSTANTS DEFINING THE MODEL ####
        n_x = 12288     # num_px * num_px * 3
        n_h = 7
        n_y = 1
        layers_dims = (n_x, n_h, n_y)
    
        parameters = two_layer_model(train_x, train_y, layers_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost=True)
    
        predictions_train = predict(train_x, train_y, parameters)
     
        predictions_test = predict(test_x, test_y, parameters)'''
        
        ### CONSTANTS ###
        layers_dims = [12288, 20, 7, 5, 1] #  5-layer model  
      
        parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations = 2500, print_cost = True)  
    
        pred_train = predict(train_x, train_y, parameters)
        pred_test = predict(test_x, test_y, parameters)
        
            print_mislabeled_images(classes, test_x, test_y, pred_test)
        
        ## START CODE HERE ##
        my_image = "my_image.jpg" # change this to the name of your image file 
        my_label_y = [1] # the true class of your image (1 -> cat, 0 -> non-cat)
        ## END CODE HERE ##
    
        fname = "images/" + my_image
        image = np.array(ndimage.imread(fname, flatten=False))
        my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((num_px*num_px*3,1))
        my_predicted_image = predict(my_image, my_label_y, parameters)
    
        plt.imshow(image)
        print ("y = " + str(np.squeeze(my_predicted_image)) + ", your L-layer model predicts a \"" + classes[int(np.squeeze(my_predicted_image)),].decode("utf-8") +  "\" picture.")

[70]: /images/20220512/917cf670d9e7496eaface5ed882cf2b7.png
[70 1]: /images/20220512/fd69503694324ee495580ba51360ccfc.png
[70 2]: /images/20220512/8a68f1caebfe4b7e9bae5fdb431410bf.png
[70 3]: /images/20220512/93f36c05ef8d433c91e3e8249c013e52.png
[70 4]: /images/20220512/0ccfaeb8dda146f5981c96a347b9b48a.png
[70 5]: /images/20220512/77131dffd8084012953aa705caa95bed.png
[70 6]: /images/20220512/51fb29a3800e403aa37f7441ebbba137.png