是一种方便的方法,用于从目录中加载图像数据集并将其转换为可用于训练和评估模型的TensorFlow数据集。 image_dataset_from_directory函数是TensorFlow中tf.keras.preprocessing.image模块中的一个函数。它可以根据指定的目录结构自动加载图像数据集,并将其转换为TensorFlow数据集对象。 使用image_dataset_from_directory函...
我试过使用 (foo, foo1) = tf.keras.preprocessing.image_dataset_from_directory(dataDirectory,etc),但我得到 (trainData, trainLabels) = tf.keras.preprocessing.image_dataset_from_directory( ValueError:toomanyvaluestounpack(expected2 )如果我尝试将其作为一个变量返回,然后将其拆分为: train = tf.keras....
image_dataset_from_directory(directory,labels ="inferred",label_mode ="int",class_names =NULL,color_mode ="rgb",batch_size =32,image_size =c(256,256),shuffle =TRUE,seed =NULL,validation_split =NULL,subset =NULL,interpolation ="bilinear",follow_links =FALSE,crop_to_aspect_ratio =FALSE,....
运行程序为 linux debain 10 ,是 香橙派 orangepi 4 lts , tensorflow 2.4 , 需要修改 tensorflow 源码 nano ~/.local/lib/python3.7/site-packages/tensorflow/keras/utils/ __init__.py from tensorflow.python.keras.preprocessing.image_dataset import image_dataset_from_directory #添加 发布...
杀毒后遗症。 病毒已被杀掉，但病毒在启动项里面加上的启动信息还在，所以开机系统仍试图运行病毒但找...
原因,2.1or2.2稳定版本的tensorflow没有这个函数: The specific function (tf.keras.preprocessing.image_dataset_from_directory) is not available under TensorFlow v2.1.x or v2.2.0 yet. It is only available with thetf-nightlybuilds and is existent in the source code of themasterbranch. ...
fromtensorflow.keras.preprocessing.imageimportImageDataGeneratorbatch_size=64img_height=224img_width=224data_dir='fruits/train'dataset=tf.keras.preprocessing.image_dataset_from_directory(data_dir,\image_size=(img_height,img_width),\batch_size=batch_size)class_names=dataset.class_namesprint(class_names...
AttributeError: module 'tensorflow.keras.preprocessing' has no attribute 'image_dataset_from_directory' tensorflow version: 2.2.0 keras version: 2.3.0-tf I was looking at the source itself:https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python/keras/preprocessing ...
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import tensorflow_hub as tfhub; print(f"\t\t– TENSORFLOW HUB VERSION: {tfhub.__version__}"); import tensorflow_addons as tfa; print(f"\t\t– TENSORFLOW ADDONS VERSION: {tfa.__version__}"); import pandas as pd; pd.options.mode.chained_assignment = None; ...