The functionality of numpy.mean and tensorflow.reduce_mean are the same. They do the same thing. From the documentation, for numpy and tensorflow, you can see that. Lets look at an example, Output Here you can see that when axis(numpy) or reduction_indices(tensorflow) is 1, it computes mean across (3,4) and (5,6) and (6,7), so 1 defines across which axis the mean is computed. When it is 0, the … Read more
I am trying to build a predictive model on stock prices. From what I’ve read, LSTM is a good layer to use. I can’t fully understand what my input_shape needs to be for my model though. Here is the tail of my DataFrame I then split the data into train / test This yields: Here’s where I am getting confused. … Read more
If you are using TensorFlow with GPU, you need to install CUDA and cuDNN. Please follow instructions on https://www.tensorflow.org/install/ If you have already install CUDA and cuDNN, but still get this error, then you probably forgot to export your libraries: for Linux, you may need to set LD_LIBRARY_PATH to include CUDA libraries.
Try to put libcuda.so.1 path to LD_LIBRARY_PATH environment variable. example:
You would need to install pip3. On Linux, the command would be: sudo apt install python3-pip On Mac, using brew, first brew install python3Then brew postinstall python3 Try calling pip3 -V to see if it worked.
No, I don’t think “open CUDA library” is enough to tell, because different nodes of the graph may be on different devices. When using tensorflow2: For tensorflow1, to find out which device is used, you can enable log device placement like this: Check your console for this type of output.
Simply: categorical_crossentropy (cce) produces a one-hot array containing the probable match for each category, sparse_categorical_crossentropy (scce) produces a category index of the most likely matching category. Consider a classification problem with 5 categories (or classes). In the case of cce, the one-hot target may be [0, 1, 0, 0, 0] and the model may predict [.2, .5, .1, .1, .1] (probably right) In the … Read more
Yes you can run keras models on GPU. Few things you will have to check first. your system has GPU (Nvidia. As AMD doesn’t work yet) You have installed the GPU version of tensorflow You have installed CUDA installation instructions Verify that tensorflow is running with GPU check if GPU is working sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) for TF … Read more
simply download MSVCP140.dll, unzip it and then paste it in system32 folder..
The following image shows various versions of tensorflow installed however I am not able to uninstall it. I tried pip, pip3, conda and still it does not recognise tensorflow. I even tried all the possibilities from this thread Tensorflow: why ‘pip uninstall tensorflow’ cannot find tensorflow How to remove Tensorflow completely ?