The MNIST set consists of 60,000 images for training set. While training my Tensorflow, I want to run the train step to train the model with the entire training set. The deep learning example on the Tensorflow website uses 20,000 iterations with a batch size of 50 (totaling to 1,000,000 batches). When I try more than 30,000 iterations, my number predictions fail (predicts 0 for all handwritten numbers). My questions is, how many iterations should I use with a batch size of 50 to train the tensorflow model with the entire MNIST set?
self.mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
for i in range(FLAGS.training_steps):
batch = self.mnist.train.next_batch(50)
self.train_step.run(feed_dict={self.x: batch[0], self.y_: batch[1], self.keep_prob: 0.5})
if (i+1)%1000 == 0:
saver.save(self.sess, FLAGS.checkpoint_dir + 'model.ckpt', global_step = i)
I think that depends on your stop criteria. You can stop training when loss doesn't improve, or you can have a validation data set, and stop training when validation accuracy doesn't improve any more.