Year 2021: this works with miniconda on Windows 10 conda create -n openblas python=3.8 conda activate openblas conda install conda-forge::blas=*=openblas conda install -c conda-forge numpy Tested also with matplotlib-base.
Running Theano on Python 3.4 is complicated. So far I would recommend that you run Theano in Python 2.7. The libraries written for Theano are Python 2.6+ based. So in order to get Theano running in Python 3.4, you would be needing the 2to3 automated python 2 to 3 code translation tool. I haven’t tested … Read more
There should be a way to do it by setting only the Path environment variable but I could never get that to work. The only thing that worked for me was to manually copy the CuDNN files into the appropriate folders in your CUDA installation. For example, if your CUDA installation is in C:\CUDA\v7.0 and … Read more
The error message is a bit misleading, as you can see here. The problem is that graphviz is not installed. But you mention that graphviz was installed using pip. This is also misleading, since that graphviz package is just a python wrapper, and the graphviz binaries have to be installed separately for the python wrapper … Read more
It is my solution for the same problem Install TDM GCC x64. Install Anaconda x64. Open the Anaconda prompt Run conda update conda Run conda update –all Run conda install mingw libpython Install the latest version of Theano, pip install git+git://github.com/Theano/Theano.git Run pip install git+git://github.com/fchollet/keras.git
Regression with neural networks is hard to get working because the output is unbounded, so you are especially prone to the exploding gradients problem (the likely cause of the nans). Historically, one key solution to exploding gradients was to reduce the learning rate, but with the advent of per-parameter adaptive learning rate algorithms like Adam, … Read more
To get the gradients of model output with respect to weights using Keras you have to use the Keras backend module. I created this simple example to illustrate exactly what to do: from keras.models import Sequential from keras.layers import Dense, Activation from keras import backend as k model = Sequential() model.add(Dense(12, input_dim=8, init=”uniform”, activation=’relu’)) model.add(Dense(8, … Read more
There are lots of things I have seen make a model diverge. Too high of a learning rate. You can often tell if this is the case if the loss begins to increase and then diverges to infinity. I am not to familiar with the DNNClassifier but I am guessing it uses the categorical cross … Read more
You can find the answer at the Keras docs: https://keras.io/getting-started/faq/#how-can-i-obtain-reproducible-results-using-keras-during-development. In short, to be absolutely sure that you will get reproducible results with your python script on one computer’s/laptop’s CPU then you will have to do the following: Set the PYTHONHASHSEED environment variable at a fixed value Set the python built-in pseudo-random generator at a … Read more