Feeding large dataset to neural network using tf I have a csv file of size ~11GB contains 2 million rows located in my local disk as my training data. What's the best way to feed the data to a neural network model? I use tf.keras to build the neural network and my RAM doesn't allow me to import as a single np array. Thank you.
You can use generator to read the data if your model uses "Mini-Batch SGD"-like optimization method which just uses a small batch of samples each step. For example, 

df_iterator = pandas.read_csv(your_data,chunksize=batch_size,iterator=True)
for small_batch in df_iterator:
#feed it to your model's input