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【神经网络八股扩展】：自制数据集前言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

课程来源：人工智能实践:Tensorflow笔记2

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

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

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

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

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

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

课程链接:MOOC人工智能实践：TensorFlow笔记2

# 机器学习实战 人工智能 Python 深度学习 tensorflow
上一篇: python_tensorflow笔记（3）神经网络实现鸢尾花分类
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