Use a generator for Keras model.fit_generator

Question

I originally tried to use generator syntax when writing a custom generator for training a Keras model. So I yielded from __next__. How

Answer 1

This is the way I implemented it for reading files any size. And it works like a charm.

import pandas as pd

hdr=[]
for i in range(num_labels+num_features):
    hdr.append("Col-"+str(i)) # data file do not have header so I need to
                              # provide one for pd.read_csv by chunks to work

def tgen(filename):
    csvfile = open(filename)
    reader = pd.read_csv(csvfile, chunksize=batch_size,names=hdr,header=None)
    while True:
    for chunk in reader:
        W=chunk.values        # labels and features
        Y =W[:,:num_labels]   # labels 
        X =W[:,num_labels:]   # features
        X= X / 255            # any required transformation
        yield X, Y
    csvfile = open(filename)
    reader = pd.read_csv(csvfile, chunksize=batchz,names=hdr,header=None)

The back in the main I have

nval=number_of_validation_samples//batchz
ntrain=number_of_training_samples//batchz
ftgen=tgen("training.csv")
fvgen=tgen("validation.csv")

history = model.fit_generator(ftgen,
                steps_per_epoch=ntrain,
                validation_data=fvgen,
                validation_steps=nval,
                epochs=number_of_epochs,
                callbacks=[checkpointer, stopper],
                verbose=2)