前言
有很多类似的,这里就直接贴代码了
我基本是对着<<Python神经网络编程>>这本书敲的
数据集为Fashion-mnist
文件解析参考(https://www.jianshu.com/p/84f72791806f)
import numpy as np
import struct
import scipy.special
import matplotlib.pyplot as plt
def decode_idx3_ubyte(idx3_ubyte_file):
"""
解析idx3文件的通用函数
:param idx3_ubyte_file: idx3文件路径
:return: 数据集
"""
# 读取二进制数据
bin_data = open(idx3_ubyte_file, 'rb').read()
# 解析文件头信息,依次为魔数、图片数量、每张图片高、每张图片宽
offset = 0
fmt_header = '>iiii'
magic_number, num_images, num_rows, num_cols = struct.unpack_from(fmt_header, bin_data, offset)
# print('魔数:%d, 图片数量: %d张, 图片大小: %d*%d' % (magic_number, num_images, num_rows, num_cols))
# 解析数据集
image_size = num_rows * num_cols
offset += struct.calcsize(fmt_header)
fmt_image = '>' + str(image_size) + 'B'
images = np.empty((num_images, num_rows, num_cols))
for i in range(num_images):
# if (i + 1) % 10000 == 0:
# print('已解析 %d' % (i + 1) + '张')
images[i] = np.array(struct.unpack_from(fmt_image, bin_data, offset)).reshape((num_rows, num_cols))
offset += struct.calcsize(fmt_image)
return images
def decode_idx1_ubyte(idx1_ubyte_file):
"""
解析idx1文件的通用函数
:param idx1_ubyte_file: idx1文件路径
:return: 数据集
"""
# 读取二进制数据
bin_data = open(idx1_ubyte_file, 'rb').read()
# 解析文件头信息,依次为魔数和标签数
offset = 0
fmt_header = '>ii'
magic_number, num_images = struct.unpack_from(fmt_header, bin_data, offset)
# print('魔数:%d, 图片数量: %d张' % (magic_number, num_images))
# 解析数据集
offset += struct.calcsize(fmt_header)
fmt_image = '>B'
labels = np.empty(num_images)
for i in range(num_images):
# if (i + 1) % 10000 == 0:
# print('已解析 %d' % (i + 1) + '张')
labels[i] = struct.unpack_from(fmt_image, bin_data, offset)[0]
offset += struct.calcsize(fmt_image)
return labels
class neuralNetwork:
def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
self.inodes = inputnodes
self.hnodes = hiddennodes
self.onodes = outputnodes
self.wih = np.random.normal(0.0, pow(self.hnodes, -0.5), (self.hnodes, self.inodes))
self.who = np.random.normal(0.0, pow(self.onodes, -0.5), (self.onodes, self.hnodes))
self.lr = learningrate
self.activation_function = lambda x: scipy.special.expit(x)
pass
def train(self, inputs_list, targets_list):
inputs = np.array(inputs_list, ndmin=2).T
targets = np.array(targets_list, ndmin=2).T
hidden_inputs = np.dot(self.wih, inputs)
hidden_outputs = self.activation_function(hidden_inputs)
final_inputs = np.dot(self.who, hidden_outputs)
final_outputs = self.activation_function(final_inputs)
output_errors = targets - final_outputs
hidden_errors = np.dot(self.who.T, output_errors)
self.who += self.lr * np.dot((output_errors * final_outputs * (1.0 - final_outputs)),
np.transpose(hidden_outputs))
self.wih += self.lr * np.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),
np.transpose(inputs))
pass
def query(self, inputs_list):
inputs = np.array(inputs_list, ndmin=2).T
hidden_inputs = np.dot(self.wih, inputs)
hidden_outputs = self.activation_function(hidden_inputs)
final_inputs = np.dot(self.who, hidden_outputs)
final_outputs = self.activation_function(final_inputs)
return final_outputs
input_nodes = 784
hidden_nodes = 100
output_nodes = 10
learningrate = 0.1
n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learningrate)
train_data = decode_idx3_ubyte('train-images-idx3-ubyte')
train_label = decode_idx1_ubyte('train-labels-idx1-ubyte')
test_data = decode_idx3_ubyte('t10k-images-idx3-ubyte')
test_label = decode_idx1_ubyte('t10k-labels-idx1-ubyte')
def run():
epochs = 5
for e in range(epochs):
i = 0
for records in train_data:
all_values = records.reshape(784)
inputs = (np.array(all_values) / 255.0 * 0.99) + 0.01
targets = np.zeros(output_nodes) + 0.01
targets[int(train_label[i])] = 0.99
n.train(inputs, targets)
i = i + 1
pass
pass
scorecard = []
g = 0
for records in test_data:
all_values = records.reshape(784)
correct_label = int(test_label[g])
inputs = (np.array(all_values) / 255.0 * 0.99) + 0.01
outputs = n.query(inputs)
label = np.argmax(outputs)
g = g + 1
if (label == correct_label):
scorecard.append(1)
else:
scorecard.append(0)
pass
pass
scorecard_array = np.asarray(scorecard)
print("performance= ", scorecard_array.sum() / scorecard_array.size)
def show_train_photo(X,i):
im = X[i].reshape(28, 28)
fig = plt.figure()
fig.add_subplot(111)
plt.imshow(im, cmap='gray')
plt.show()
def plot_100_image(X):
size = 28
sample_idx = np.random.choice(np.array(X.shape[0]), 100)
sample_images = X[sample_idx, :]
fig, ax_array = plt.subplots(nrows=10, ncols=10, sharex=True, sharey=True, figsize=(10, 10))
for r in range(10):
for c in range(10):
ax_array[r, c].matshow(sample_images[10 * r + c].reshape((size, size)), cmap='gray_r')
plt.yticks([])
plt.xticks([])
plt.show()
if __name__ == '__main__':
# 随机参数
run()
#参数1.数据集 2指定照片序数
# show_train_photo(1)
#随机生成100张 参数:数据集
# plot_100_image(test_data)
一百张图随机生成结果