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Tensorflow分类器项目自定义数据读入的要领引见(代码示例)【Python教程】,tensorflow,python,input

摘要: 本篇文章给人人带来的内容是关于Tensorflow分类器项目自定义数据读入的要领引见(代码示例),有肯定的参考价值,有须要的朋侪能够参考一下,愿望对你有所协助。Tensorflow分类器项目自...

本篇文章给人人带来的内容是关于Tensorflow分类器项目自定义数据读入的要领引见(代码示例),有肯定的参考价值,有须要的朋侪能够参考一下,愿望对你有所协助。

Tensorflow分类器项目自定义数据读入

在照着Tensorflow官网的demo敲了一遍分类器项目的代码后,运转却是胜利了,效果也不错。然则终究照样要练习本身的数据,所以尝试预备加载自定义的数据,但是demo中只是涌现了fashion_mnist.load_data()并没有细致的读取历程,随后我又找了些材料,把读取的历程纪录在这里。

起首提一下须要用到的模块:

import os

import keras
import matplotlib.pyplot as plt
from PIL import Image
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split

图片分类器项目,起首肯定你要处置惩罚的图片分辨率将是若干,这里的例子为30像素:

IMG_SIZE_X = 30
IMG_SIZE_Y = 30

其次肯定你图片的体式格局目次:

image_path = r'D:\Projects\ImageClassifier\data\set'
path = ".\data"
# 你也能够运用相对路径的体式格局
# image_path =os.path.join(path, "set")

目次下的构造以下:

响应的label.txt以下:

动漫
景致
玉人
物语
樱花

接下来是接在labels.txt,以下:

label_name = "labels.txt"
label_path = os.path.join(path, label_name)
class_names = np.loadtxt(label_path, type(""))

这里轻便起见,直接应用了numpy的loadtxt函数直接加载。

以后就是正式处置惩罚图片数据了,解释就写在内里了:

re_load = False
re_build = False
# re_load = True
re_build = True

data_name = "data.npz"
data_path = os.path.join(path, data_name)
model_name = "model.h5"
model_path = os.path.join(path, model_name)

count = 0

# 这里推断是不是存在序列化以后的数据,re_load是一个开关,是不是强迫重新处置惩罚,测试用,能够去除。
if not os.path.exists(data_path) or re_load:
    labels = []
    images = []
    print('Handle images')
    # 因为label.txt是和图片防备目次的分类目次一一对应的,即每一个子目次的目次名就是labels.txt里的一个label,所以这里能够经由过程读取class_names的每一项去拼接path后读取
    for index, name in enumerate(class_names):
        # 这里是拼接后的子目次path
        classpath = os.path.join(image_path, name)
        # 先推断一下是不是是目次
        if not os.path.isdir(classpath):
            continue
        # limit是测试时刻用的这里能够去除
        limit = 0
        for image_name in os.listdir(classpath):
            if limit >= max_size:
                break
            # 这里是拼接后的待处置惩罚的图片path
            imagepath = os.path.join(classpath, image_name)
            count = count + 1
            limit = limit + 1
            # 应用Image翻开图片
            img = Image.open(imagepath)
            # 缩放到你最初肯定要处置惩罚的图片分辨率大小
            img = img.resize((IMG_SIZE_X, IMG_SIZE_Y))
            # 转为灰度图片,这里彩色通道会滋扰效果,并且会加大盘算量
            img = img.convert("L")
            # 转为numpy数组
            img = np.array(img)
            # 由(30,30)转为(1,30,30)(即`channels_first`),固然你也能够转换为(30,30,1)(即`channels_last`)但为了以后预览处置惩罚后的图片轻易这里采用了(1,30,30)的花样寄存
            img = np.reshape(img, (1, IMG_SIZE_X, IMG_SIZE_Y))
            # 这里应用轮回生成labels数据,个中寄存的现实是class_names中对应元素的索引
            labels.append([index])
            # 添加到images中,末了一致处置惩罚
            images.append(img)
            # 轮回中一些状况的输出,能够去除
            print("{} class: {} {} limit: {} {}"
                  .format(count, index + 1, class_names[index], limit, imagepath))
    # 末了一次性将images和labels都转换成numpy数组
    npy_data = np.array(images)
    npy_labels = np.array(labels)
    # 处置惩罚数据只须要一次,所以我们挑选在这里应用numpy自带的要领将处置惩罚以后的数据序列化存储
    np.savez(data_path, x=npy_data, y=npy_labels)
    print("Save images by npz")
else:
    # 假如存在序列化号的数据,便直接读取,进步速率
    npy_data = np.load(data_path)["x"]
    npy_labels = np.load(data_path)["y"]
    print("Load images by npz")
image_data = npy_data
labels_data = npy_labels

到了这里原始数据的加工预处置惩罚便已完成,只须要末了一步,就和demo中fashion_mnist.load_data()返回的效果一样了。代码以下:

