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 np
import matplotlib.pyplot as plt
from testCases import *
import sklearn
import sklearn.datasets
import sklearn.linear_model
from 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 inline

np.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 parameters

def 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 grads

parameters, 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 parameters

def 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('***************************')