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Check out the official docs and this issue for the Keras version. … as K import keras...... …

As the link you added suggests, you must also create a wrapper function to use this custom function as a loss function in Keras: def specificity_loss_wrapper(): """A wrapper to create and return a … This can then be used like this: # Create a Keras model object as usual model = my_model() # ... …

You could alter the code above in your local version of Keras to cover your case, essentially converting the received input into a NumPy array, which would then pass then checks and be used. … Testing Just an example to show how the above transformation should really end up with your data being fed to the model as a numpy array: import numpy as np import keras a = np.arange(0, 75).reshape …

Using valid will essentially use as much of your input as possible, such that the dimensions continue to work. This means there is a chance some input will be trimmed (removed). same on the other han …

I think your train_images array should have shape (79, 698, 608, 3). The generator works through each of the first dimensions of those arrays, so is passing a batch of 4d numpy arrays, instead of a ba …

If theya re showing, but Keras/Tensorflow is not finding them, have a look at this thread for more checks for Tensorflow backend. …

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 …

You could perhaps define a custom function that compute the regularisation terms you are using, and execute that as either your own metric or as a callback function in a Keras model. …

I assume you mean that you build a network using Keras (which contains recurrent GRU layers) and would like to save the model after some training, then restart from the same point e.g. with new data or …

I am afraid it is not that simple - Have a look at this pretty good walkthrough. The table you posted is a kind of overview that doesn't contain all the details of how the "blocks" are linked. Other …

I suggest having a look at the relevant documentation. There, it states: rotation_range: Int. Degree range for random rotations. ... horizontal_flip: Boolean. Randomly flip inputs horizontally. Sayi …

It must be in your generators - I ran the following code and a model trained as expected: from keras import models, layers, metrics import numpy as np model = models.Sequential() ...: model.add(layers.Conv2D …

There is a kind of bias that you are introducing, yes. You are basically extracting some statistics (i.e. the mean) from your hold-out set and using that to train, which makes your final claims of acc …

Also, have a look here for a sample of a larger network performing classification, to see how R Keras is best used. … activation = "softmax") model %>% compile( optimizer = "rmsprop", loss = "categorical_crossentropy", metrics = c("accuracy") ) history <- model %>% keras …

One thing that isn't always obvious if you just started learning KEras, is that you can mix in Tensorflow operations directly from the Tensorflow library in with your Keras code. … If you don't force the padded tensors into a Keras layer, attributes will be missing: # AttributeError: 'Tensor' object has no attribute '_keras_history' Which you can hack by just adding the attribute …