# 末了一步就是将原始数据分红练习数据和测试数据
train_images, test_images, train_labels, test_labels = \
    train_test_split(image_data, labels_data, test_size=0.2, random_state=6)

这里将相干信息打印的要领也附上:

print("_________________________________________________________________")
print("%-28s %-s" % ("Name", "Shape"))
print("=================================================================")
print("%-28s %-s" % ("Image Data", image_data.shape))
print("%-28s %-s" % ("Labels Data", labels_data.shape))
print("=================================================================")

print('Split train and test data,p=%')
print("_________________________________________________________________")
print("%-28s %-s" % ("Name", "Shape"))
print("=================================================================")
print("%-28s %-s" % ("Train Images", train_images.shape))
print("%-28s %-s" % ("Test Images", test_images.shape))
print("%-28s %-s" % ("Train Labels", train_labels.shape))
print("%-28s %-s" % ("Test Labels", test_labels.shape))
print("=================================================================")

以后别忘了归一化哟:

print("Normalize images")
train_images = train_images / 255.0
test_images = test_images / 255.0

末了附上读取自定义数据的完全代码:

import os

import keras
import matplotlib.pyplot as plt
from PIL import Image
from keras.layers import *
from keras.models import *
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# 支撑中文
plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来一般显现中文标签
plt.rcParams['axes.unicode_minus'] = False  # 用来一般显现负号
re_load = False
re_build = False
# re_load = True
re_build = True
epochs = 50
batch_size = 5
count = 0
max_size = 2000000000
IMG_SIZE_X = 30
IMG_SIZE_Y = 30
np.random.seed(9277)
image_path = r'D:\Projects\ImageClassifier\data\set'
path = ".\data"
data_name = "data.npz"
data_path = os.path.join(path, data_name)
model_name = "model.h5"
model_path = os.path.join(path, model_name)
label_name = "labels.txt"
label_path = os.path.join(path, label_name)
class_names = np.loadtxt(label_path, type(""))
print('Load class names')
if not os.path.exists(data_path) or re_load:
    labels = []
    images = []
    print('Handle images')
    for index, name in enumerate(class_names):
        classpath = os.path.join(image_path, name)
        if not os.path.isdir(classpath):
            continue
        limit = 0
        for image_name in os.listdir(classpath):
            if limit >= max_size:
                break
            imagepath = os.path.join(classpath, image_name)
            count = count + 1
            limit = limit + 1
            img = Image.open(imagepath)
            img = img.resize((30, 30))
            img = img.convert("L")
            img = np.array(img)
            img = np.reshape(img, (1, 30, 30))
            # img = skimage.io.imread(imagepath, as_grey=True)
            # if img.shape[2] != 3:
            #     print("{} shape is {}".format(image_name, img.shape))
            #     continue
            # data = transform.resize(img, (IMG_SIZE_X, IMG_SIZE_Y))
            labels.append([index])
            images.append(img)
            print("{} class: {} {} limit: {} {}"
                  .format(count, index + 1, class_names[index], limit, imagepath))
    npy_data = np.array(images)
    npy_labels = np.array(labels)
    np.savez(data_path, x=npy_data, y=npy_labels)
    print("Save images by npz")
else:
    npy_data = np.load(data_path)["x"]
    npy_labels = np.load(data_path)["y"]
    print("Load images by npz")
image_data = npy_data
labels_data = npy_labels
print("_________________________________________________________________")
print("%-28s %-s" % ("Name", "Shape"))
print("=================================================================")
print("%-28s %-s" % ("Image Data", image_data.shape))
print("%-28s %-s" % ("Labels Data", labels_data.shape))
print("=================================================================")
train_images, test_images, train_labels, test_labels = \
    train_test_split(image_data, labels_data, test_size=0.2, random_state=6)
print('Split train and test data,p=%')
print("_________________________________________________________________")
print("%-28s %-s" % ("Name", "Shape"))
print("=================================================================")
print("%-28s %-s" % ("Train Images", train_images.shape))
print("%-28s %-s" % ("Test Images", test_images.shape))
print("%-28s %-s" % ("Train Labels", train_labels.shape))
print("%-28s %-s" % ("Test Labels", test_labels.shape))
print("=================================================================")

# 归一化
# 我们将这些值缩小到 0 到 1 之间,然后将其馈送到神经网络模子。为此,将图象组件的数据类型从整数转换为浮点数,然后除以 255。以下是预处置惩罚图象的函数:
# 务必要以雷同的体式格局对练习集和测试集举行预处置惩罚:
print("Normalize images")
train_images = train_images / 255.0
test_images = test_images / 255.0

以上就是Tensorflow分类器项目自定义数据读入的要领引见(代码示例)的细致内容,更多请关注ki4网别的相干文章!

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