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【神经网络八股扩展】:自制数据集



文章目录

  • ​​前言​​
  • ​​1、文件一览​​
  • ​​2、将load_data()函数替换掉​​
  • ​​2、调用generateds函数​​
  • ​​4、效果​​
  • ​​总结​​

前言

本讲目标:自制数据集,解决本领域应用
将我们手中的图片和标签信息制作为可以直接导入的npy文件。

1、文件一览

首先看看我们的文件长什么样:
路径:D:\python code\AI\class4\MNIST_FC\mnist_image_label\mnist_test_jpg_10000
图片文件:(黑底白字的灰度图,大小:28x28,每个像素点都是0~255之间的整数)
【神经网络八股扩展】:自制数据集_深度学习
标签文件:(图片名和对应的标签,中间用空格隔开)
【神经网络八股扩展】:自制数据集_python_02

2、将load_data()函数替换掉

之前我们导入数据集的方式是(以mnist数据集为例):

fashion = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()

导入后变量的数据类型和形状:

x_train.shape

(60000,28,28) ,3维数组,60000个28行28列的图片灰度值

y_train.shape

(60000,) ,60000张图片对应的标签,是1维数组

x_test.shape

(10000,28,28) ,3维数组,10000个28行28列的图片灰度值

y_test.shape

(10000,) ,10000张图片对应的标签,是1维数组

我们需要自己写个函数generateds(图片路径,标签文件):
观察数据集:
【神经网络八股扩展】:自制数据集_tensorflow_03
我们需要做的:把图片灰度值数据拼接到图片列表,把标签数据拼接到标签列表。

函数代码如下:

def generateds(path, txt):
f = open(txt, 'r') #只读形式读取文本数据
contents = f.readlines() # 按行读取,读取所有行
f.close() #关闭文件
x, y_ = [], [] #建立空列表
for content in contents: #逐行读出
value = content.split() # 以空格分开,存入数组 图片名为value0 标签为value1
img_path = path + value[0] #图片路径+图片名->拼接出索引路径
img = Image.open(img_path) #读入图片
img = np.array(img.convert('L'))
img = img / 255. #归一化数据
x.append(img) #将归一化的数据贴到列表x
y_.append(value[1]) #标签贴到列表y_
print('loading : ' + content) #打印状态提示

x = np.array(x)
y_ = np.array(y_)
y_ = y_.astype(np.int64)
return x, y_

2、调用generateds函数

使用函数代码:

'''添加了:
训练集图片路径
训练集标签文件
训练集输入特征存储文件
训练集标签存储文件
测试集图片路径
测试集标签文件
测试集输入特征存储文件
测试集标签存储文件'''
train_path = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_train_jpg_60000/'
train_txt = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_train_jpg_60000.txt'
x_train_savepath = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_x_train.npy'
y_train_savepath = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fahion_y_train.npy'

test_path = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_test_jpg_10000/'
test_txt = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_test_jpg_10000.txt'
x_test_savepath = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_x_test.npy'
y_test_savepath = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_y_test.npy'
#观察测试集训练集文件是否存在,如果存在直接读取,如果不存在调用generate datasets函数
if os.path.exists(x_train_savepath) and os.path.exists(y_train_savepath) and os.path.exists(
x_test_savepath) and os.path.exists(y_test_savepath):
print('-------------Load Datasets-----------------')
x_train_save = np.load(x_train_savepath)
y_train = np.load(y_train_savepath)
x_test_save = np.load(x_test_savepath)
y_test = np.load(y_test_savepath)
x_train = np.reshape(x_train_save, (len(x_train_save), 28, 28))
x_test = np.reshape(x_test_save, (len(x_test_save), 28, 28))
else:
print('-------------Generate Datasets-----------------')
x_train, y_train = generateds(train_path, train_txt)
x_test, y_test = generateds(test_path, test_txt)

print('-------------Save Datasets-----------------')
x_train_save = np.reshape(x_train, (len(x_train), -1))
x_test_save = np.reshape(x_test, (len(x_test), -1))
np.save(x_train_savepath, x_train_save)
np.save(y_train_savepath, y_train)
np.save(x_test_savepath, x_test_save)
np.save(y_test_savepath, y_test)

model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['sparse_categorical_accuracy'])

model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
model.summary()

4、效果

制作完数据集之后开始用神经网络训练:
【神经网络八股扩展】:自制数据集_人工智能_04
可以发现原本的文件夹中出现了你所需要的npy文件。
【神经网络八股扩展】:自制数据集_人工智能_05
完整代码:

import tensorflow as tf
from PIL import Image
import numpy as np
import os

train_path = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_train_jpg_60000/'
train_txt = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_train_jpg_60000.txt'
x_train_savepath = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_x_train.npy'
y_train_savepath = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fahion_y_train.npy'

test_path = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_test_jpg_10000/'
test_txt = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_test_jpg_10000.txt'
x_test_savepath = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_x_test.npy'
y_test_savepath = 'D:/python code/AI/class4/FASHION_FC/fashion_image_label/fashion_y_test.npy'


def generateds(path, txt):
f = open(txt, 'r')
contents = f.readlines() # 按行读取
f.close()
x, y_ = [], []
for content in contents:
value = content.split() # 以空格分开,存入数组
img_path = path + value[0]
img = Image.open(img_path)
img = np.array(img.convert('L'))
img = img / 255.
x.append(img)
y_.append(value[1])
print('loading : ' + content)

x = np.array(x)
y_ = np.array(y_)
y_ = y_.astype(np.int64)
return x, y_


if os.path.exists(x_train_savepath) and os.path.exists(y_train_savepath) and os.path.exists(
x_test_savepath) and os.path.exists(y_test_savepath):
print('-------------Load Datasets-----------------')
x_train_save = np.load(x_train_savepath)
y_train = np.load(y_train_savepath)
x_test_save = np.load(x_test_savepath)
y_test = np.load(y_test_savepath)
x_train = np.reshape(x_train_save, (len(x_train_save), 28, 28))
x_test = np.reshape(x_test_save, (len(x_test_save), 28, 28))
else:
print('-------------Generate Datasets-----------------')
x_train, y_train = generateds(train_path, train_txt)
x_test, y_test = generateds(test_path, test_txt)

print('-------------Save Datasets-----------------')
x_train_save = np.reshape(x_train, (len(x_train), -1))
x_test_save = np.reshape(x_test, (len(x_test), -1))
np.save(x_train_savepath, x_train_save)
np.save(y_train_savepath, y_train)
np.save(x_test_savepath, x_test_save)
np.save(y_test_savepath, y_test)

model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
metrics=['sparse_categorical_accuracy'])

model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
model.summary()

总结




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