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 pythondeep 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)))
