吴恩达第一课第四周编程作业

链接：https://pan.baidu.com/s/1uRw1aQQww-ZD1UNbN1Y9fA 
提取码：cwlh 
 

源代码：

import numpy as np
import h5py
import matplotlib.pyplot as plt
import testCases
from dnn_utils import sigmoid, sigmoid_backward, relu, relu_backward  # 参见资料包
import lr_utils
np.random.seed(1)
# 初始化两层参数
def initialize_parameters(n_x, n_h, n_y):
    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))

    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,
        'W2': W2,
        'b1': b1,
        'b2': b2
    }
    return parameters
# 初始化多层网络参数
def initialize_parameters_deep(layers_dim):
    np.random.seed(3)
    L = len(layers_dim)
    parameters = {}

    for l in range(1, L):
        parameters['W' + str(l)] = np.random.randn(layers_dim[l], layers_dim[l - 1]) / np.sqrt(layers_dim[l - 1])
        parameters['b' + str(l)] = np.zeros((layers_dim[l], 1))

        assert (parameters['W' + str(l)].shape == (layers_dim[l], layers_dim[l - 1]))
        assert (parameters['b' + str(l)].shape == (layers_dim[l], 1))
    return parameters
# 前向传播线性部分
def linear_forward(A, W, b):
    Z = np.dot(W, A) + b

    assert (Z.shape == (W.shape[0], A.shape[1]))
    cache = (A, W, b)
    return (Z, cache)
# 前向激活部分
def linear_activation_forward(A_prev, W, b, activation):
    """
    :param A_prev: 上一层的激活值
    """
    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
# 多层网络的前向
def L_model_forward(X, parameters):
    caches = []
    L = len(parameters) // 2
    A = X

    for l in range(1, L):
        A_prev = A
        A, cache = linear_activation_forward(A_prev, parameters['W' + str(l)], parameters['b' + str(l)], 'relu')
        caches.append(cache)

    AL, cache = linear_activation_forward(A, parameters['W' + str(L)], parameters['b' + str(L)], 'sigmoid')
    caches.append(cache)

    assert (AL.shape == (1, X.shape[1]))
    return AL, caches
# 计算成本
def compute_cost(AL, Y):
    m = Y.shape[1]

    cost = -np.sum(np.multiply(Y, np.log(AL)) + np.multiply(1 - Y, np.log(1 - AL))) / m

    cost = np.squeeze(cost)
    assert (cost.shape == ())
    return cost
# 后向传播，第L层的线性部分
def linear_backward(dZ, cache):
    A_prev, W, b = cache
    m = A_prev.shape[1]

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

    assert (dW.shape == (W.shape))
    assert (db.shape == (b.shape))
    assert (dA_prev.shape == (A_prev.shape))
    return dA_prev, dW, db
# 后向激活
def linear_activation_backward(dA, cache, activation):
    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
# 多层网络后向传播
def L_model_backward(AL, Y, caches):
    grads={}
    L=len(caches)
    Y=Y.reshape(AL.shape)
    dAL = - (np.divide(Y, AL) - np.divide(1 - Y, 1 - AL))

    current_cache=caches[L-1]
    grads['dA'+str(L)],grads['dW'+str(L)],grads['db'+str(L)]=linear_activation_backward(dAL, current_cache, 'sigmoid')

    for l in reversed(range(L-1)):
        current_cache=caches[l]
        dA_prev_temp,dW_temp,db_temp=linear_activation_backward(grads['dA'+str(l+2)],current_cache,'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
#更新参数
def update_parameters(parameters, grads,learning_rate):
    L=len(parameters)//2
    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
#搭建两层神经网络
def two_layer_model(X,Y,layers_dims,learning_rate=0.0075,num_iterations=3000,print_cost=False,isPlot=True):
    """
    :param isPlot: 是否绘制误差图像
    """
    np.random.seed(1)
    grads={}
    costs=[]
    (n_x,n_h,n_y)=layers_dims
    #初始化参数
    parameters=initialize_parameters(n_x, n_h, n_y)
    W1=parameters['W1']
    b1=parameters['b1']
    W2=parameters['W2']
    b2=parameters['b2']

