tensorflow version 2.3.1 numpy version 1.20
below the code
# define model
model = Sequential()
model.add(LSTM(50, activation='relu', input_shape=(n_steps, n_features)))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
we got
NotImplementedError: Cannot convert a symbolic Tensor (lstm_2/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported
it seems to me a crazy error!
I had the same problem, solved it by downgrading python from 3.8 to 3.6
Similar issue, with
on Windows 7.
Solved by modifying tensorflow/python/framework/ops.py, replacing
def __array__(self):
raise NotImplementedError(
at line #845~846 with
def __array__(self):
raise TypeError(
.
Tensorflow 2.5 update:
tensorflow and tensorflow-gpu 2.5 packages still includes numpy-1.19.5 as a dependency.
The error referenced in this post will be reproduced if tensorflow 2.5 installation is mixed with numpy>1.19.5
tensorflow-2.5, numpy-1.19.5 are compatible with python-3.9
If you are using anaconda:
conda install numpy=1.19
I faced this issue with M1 chip. Here is the how I fixed:
conda create create --name tf
conda activate tf
conda install numpy ~=1.18.5
pip install tensorflow-macos
and voila you are ready to go !
I had the same issue with tensorflow 2.5.0 and numpy 1.21.2. There were suggestions here to make changes in array_ops.py file but this didn't work for me. Another answer in the same page with following steps worked.
pip uninstall tensorflow
pip install tensorflow
pip uninstall numpy
pip install numpy
Basically these steps don't downgrade numpy but either upgrades or keeps it at the same level. Above steps upgraded tensorflow 2.7.0 and numpy 1.21.4 and my code ran without any issues.
I solved with numpy downgrade to 1.18.5
pip install -U numpy==1.18.5