Python Keras后端json被定义为tensorflow，但Keras仍然可以'；找不到张量流

我已将keras的json文件更改为：

{
    "image_dim_ordering": "tf", 
    "epsilon": 1e-07, 
    "floatx": "float32", 
    "backend": "tensorflow"
}

但当我为神经网络运行以下简单的Keras教程时：

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.optimizers import SGD

model = Sequential()
# Dense(64) is a fully-connected layer with 64 hidden units.
# in the first layer, you must specify the expected input data shape:
# here, 20-dimensional vectors.
model.add(Dense(64, input_dim=20, init='uniform'))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(64, init='uniform'))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(10, init='uniform'))
model.add(Activation('softmax'))

sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])

model.fit(X_train, y_train,
          nb_epoch=20,
          batch_size=16)
score = model.evaluate(X_test, y_test, batch_size=16)

摘自：

我仍然得到以下错误：

Using TensorFlow backend.
Traceback (most recent call last):
  File "./keras_test", line 3, in <module>
    from keras.models import Sequential
  File "/usr/local/lib/python2.7/dist-packages/keras/__init__.py", line 2, in <module>
    from . import backend
  File "/usr/local/lib/python2.7/dist-packages/keras/backend/__init__.py", line 67, in <module>
    from .tensorflow_backend import *
  File "/usr/local/lib/python2.7/dist-packages/keras/backend/tensorflow_backend.py", line 1, in <module>
    import tensorflow as tf
ImportError: No module named tensorflow

如果我再次运行它，我会得到以下输出：

Requirement already satisfied (use --upgrade to upgrade): keras in /usr/local/lib/python2.7/dist-packages
Requirement already satisfied (use --upgrade to upgrade): theano in /usr/local/lib/python2.7/dist-packages (from keras)
Requirement already satisfied (use --upgrade to upgrade): pyyaml in /usr/local/lib/python2.7/dist-packages (from keras)
Requirement already satisfied (use --upgrade to upgrade): six in /usr/local/lib/python2.7/dist-packages (from keras)
Requirement already satisfied (use --upgrade to upgrade): numpy>=1.7.1 in /usr/local/lib/python2.7/dist-packages (from theano->keras)
Requirement already satisfied (use --upgrade to upgrade): scipy>=0.11 in /usr/local/lib/python2.7/dist-packages (from theano->keras)
You are using pip version 8.1.2, however version 9.0.1 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.

此外，我100%确信我的Tensorflow安装工作正常，因为我已经为它运行了（并编写了）一些GPU Cuda示例

---

谢谢

我想你忘了最明显的一件事，TensorFlow没有安装，也不是Keras依赖项。我建议您使用以下设备安装TensorFlow：

pip install --user tensorflow

---

这将在我们的用户文件夹（~/.local）中安装TensorFlow，并且不需要root权限。

您可以安装TensorFlow和keras的所有依赖项，如下所示：

此设置适用于Ubuntu 14.04服务器

# Pick up some TF dependencies
apt-get update && apt-get install -y --no-install-recommends \
    build-essential \
    curl \
    git \
    cmake \
    libfreetype6-dev \
    libpng12-dev \
    libzmq3-dev \
    pkg-config \
    python \
    python-dev \
    rsync \
    software-properties-common \
    unzip \
    && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*

curl -O https://bootstrap.pypa.io/get-pip.py && \
python get-pip.py && \
rm get-pip.py

pip --no-cache-dir install --upgrade ipython && \
pip --no-cache-dir install \
    ipykernel \
    jupyter \
    matplotlib \
    numpy \
    scipy \
    sklearn \
    pandas \
    Pillow \
    && \
python -m ipykernel.kernelspec

# Install TensorFlow CPU version from central repo
pip --no-cache-dir install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.1-cp27-none-linux_x86_64.whl

# h5py is optional dependency for keras
apt-get update && apt-get install -y libhdf5-dev libhdf5-serial-dev
pip install keras h5py

如果你仍然有环境问题，我建议你使用这个。这使我们能够独立于本地python工作，我发现本地python对于在任何其他系统上复制环境都非常有用。您可能还发现Datmo转换有助于实现这一点

---

免责声明：我在一家名为的公司工作，我们正在通过简化机器学习工作流程建立一个开发人员社区

您是否在conda环境中运行keras？当您启动python然后“导入tensorflow”时会发生什么？当我启动python并使用“导入tensorflow”时，我没有收到任何错误消息。一切都很完美。我不是在conda环境下运行，一切都在我的主pc/系统上。话虽如此，当我在virtualenv中做所有事情时，一切似乎都很好（这并不是说这有助于解决我在这里发布的问题），但我至少可以开始与keras合作了/我相信我已经安装了TensorFlow。：/因为我已经在我的电脑上安装并使用了tensorflow gpu，所以我知道它就在那里functional@pche8701当然可以，但问题中没有任何证据表明安装了tensorflow。请提供一些。

# Pick up some TF dependencies
apt-get update && apt-get install -y --no-install-recommends \
    build-essential \
    curl \
    git \
    cmake \
    libfreetype6-dev \
    libpng12-dev \
    libzmq3-dev \
    pkg-config \
    python \
    python-dev \
    rsync \
    software-properties-common \
    unzip \
    && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*

curl -O https://bootstrap.pypa.io/get-pip.py && \
python get-pip.py && \
rm get-pip.py

pip --no-cache-dir install --upgrade ipython && \
pip --no-cache-dir install \
    ipykernel \
    jupyter \
    matplotlib \
    numpy \
    scipy \
    sklearn \
    pandas \
    Pillow \
    && \
python -m ipykernel.kernelspec

# Install TensorFlow CPU version from central repo
pip --no-cache-dir install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.1-cp27-none-linux_x86_64.whl

# h5py is optional dependency for keras
apt-get update && apt-get install -y libhdf5-dev libhdf5-serial-dev
pip install keras h5py