TensorFlow with Batch and Stochastic Training. We will show how to extend the prior regression example(TensorFlow Backpropagation), which used stochastic training to batch training.

TensorFlow updates our model variables according to the prior described backpropagation, it can operate on anywhere from one datum observation to a large group of data at once. Operating on one training example can make for a very erratic learning process while using a too large batch can be computationally expensive. Choosing the right type of training is crucial to getting our machine learning algorithms to converge to a solution.

- In order for TensorFlow to compute the variable gradients for backpropagation to work, we have to measure the loss on a sample or multiple samples.
- Stochastic training is only putting through one randomly sampled data-target pair at a time.
- Another option is to put a larger portion of the training examples in at a time and average the loss for the gradient calculation.
- Batch training size can vary up to and including the whole dataset at once.

We will show how to extend the prior regression example(TensorFlow Backpropagation), which used stochastic training to batch training.

Let’s begin with by loading NumPy , matplotlib , and TensorFlow and start a graph session, as

follows:

```
import matplotlib as plt
import numpy as np
import tensorflow as tf
sess = tf.Session()
```

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