    for i in range(0,num_iterations):
        A1,cache1=linear_activation_forward(X, W1, b1, 'relu')
        A2,cache2=linear_activation_forward(A1, W2, b2, 'sigmoid')#前向传播
        #计算成本
        cost=compute_cost(A2, Y)
        #后向传播
        dA2 = - (np.divide(Y, A2) - np.divide(1 - Y, 1 - A2))
        dA1, dW2, db2 = linear_activation_backward(dA2, cache2, "sigmoid")
        dA0, dW1, db1 = linear_activation_backward(dA1, cache1, "relu")
        grads["dW1"] = dW1
        grads["db1"] = db1
        grads["dW2"] = dW2
        grads["db2"] = db2
        #更新参数
        parameters = update_parameters(parameters, grads, learning_rate)
        W1=parameters['W1']
        b1=parameters['b1']
        W2=parameters['W2']
        b2=parameters['b2']
        #打印
        if i%100==0:
            costs.append(cost)
            if print_cost:
                print('第',i,'次迭代，成本为：',np.squeeze(cost))
    if isPlot:
            plt.plot(np.squeeze(costs))
            plt.ylabel('cost')
            plt.xlabel('iterations (per tens)')
            plt.title("Learning rate =" + str(learning_rate))
            plt.show()
    return parameters
#预测
def predict(X, y, parameters):
    m = X.shape[1]
    p = np.zeros((1,m))

    probas, caches = L_model_forward(X, parameters)

    for i in range(0, probas.shape[1]):
        if probas[0,i] > 0.5:
            p[0,i] = 1
        else:
            p[0,i] = 0

    print("准确度为: "  + str(float(np.sum((p == y))/m)))
    return p
# train_set_x_orig , train_set_y , test_set_x_orig , test_set_y , classes = lr_utils.load_dataset()
# train_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
# test_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T
# train_x = train_x_flatten / 255
# train_y = train_set_y
# test_x = test_x_flatten / 255
# test_y = test_set_y
# n_x = 12288
# n_h = 7
# n_y = 1
# layers_dims = (n_x,n_h,n_y)
# parameters = two_layer_model(train_x, train_set_y, layers_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost=True,isPlot=True)
# predictions_train = predict(train_x, train_y, parameters) #训练集
# predictions_test = predict(test_x, test_y, parameters) #测试集
def L_layer_model(X, Y, layers_dims, learning_rate=0.0075, num_iterations=3000, print_cost=False,isPlot=True):
    np.random.seed(1)
    costs = []

    parameters = initialize_parameters_deep(layers_dims)

    for i in range(0,num_iterations):
        AL , caches = L_model_forward(X,parameters)

        cost = compute_cost(AL,Y)

        grads = L_model_backward(AL,Y,caches)

        parameters = update_parameters(parameters,grads,learning_rate)

        #打印成本值，如果print_cost=False则忽略
        if i % 100 == 0:
            #记录成本
            costs.append(cost)
            #是否打印成本值
            if print_cost:
                print("第", i ,"次迭代，成本值为：" ,np.squeeze(cost))
    #迭代完成，根据条件绘制图
    if isPlot:
        plt.plot(np.squeeze(costs))
        plt.ylabel('cost')
        plt.xlabel('iterations (per tens)')
        plt.title("Learning rate =" + str(learning_rate))
        plt.show()
    return parameters
train_set_x_orig , train_set_y , test_set_x_orig , test_set_y , classes = lr_utils.load_dataset()

train_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
test_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T

train_x = train_x_flatten / 255
train_y = train_set_y
test_x = test_x_flatten / 255
test_y = test_set_y
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,isPlot=True)
pred_train = predict(train_x, train_y, parameters) #训练集
pred_test = predict(test_x, test_y, parameters) #测试集

参考：https://blog.csdn.net/u013733326/article/details/79827273