How to perform k-fold cross validation with tensorflow?

Question

I am following the IRIS example of tensorflow.

My case now is I have all data in a single CSV file, not separated, and I want to apply k-fold cross validation on that data.

I have

data_set = tf.contrib.learn.datasets.base.load_csv(filename="mydata.csv",                                                    target_dtype=np.int) 

How can I perform k-fold cross validation on this dataset with multi-layer neural network as same as IRIS example?

Answer 1

I know this question is old but in case someone is looking to do something similar, expanding on ahmedhosny's answer:

The new tensorflow datasets API has the ability to create dataset objects using python generators, so along with scikit-learn's KFold one option can be to create a dataset from the KFold.split() generator:

import numpy as np  from sklearn.model_selection import LeaveOneOut,KFold  import tensorflow as tf import tensorflow.contrib.eager as tfe tf.enable_eager_execution()  from sklearn.datasets import load_iris data = load_iris() X=data['data'] y=data['target']  def make_dataset(X_data,y_data,n_splits):      def gen():         for train_index, test_index in KFold(n_splits).split(X_data):             X_train, X_test = X_data[train_index], X_data[test_index]             y_train, y_test = y_data[train_index], y_data[test_index]             yield X_train,y_train,X_test,y_test      return tf.data.Dataset.from_generator(gen, (tf.float64,tf.float64,tf.float64,tf.float64))  dataset=make_dataset(X,y,10) 

Then one can iterate through the dataset either in the graph based tensorflow or using eager execution. Using eager execution:

for X_train,y_train,X_test,y_test in tfe.Iterator(dataset):     ....