吴恩达深度学习笔记 第二章作业2

源码以及注释:

import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy

from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
train_set_x_orig , train_set_y , test_set_x_orig , test_set_y , classes = load_dataset()
# Example of a picture
"""
index = 25
plt.imshow(train_set_x_orig[index])
print ("y = " + str(train_set_y[:, index]) + ", it‘s a ‘" + classes[np.squeeze(train_set_y[:, index])].decode("utf-8") +  "‘ picture.")
"""

#Calculating the - m_train (number of training examples)  - m_test (number of test examples) - num_px (= height = width of a training image) 
m_train=train_set_x_orig.shape[0]
m_test=test_set_x_orig.shape[0]
num_px=train_set_x_orig.shape[1]
print ("Number of training examples: m_train = " + str(m_train))
print ("Number of testing examples: m_test = " + str(m_test))
print ("Height/Width of each image: num_px = " + str(num_px))
print ("Each image is of size: (" + str(num_px) + ", " + str(num_px) + ", 3)")
print ("train_set_x shape: " + str(train_set_x_orig.shape))
print ("train_set_y shape: " + str(train_set_y.shape))
print ("test_set_x shape: " + str(test_set_x_orig.shape))
print ("test_set_y shape: " + str(test_set_y.shape))

# Reshape the training and test data sets so that images of size (num_px, num_px, 3) are flattened into single vectors of shape (num_px ?num_px ?3, 1).
train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0],-1).T
test_set_x_flatten=test_set_x_orig.reshape(test_set_x_orig.shape[0],-1).T
print ("train_set_x_flatten shape: " + str(train_set_x_flatten.shape))
print ("train_set_y shape: " + str(train_set_y.shape))
print ("test_set_x_flatten shape: " + str(test_set_x_flatten.shape))
print ("test_set_y shape: " + str(test_set_y.shape))
print ("sanity check after reshaping: " + str(train_set_x_flatten[0:5,0]))

#standardize our data_set
train_set_x = train_set_x_flatten/255
test_set_x = test_set_x_flatten/255

#define sigmoid function
def sigmoid(z):
  s=1/(1+np.exp(-z))
  return s

#Initial function(Initial w and b)
def initialize_with_zeros(dim):
    w=np.zeros((dim,1)) #dim is the size we want
    b=0
    assert(w.shape == (dim, 1))
    assert(isinstance(b, float) or isinstance(b, int))
    return w, b
#Implement a function propagate() that computes the cost function and its gradient
def propagate(w, b, X, Y):
    # FORWARD PROPAGATION (FROM X TO COST)
    m = X.shape[1]
    A=sigmoid(np.dot(w.T,X)+b)
    cost=-1/m*np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))
    
    # BACKWARD PROPAGATION (TO FIND GRAD)
    dw=1/m*np.dot(X,(A-Y).T)
    db=1/m*np.sum(A-Y)
    assert(dw.shape == w.shape)
    assert(db.dtype == float)
    cost = np.squeeze(cost)
    assert(cost.shape == ())    
    grads = {"dw": dw,
             "db": db}    
    return grads, cost
def optimize(w,b,X,Y,num_iterations,learning_rate,print_cost=False):
    costs = []   
    for i in range(num_iterations):
        grads,cost=propagate(w, b, X, Y)
        dw=grads["dw"]
        db=grads["db"]
        w=w-learning_rate*dw
        b=b-learning_rate*db
        if i % 100 == 0:
            costs.append(cost)
        # Print the cost every 100 training examples
        if print_cost and i % 100 == 0:
            print ("Cost after iteration %i: %f" %(i, cost))   
    params = {"w": w,
              "b": b}   
    grads = {"dw": dw,
             "db": db}
    return params, grads, costs
def predict(w, b, X):
    m = X.shape[1]
    Y_prediction = np.zeros((1,m))
    w = w.reshape(X.shape[0], 1)
    A=sigmoid(np.dot(w.T,X)+b)
    for i in range(A.shape[1]):
        if(A[0,i]<=0.5):
            Y_prediction[0,i]=0
        else:
            Y_prediction[0,i]=1
    assert(Y_prediction.shape == (1, m))
    return Y_prediction
def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):
    # initialize parameters with zeros
    w,b=initialize_with_zeros(X_train.shape[0])
    params,grads,costs=optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
    w=params["w"]
    b=params["b"]
    Y_prediction_test=predict(w, b, X_test)
    Y_prediction_train=predict(w, b, X_train)
    print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
    print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))
    
    d = {"costs": costs,
         "Y_prediction_test": Y_prediction_test, 
         "Y_prediction_train" : Y_prediction_train, 
         "w" : w, 
         "b" : b,
         "learning_rate" : learning_rate,
         "num_iterations": num_iterations}
    
    return d
d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)
#compare the learning curve of our model with several choices of learning rates.
learning_rates = [0.01, 0.001, 0.0001]
models = {}
for i in learning_rates:
    print ("learning rate is: " + str(i))
    models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)
    print (‘\n‘ + "-------------------------------------------------------" + ‘\n‘)

for i in learning_rates:
    plt.plot(np.squeeze(models[str(i)]["costs"]), label= str(models[str(i)]["learning_rate"]))

plt.ylabel(‘cost‘)
plt.xlabel(‘iterations‘)

legend = plt.legend(loc=‘upper center‘, shadow=True)
frame = legend.get_frame()
frame.set_facecolor(‘0.90‘)
#plt.show()
#Test with your own image
my_image = "cat_in_iran.jpg"   # change this to the name of your image file 
fname =  my_image
image = np.array(ndimage.imread(fname, flatten=False))
my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1, num_px*num_px*3)).T
my_predicted_image = predict(d["w"], d["b"], my_image)
plt.imshow(image)
print("y = " + str(np.squeeze(my_predicted_image)) + ", your algorithm predicts a \"" + classes[int(np.squeeze(my_predicted_image)),].decode("utf-8") +  "\" picture.")