I have Keras installed with the Tensorflow backend and CUDA. I’d like to sometimes on demand force Keras to use CPU. Can this be done without say installing a separate CPU-only Tensorflow in a virtual environment? If so how? If the backend were Theano, the flags could be set, but I have not heard of Tensorflow flags accessible via Keras.
I’m trying to save bottleneck values to a newly created hdf5 file.
The bottleneck values come in batches of shape (120,10,10, 2048).
Saving one alone batch is taking up more than 16 gigs and python seems to be freezing at that one batch. Based on recent findings (see update, it seems hdf5 taking up large memory is okay, but the freezing part seems to be a glitch.
I am interested in building reinforcement learning models with the simplicity of the Keras API. Unfortunately, I am unable to extract the gradient of the output (not error) with respect to the weights. I found the following code that performs a similar function (Saliency maps of neural networks (using Keras))
I was wondering if it was possible to save a partly trained Keras model and continue the training after loading the model again.
I have a data matrix in “one-hot encoding” (all ones and zeros) with 260,000 rows and 35 columns. I am using Keras to train a simple neural network to predict a continuous variable. The code to make the network is the following:
When I run a keras script, I get the following output:
I’m working on a image class-incremental classifier approach using a CNN as a feature extractor and a fully-connected block for classifying.
Let’s suppose I have a sequence of integers:
I’ve ran into serious incompatibility problems for the same code ran with one vs. the other; e.g.:
The code that I have (that I can’t change) uses the Resnet with my_input_tensor as the input_tensor.