但是,既然你的问题是关于从keras.utils.np_utils导入to_categorical,我将基于这个前提进行回答。如果你使用的是旧版本的Keras,可以按照以下方式导入: python from keras.utils.np_utils import to_categorical 如果你使用的是TensorFlow 2.x中的Keras,则应该这样导入: ...
代码: import cv2 import numpy as np from PIL import Image import os import numpy as np import cv2 import os import h5py import dlib from imutils import face_utils from keras.models import load_model import sys from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D,...
你不能在Python==2和Python〈3.6中使用keras，因为keras使用f字符串：
from tensorflow.keras.datasets import cifar10 from tensorflow.keras.utils import to_categorical # ...
This code works in TensorFlow version 1, but starting in TensorFlow version 2, thekerasmodule is now bundled withtensorflow. You need to change the import statement to this: fromtensorflow.keras.utilsimportto_categorical So that Python can find theto_categorical()function and you won’t receive...
用from tensorflow.keras.utils import get_file 代替 from keras.utils import get_file 原文地址:https://exerror.com/importerror-cannot-import-name-to-categorical-from-keras-utils-usr-local/
from keras.utils import to_categorical import keras.backend as K import matplotlib.pyplot as plt import numpy as np class CUSGAN(): definit(self): self.img_rows = 28 self.img_cols = 28 self.channels = 1 self.img_shape = (self.img_rows, self.img_cols, self.channels) ...
from pickle import load from numpy import array from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical from keras.utils.vis_utils import plot_model from keras.models import Sequential from keras.layers import LSTM from...
Source File: generic_utils.py From artificial_neural_networks with Apache License 2.0 6 votes def load_keras_model(h5_file, json_file=None, yaml_file=None, is_weights=False, from_json=True): """ Utility to load the whole model """ # third-party imports from keras.models import load...
從目錄中的文本文件生成tf.data.Dataset。 用法 tf.keras.utils.text_dataset_from_directory( directory, labels='inferred', label_mode='int', class_names=None, batch_size=32, max_length=None, shuffle=True, seed=None, validation_split=None, subset=None, follow_links=False) ...