import sys,os
import numpy as np
import matplotlib.pylab as plt
import pickle#pickle提供了一个简单的持久化功能。可以将对象以文件的形式存放在磁盘上
sys.path.append('E:\deep learning ?by python\deep learn by python') # 为了导入父目录中的文件而进行的设定
from dataset.mnist import load_mnist#导入数据集
from PIL import Image
def step_function(x):
? ? return np.array(x > 0, dtype=np.int)
X = np.arange(-5.0, 5.0, 0.1)
Y = step_function(X)
plt.plot(X, Y)
plt.ylim(-0.1, 1.1) ?# 指定图中绘制的y轴的范围
plt.show()
#sigmoid函数
def sigmoid(x):
? ? return 1 / (1 + np.exp(-x)) ? ?
X = np.arange(-5.0, 5.0, 0.1)
Y = sigmoid(X)
plt.plot(X, Y)
plt.ylim(-0.1, 1.1)
plt.show()
#relu函数
def relu(x):
? ? return np.maximum(0, x)
x = np.arange(-5.0, 5.0, 0.1)
y = relu(x)
plt.plot(x, y)
plt.ylim(-1.0, 5.5)
plt.show()
def softmax(x):
? ? C = np.max( x )
? ? exp_a = np.exp( x - C )
? ? sum_exp = np.sum( exp_a )
? ? y = exp_a / sum_exp
? ? return y
#进行训练
def get_data():
? ? (x_train, t_train), (x_test, t_test) = load_mnist(normalize=True, flatten=True, one_hot_label=False)
? ? return x_test, t_test
def init_network():
? ? with open("sample_weight.pkl", 'rb') as f:
? ? ? ? network = pickle.load(f)
? ? return network
def predict(network, x):
? ? W1, W2, W3 = network['W1'], network['W2'], network['W3']
? ? b1, b2, b3 = network['b1'], network['b2'], network['b3']
? ? a1 = np.dot(x, W1) + b1
? ? z1 = sigmoid(a1)
? ? a2 = np.dot(z1, W2) + b2
? ? z2 = sigmoid(a2)
? ? a3 = np.dot(z2, W3) + b3
? ? y = softmax(a3)
? ? return y
x, t = get_data()
network = init_network()
accuracy_cnt = 0
for i in range(0, len(x),100):#用for循环逐一取出保存在x中的图像数据
? ? y = predict(network, x[i])#predict函数进行分类，以numpy库数组的形式输出各个标签的对应的概率
? ? p= np.argmax(y)#获取概率最高的元素的索引
? ? if p == t[i]:
? ? ? ? accuracy_cnt += 1
print("Accuracy(sigmoid):" + str(100*float(accuracy_cnt) / len(x)))
###relu
def predict2(network, x):
? ? W1, W2, W3 = network['W1'], network['W2'], network['W3']
? ? b1, b2, b3 = network['b1'], network['b2'], network['b3']
? ? a1 = np.dot(x, W1) + b1
? ? z1 = relu(a1)
? ? a2 = np.dot(z1, W2) + b2
? ? z2 = relu(a2)
? ? a3 = np.dot(z2, W3) + b3
? ? y = softmax(a3)
? ? return y
x, t = get_data()
network = init_network()
accuracy_cnt = 0
for i in range(0, len(x),100):
? ? y = predict2(network, x[i])
? ? p= np.argmax(y)
? ? if p == t[i]:
? ? ? ? accuracy_cnt += 1
print("Accuracy(relu):" + str(100*float(accuracy_cnt) / len(x)))
###step_function
def predict3(network, x):
? ? W1, W2, W3 = network['W1'], network['W2'], network['W3']
? ? b1, b2, b3 = network['b1'], network['b2'], network['b3']
? ? a1 = np.dot(x, W1) + b1
? ? z1 = step_function(a1)
? ? a2 = np.dot(z1, W2) + b2
? ? z2 = step_function(a2)
? ? a3 = np.dot(z2, W3) + b3
? ? y = softmax(a3)
? ? return y
? ??
x, t = get_data()
network = init_network()
accuracy_cnt = 0
for i in range(0, len(x),100):
? ? y = predict3(network, x[i])
? ? p= np.argmax(y)
? ? if p == t[i]:
? ? ? ? accuracy_cnt += 1
print("Accuracy(step_function):" + str(100*float(accuracy_cnt) / len(x)))
?
?
?