Now comes the part where we build up all these components together. in a 6-class problem, the third label corresponds to ) suited for classification. computations from source files) without worrying that data generation becomes a bottleneck in the training process.Īlso, please note that we used Keras' _categorical function to convert our numerical labels stored in y to a binary form (e.g.
#Gear template generator tutorial code
Since our code is multicore-friendly, note that you can do more complex operations instead (e.g. Return X, _categorical(y, num_classes = self.n_classes)ĭuring data generation, this code reads the NumPy array of each example from its corresponding file ID.npy.
# Generate data for i, ID in enumerate(list_IDs_temp): Y = np.empty(( self.batch_size), dtype = int) X = np.empty(( self.batch_size, * self.dim, self.n_channels)) 'Generates data containing batch_size samples ' # X : (n_samples, *dim, n_channels) # Initialization We make the latter inherit the properties of so that we can leverage nice functionalities such as multiprocessing.ĭef _data_generation( self, list_IDs_temp):
#Gear template generator tutorial how to
Now, let's go through the details of how to set the Python class DataGenerator, which will be used for real-time data feeding to your Keras model.įirst, let's write the initialization function of the class. Where data/ is assumed to be the folder containing your dataset.įinally, it is good to note that the code in this tutorial is aimed at being general and minimal, so that you can easily adapt it for your own dataset. In that case, the Python variables partition and labels look like > partitionĪlso, for the sake of modularity, we will write Keras code and customized classes in separate files, so that your folder looks like folder/