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I am a pretty new user of Keras. Recently I have started using it to train quite simple neural networks. I installed Keras, tensorflow-GPU, CUDA and CUDNN. Here is the code I am running :

num_epochs=500
all_mae_histories = []

for i in range(k):
    print('procesing fold #',i)
    val_data=train_data[i*num_val_samples:(i+1)*num_val_samples]
    val_targets=train_targets[i*num_val_samples: (i+1)*num_val_samples]

    partial_train_data = np.concatenate(
            [train_data[:i*num_val_samples],
             train_data[(i+1)*num_val_samples:]],
             axis=0)
    partial_train_targets = np.concatenate(
            [train_targets[:i*num_val_samples],
             train_targets[(i+1)*num_val_samples:]],
             axis=0)

    model = build_model()

    history= model.fit(partial_train_data, partial_train_targets, epochs=num_epochs,
              batch_size=1,verbose=0)
    mae_history = history.history['mean_absolute_error']
    all_mae_histories.append(mae_history)

average_mae_history=[np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]

basically, it is performing k-fold validation with 500 epochs and tracking the performance of the NN as a function of the epoch. Executing this code is quite long even though I have a NVIDIA 1060 graphic card. So I checked the load on the grapic card and it turned out that the code is using only 10% of the GPU and 50 % of each of my 8 CPU.

So my question is : how can I get Keras use the GPU rather than the CPU ? Would this lead to a faster execution ?

Thanks

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OK, I finally got this. Everything was fine. It is just that the use of the GPU depends on the type of task at hand. When working with larger batchs and larger datasets, my GPU reaches higher load. So I guess this is just normal.

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