【问题标题】:cannot import name 'ops' python无法导入名称'ops' python
【发布时间】:2018-06-28 06:42:49
【问题描述】:

我正在尝试运行一个应用程序。但是我收到一个错误:

from createDB import load_dataset
import numpy as np
import keras
from keras.utils import to_categorical
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from keras.models import Sequential,Input,Model
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU
#################################################33
#show dataset
X_train,y_train,X_test,y_test = load_dataset()
print('Training data shape : ', X_train.shape, y_train.shape)
print('Testing data shape : ', X_test.shape, y_test.shape)
############################################################
# Find the unique numbers from the train labels
classes = np.unique(y_train)
nClasses = len(classes)
print('Total number of outputs : ', nClasses)
print('Output classes : ', classes)
###################################################
#plt.figure(figsize=[5,5])
#
## Display the first image in training data
#plt.subplot(121)
#plt.imshow(X_train[0,:,:], cmap='gray')
#plt.title("Ground Truth : {}".format(y_train[0]))
#
## Display the first image in testing data
#plt.subplot(122)
########################################################
#X_train.max()
#X_train.shape()
##################################
# normalization and float32
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train = X_train / 255.
X_test = X_test / 255.
###############################3
#Change the labels from categorical to one-hot encoding
y_train_one_hot = to_categorical(y_train)
y_test_one_hot = to_categorical(y_test)

# Display the change for category label using one-hot encoding
print('Original label:', y_train[25])
print('After conversion to one-hot:', y_train_one_hot[25])
############################################
# training split to trainig and validation
X_train,X_valid,train_label,valid_label = train_test_split(X_train, y_train_one_hot, test_size=0.2, random_state=13)
X_train.shape,
X_valid.shape,
train_label.shape,
valid_label.shape
#########################
batch_size = 64
epochs = 20
num_classes = 3
####################
fashion_model = Sequential()
fashion_model.add(Conv2D(32, kernel_size=(3, 3),activation='linear',input_shape=(28,28,3),padding='same'))
fashion_model.add(LeakyReLU(alpha=0.1))
fashion_model.add(MaxPooling2D((2, 2),padding='same'))
fashion_model.add(Conv2D(64, (3, 3), activation='linear',padding='same'))
fashion_model.add(LeakyReLU(alpha=0.1))
fashion_model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
fashion_model.add(Conv2D(128, (3, 3), activation='linear',padding='same'))
fashion_model.add(LeakyReLU(alpha=0.1))                  
fashion_model.add(MaxPooling2D(pool_size=(2, 2),padding='same'))
fashion_model.add(Flatten())
fashion_model.add(Dense(128, activation='linear'))
fashion_model.add(LeakyReLU(alpha=0.1))                  
fashion_model.add(Dense(num_classes, activation='softmax'))

文件“F:\anaconda\install\envs\anaconda35\lib\site-packages\keras\backend\tensorflow_backend.py”,第 6 行,在 from tensorflow.python.framework import ops as tf_ops

ImportError: 无法导入名称'ops'

如何解决此错误?

【问题讨论】:

    标签: tensorflow machine-learning keras


    【解决方案1】:

    遇到同样的问题,升级无法解决问题

    我解决了:

    sudo pip uninstall keras
    sudo pip uninstall tensorflow
    
    sudo pip install tensorflow
    sudo pip install keras
    

    现在运行良好。

    【讨论】:

      【解决方案2】:

      你可以试试这个:

      pip install tensorflow --upgrade
      pip install keras --upgrade
      

      也许 Keras 框架会检查您的 TensorFlow 后端版本是否太旧。

      【讨论】:

      • 谢谢,但他们已升级到最新版本
      【解决方案3】:

      先尝试卸载:

      pip uninstall tensorflow tensorflow-gpu protocol --yes
      
      pip install tensorflow-gpu==1.9.0
      
      pip install keras==2.2.0
      

      【讨论】:

        【解决方案4】:

        使用 pip uninstall 删除 keras,然后使用

        安装 keras
        conda install keras
        

        如果你有 conda 发行版

        【讨论】:

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