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parameters means less complexity and less ability to overfit data augmentation -> more data samples means more variation in the dataset for the model to capture early-stopping -> use something like the Keras … callback, which will stop the model training once the validation loss doesn't decrease for a number of epochs If you happen to be using image data, you might take a look at the Keras ImageDataGenerator …

For the second expression it is really just the same thing, but if you haven't provided a batch shape, Tensorflow & Keras represent that as something that could be anything, and store it as None. …

Given you already have the tf.data.Dataset, one way to do it would be to iterate over the dataset and each time you come across a new label, save that e.g. to a dictionary, otherwise skip an already s …

You could try using the flow_from_directory() method on your ImageDataGenerator class, which does the augmentation - only a small change is necessary: H = model.fit( aug.flow_from_directory(trainX …

other than using a cronjob because it will be unnecessary to pay for the time and resources when it will not be in use It sounds like you want to use cloud compute. I would suggest looking at th …

Your images_dir actually seems to be the path to a single image... but nevertheless, I would simply create a single number array with shape: (num_images, height, width, channels) by doing the followi …

It is hard to know what is happening from just that screenshot and no code. The training and validation plots are usually separated on the page, not lines on the same graph. If you are using Tensorf …

It really only tracks the value of the metric you selected, there is no tolerance option. In the relevant documentation, the definition is given: save_best_only: if save_best_only=True, the latest …

Keras (or its backend) caches this data as much as it can, meaning all subsequent epoch train faster. …

There is a section on fine-tuning the Keras implementation of the InceptionV3 network, but the principals are the same: you should freeze some of the earlier feature-extraction layers, leaving only some …

Interesting question :) Using spectrograms means you are essentially using images (of frequencies varying over time). I understand the content is in understood like the graph i.e. with axes time and …

Yes, by default it does shuffle. Here is the documentation. The default call signature: fit(x=None, y=None, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_d …

Documentation If you look at the Tensorflow/Keras documentation for LSTM modules (or any recurrent cell), you will notice that they speak of two activations: an (output) activation and a recurrent activation …

Here is a summary from their homepage: Here is a longer description, and here is a full example based on Keras. …

Do you set the random seed in Numpy/Keras/Tensorflow? Did you set any Nvidia CUDA flags for determinism? …