吴恩达DeepLearning.ai系列课后编程题实践总结week3

# -*- coding: utf-8 -*-"""Created on Sun Sep 24 09:09:10 2017@author: Jay"""import numpy as npimport matplotlib.pyplot as pltfrom testCases import *import sklearnimport sklearn.datasetsimport sklearn.linear_modelfrom planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets#将那些用matplotlib绘制的图显示在页面里而不是弹出一个窗口:%matplotlib inlinenp.random.seed(1)X, Y = load_planar_dataset()#plt.scatter(X[0, :], X[1, :], c=Y, s=20, cmap=plt.cm.Spectral)'''clf=sklearn.linear_model.LogisticRegressionCV();clf.fit(X.T,Y.T);plot_decision_boundary(lambda x:clf.predict(x),X,Y)plt.title('Logistic Regression')LR_predictions=clf.predict(X.T)print('Accuracy of logistic regression: %d' %float((np.dot(Y,LR_predictions)+\                                                    np.dot(1-Y,1-LR_predictions))/float(Y.size)*100)+'%')'''def layer_sizes(X,Y):    n_x=X.shape[0]    n_h=4    n_y=Y.shape[0]    return(n_x,n_h,n_y)def initialize_parameters(n_x, n_h, n_y):    np.random.seed(2)    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,               'b1':b1,               'W2':W2,               'b2':b2}    return parametersdef forward_propagation(X,parameters):    W1 = parameters['W1']    b1 = parameters['b1']    W2 = parameters['W2']    b2 = parameters['b2']    Z1=np.dot(W1,X)+b1    A1=np.tanh(Z1)    Z2=np.dot(W2,A1)+b2    A2=sigmoid(Z2)    assert(A2.shape==(1,X.shape[1]))    cache={'Z1':Z1,           'A1':A1,           'Z2':Z2,           'A2':A2}    return A2,cache'''X_assess,parameters=forward_propagation_test_case()A2,cache=forward_propagation(X_assess,parameters)print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))'''def compute_cost(A2,Y,parameters):    m=Y.shape[1]    W1=parameters['W1']    W2=parameters['W2']    cost =-(float(np.dot(np.log(A2),Y.T))+np.dot(np.log(1.-A2),(1.-Y).T))/m    #logprobs = np.multiply(np.log(A2),Y)    #cost = - np.sum(np.multiply(np.log(A2), Y) + np.multiply(np.log(1. - A2), 1. - Y)) / m    cost = np.squeeze(cost)    #assert(isinstance(cost,float))    return cost#A2,Y_assess,parameters=compute_cost_test_case()#print('cost=' + str(compute_cost(A2,Y_assess,parameters)))def backward_propagation(parameters,cache,X,Y):    m=X.shape[1]    W1=parameters['W1']    W2=parameters['W2']    A1=cache['A1']    A2=cache['A2']    dZ2=A2-Y    dW2=np.dot(dZ2,A1.T)/m    db2=np.sum(dZ2,axis=1,keepdims=True)/m    dZ1=np.dot(W2.T,dZ2)*(1-A1**2)    dW1=np.dot(dZ1,X.T)/m    db1=np.sum(dZ1,axis=1,keepdims=True)/m    grads={'dW1':dW1,           'db1':db1,           'dW2':dW2,           'db2':db2}    return gradsparameters, cache, X_assess, Y_assess = backward_propagation_test_case()'''grads = backward_propagation(parameters, cache, X_assess, Y_assess)print ("dW1 = "+ str(grads["dW1"]))print ("db1 = "+ str(grads["db1"]))print ("dW2 = "+ str(grads["dW2"]))print ("db2 = "+ str(grads["db2"]))'''def update_parameters(parameters, grads, learning_rate = 1.2):    W1 = parameters['W1']    b1 = parameters['b1']    W2 = parameters['W2']    b2 = parameters['b2']    dW1 = grads['dW1']    db1 = grads['db1']    dW2 = grads['dW2']    db2 = grads['db2']    W1 = W1 - learning_rate * dW1    b1 = b1 - learning_rate * db1    W2 = W2 - learning_rate * dW2    b2 = b2 - learning_rate * db2    parameters = {"W1": W1,                  "b1": b1,                  "W2": W2,                  "b2": b2}    return parametersdef nn_model(X,Y,n_h,num_iterations=10000,print_cost=False):    np.random.seed(3)    n_x=layer_sizes(X,Y)[0]    n_y=layer_sizes(X,Y)[2]    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):        A2, cache = forward_propagation(X, parameters)        cost = compute_cost(A2, Y, parameters)        grads = backward_propagation(parameters, cache, X, Y)        parameters = update_parameters(parameters, grads)        if i % 1000 == 0:            print ("Cost after iteration %i: %f" %(i, cost))    return parameters'''X_assess, Y_assess = nn_model_test_case()parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=False)print("W1 = " + str(parameters["W1"]))print("b1 = " + str(parameters["b1"]))print("W2 = " + str(parameters["W2"]))print("b2 = " + str(parameters["b2"]))'''def predict(parameters,X):    A2,cache=forward_propagation(X,parameters)    predictions=np.array([0 if i<=0.5 else 1 for i in np.squeeze(A2)])    return predictions'''parameters, X_assess = predict_test_case()predictions = predict(parameters, X_assess)print("predictions mean = " + str(np.mean(predictions)))''''''parameters = nn_model(X, Y, n_h = 4, num_iterations = 20000, print_cost=True)plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)plt.title("Decision Boundary for hidden layer size " + str(4))predictions = predict(parameters, X)print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')plt.figure(figsize=(16, 32))hidden_layer_sizes = [1, 2, 3, 4, 5, 20, 50]for i, n_h in enumerate(hidden_layer_sizes):        #枚举    plt.subplot(5, 2, i+1)    plt.title('Hidden Layer of size %d' % n_h)    parameters = nn_model(X, Y, n_h, num_iterations = 5000)    plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)    predictions = predict(parameters, X)    accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)    print ("Accuracy for {} hidden units: {} %".format(n_h, accuracy))'''noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()datasets = {"noisy_circles": noisy_circles,            "noisy_moons": noisy_moons,            "blobs": blobs,            "gaussian_quantiles": gaussian_quantiles}for i,j in datasets.items():#遍历字典，对每一个类型算一遍    dataset = "j"    X, Y = j    X,Y=X.T,Y.reshape(1,Y.shape[0])    if dataset=='blobs':        Y=Y%2#plt.scatter(X[0,:],X[1,:],c=Y,s=40,cmap=plt.cm.Spectral);    parameters=nn_model(X,Y,5,num_iterations=10000)    predictions=predict(parameters,X)    accuracy=float((np.dot(Y,predictions.T)+np.dot(1.-Y,1.-predictions.T))/Y.size*100)    print('accuracy for {} is:{}%'.format(i,accuracy))    print('***************************')