深层神经网络编程作业2

DeepLearning/ 01神经网络和深度学习神经网络

创建时间:2020-02-16 17:11

字数:897 阅读:

导包
加载数据
改变数据形状
初始化每层神经元数
双层神经网络模型
L层神经网络
测试自己的照片

导包

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_v3 import *

%matplotlib inline
plt.rcParams['figure.figsize']=(5.0,4.0)
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap']='gray'

%load_ext autoreload
%autoreload 2

np.random.seed(1)

加载数据

train_x_orig, train_y, test_x_orig, test_y, classes = load_data()

m_train = train_x_orig.shape[0]
num_px = train_x_orig.shape[1]
m_test = test_x_orig.shape[0]

改变数据形状

train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T
test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T

train_x = train_x_flatten/255
test_x = test_x_flatten/255

)

初始化每层神经元数

n_x = num_px*num_px*3
n_h = 7
n_y = 1
layers_dims = (n_x, n_h, n_y)

双层神经网络模型

def two_layer_model(X, Y, layer_dims, learning_rate = 0.0075, num_iterations=3000, print_cost=False):
    np.random.seed(1)
    grads = {}
    costs = []
    m = X.shape[1]
    (n_x, n_h, n_y) = layer_dims
    
    parameters = initialize_parameters(n_x, n_h, n_y)
    
    W1 = parameters["W1"]
    W2 = parameters["W2"]
    b1 = parameters["b1"]
    b2 = parameters["b2"]
    
    for i in range(0, num_iterations):
        #向前传播
        # Forward propagation: LINEAR -> RELU -> LINEAR -> SIGMOID. Inputs: "X, W1, b1, W2, b2". Output: "A1, cache1, A2, cache2".
        A1, cache1 = linear_activation_forward(X, W1, b1, "relu")
        A2, cache2 = linear_activation_forward(A1, W2, b2, "sigmoid")
        
        #损失函数
        cost = compute_cost(A2, Y)
        
        #向后传播
        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["dW2"] = dW2
        grads["db1"] = db1
        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"]
        
        #打印损失
        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)
        
    plt.plot(np.squeeze(costs))
    plt.ylabel('cost')
    plt.xlabel('iterations (per tens)')
    plt.title("Learning rate =" + str(learning_rate))
    plt.show()
    
    return parameters

1	parameters = two_layer_model(train_x, train_y, layer_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost=True)

Cost after iteration 0: 0.6930497356599888
Cost after iteration 100: 0.6464320953428849
Cost after iteration 200: 0.6325140647912677
Cost after iteration 300: 0.6015024920354665
Cost after iteration 400: 0.5601966311605747
Cost after iteration 500: 0.5158304772764729
Cost after iteration 600: 0.47549013139433255
Cost after iteration 700: 0.43391631512257495
Cost after iteration 800: 0.4007977536203886
Cost after iteration 900: 0.3580705011323798
Cost after iteration 1000: 0.3394281538366412
Cost after iteration 1100: 0.3052753636196264
Cost after iteration 1200: 0.2749137728213015
Cost after iteration 1300: 0.24681768210614846
Cost after iteration 1400: 0.19850735037466108
Cost after iteration 1500: 0.17448318112556654
Cost after iteration 1600: 0.17080762978096023
Cost after iteration 1700: 0.11306524562164728
Cost after iteration 1800: 0.09629426845937154
Cost after iteration 1900: 0.08342617959726861
Cost after iteration 2000: 0.07439078704319084
Cost after iteration 2100: 0.06630748132267932
Cost after iteration 2200: 0.05919329501038171
Cost after iteration 2300: 0.053361403485605564
Cost after iteration 2400: 0.04855478562877018

1 2	predictions_train = predict(train_x, train_y, parameters) predictions_test = predict(test_x, test_y, parameters)

Accuracy: 0.9999999999999998
Accuracy: 0.72

L层神经网络

1	layers_dims = [12288, 20, 7, 5, 1]

def L_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):
    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)
        
        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)
            
    plt.plot(np.squeeze(costs))
    plt.ylabel("cost")
    plt.xlabel("iterations(per tens)")
    plt.title("Learning rate ="+str(learning_rate))
    plt.show()
        
    return parameters

1	parameters = L_layer_model(train_x, train_y, layers_dims, learning_rate=0.0075, num_iterations=3000, print_cost=True)

Cost after iteration 0: 0.771749
Cost after iteration 100: 0.672053
Cost after iteration 200: 0.648263
Cost after iteration 300: 0.611507
Cost after iteration 400: 0.567047
Cost after iteration 500: 0.540138
Cost after iteration 600: 0.527930
Cost after iteration 700: 0.465477
Cost after iteration 800: 0.369126
Cost after iteration 900: 0.391747
Cost after iteration 1000: 0.315187
Cost after iteration 1100: 0.272700
Cost after iteration 1200: 0.237419
Cost after iteration 1300: 0.199601
Cost after iteration 1400: 0.189263
Cost after iteration 1500: 0.161189
Cost after iteration 1600: 0.148214
Cost after iteration 1700: 0.137775
Cost after iteration 1800: 0.129740
Cost after iteration 1900: 0.121225
Cost after iteration 2000: 0.113821
Cost after iteration 2100: 0.107839
Cost after iteration 2200: 0.102855
Cost after iteration 2300: 0.100897
Cost after iteration 2400: 0.092878
Cost after iteration 2500: 0.088413
Cost after iteration 2600: 0.085951
Cost after iteration 2700: 0.081681
Cost after iteration 2800: 0.078247
Cost after iteration 2900: 0.075444

1 2	predict_train = predict(train_x, train_y, parameters) predict_test = predict(test_x, test_y, parameters)

Accuracy: 0.9904306220095691
Accuracy: 0.8200000000000001

1	print_mislabeled_images(classes, test_x, test_y, predict_test)

测试自己的照片

my_image = "cat1.jpg"
my_label_y = [1]

fname = "C:\\Users\\董润泽\\Desktop\\cat_picture\\" + 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_image = my_image/255
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.")

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