# MNIST For ML Beginners

*This tutorial is intended for readers who are new to both machine learning and
TensorFlow. If you already know what MNIST is, and what softmax (multinomial
logistic) regression is, you might prefer this
[faster paced tutorial](../pros/index.md).  Be sure to
[install TensorFlow](../../../get_started/os_setup.md) before starting either
tutorial.*

When one learns how to program, there's a tradition that the first thing you do
is print "Hello World." Just like programming has Hello World, machine learning
has MNIST.

MNIST is a simple computer vision dataset. It consists of images of handwritten
digits like these:

<div style="width:40%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../../images/MNIST.png">
</div>

It also includes labels for each image, telling us which digit it is. For
example, the labels for the above images are 5, 0, 4, and 1.

In this tutorial, we're going to train a model to look at images and predict
what digits they are. Our goal isn't to train a really elaborate model that
achieves state-of-the-art performance -- although we'll give you code to do that
later! -- but rather to dip a toe into using TensorFlow. As such, we're going
to start with a very simple model, called a Softmax Regression.

The actual code for this tutorial is very short, and all the interesting
stuff happens in just three lines. However, it is very
important to understand the ideas behind it: both how TensorFlow works and the
core machine learning concepts. Because of this, we are going to very carefully
work through the code.

## About this tutorial

This tutorial is an explanation, line by line, of what is happening in the
[mnist_softmax.py](https://www.tensorflow.org/code/tensorflow/examples/tutorials/mnist/mnist_softmax.py) code.

You can use this tutorial in a few different ways, including:

- Copy and paste each code snippet, line by line, into a Python environment as
  you read through the explanations of each line.

- Run the entire `mnist_softmax.py` Python file either before or after reading
  through the explanations, and use this tutorial to understand the lines of
  code that aren't clear to you.

What we will accomplish in this tutorial:

- Learn about the MNIST data and softmax regressions

- Create a function that is a model for recognizing digits, based on looking at
  every pixel in the image

- Use Tensorflow to train the model to recognize digits by having it "look" at
  thousands of examples (and run our first Tensorflow session to do so)

- Check the model's accuracy with our test data

## The MNIST Data

The MNIST data is hosted on
[Yann LeCun's website](http://yann.lecun.com/exdb/mnist/).  If you are copying and
pasting in the code from this tutorial, start here with these two lines of code
which will download and read in the data automatically:

```python
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
```

The MNIST data is split into three parts: 55,000 data points of training
data (`mnist.train`), 10,000 points of test data (`mnist.test`), and 5,000
points of validation data (`mnist.validation`). This split is very important:
it's essential in machine learning that we have separate data which we don't
learn from so that we can make sure that what we've learned actually
generalizes!

As mentioned earlier, every MNIST data point has two parts: an image of a
handwritten digit and a corresponding label. We'll call the images "x"
and the labels "y". Both the training set and test set contain images and their
corresponding labels; for example the training images are `mnist.train.images`
and the training labels are `mnist.train.labels`.

Each image is 28 pixels by 28 pixels. We can interpret this as a big array of
numbers:

<div style="width:50%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../../images/MNIST-Matrix.png">
</div>

We can flatten this array into a vector of 28x28 = 784 numbers. It doesn't
matter how we flatten the array, as long as we're consistent between images.
From this perspective, the MNIST images are just a bunch of points in a
784-dimensional vector space, with a
[very rich structure](http://colah.github.io/posts/2014-10-Visualizing-MNIST/)
(warning: computationally intensive visualizations).

Flattening the data throws away information about the 2D structure of the image.
Isn't that bad? Well, the best computer vision methods do exploit this
structure, and we will in later tutorials. But the simple method we will be
using here, a softmax regression (defined below), won't.

The result is that `mnist.train.images` is a tensor (an n-dimensional array)
with a shape of `[55000, 784]`. The first dimension is an index into the list
of images and the second dimension is the index for each pixel in each image.
Each entry in the tensor is a pixel intensity between 0 and 1, for a particular
pixel in a particular image.

<div style="width:40%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../../images/mnist-train-xs.png">
</div>

Each image in MNIST has a corresponding label, a number between 0 and 9
representing the digit drawn in the image.

For the purposes of this tutorial, we're going to want our labels as "one-hot
vectors". A one-hot vector is a vector which is 0 in most dimensions, and 1 in a
single dimension. In this case, the \\(n\\)th digit will be represented as a
vector which is 1 in the \\(n\\)th dimension. For example, 3 would be
\\([0,0,0,1,0,0,0,0,0,0]\\).  Consequently, `mnist.train.labels` is a
`[55000, 10]` array of floats.

<div style="width:40%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../../images/mnist-train-ys.png">
</div>

We're now ready to actually make our model!

## Softmax Regressions

We know that every image in MNIST is of a handwritten digit between zero and
nine.  So there are only ten possible things that a given image can be. We want
to be able to look at an image and give the probabilities for it being each
digit. For example, our model might look at a picture of a nine and be 80% sure
it's a nine, but give a 5% chance to it being an eight (because of the top loop)
and a bit of probability to all the others because it isn't 100% sure.

This is a classic case where a softmax regression is a natural, simple model.
If you want to assign probabilities to an object being one of several different
things, softmax is the thing to do, because softmax gives us a list of values
between 0 and 1 that add up to 1. Even later on, when we train more sophisticated
models, the final step will be a layer of softmax.

A softmax regression has two steps: first we add up the evidence of our input
being in certain classes, and then we convert that evidence into probabilities.

To tally up the evidence that a given image is in a particular class, we do a
weighted sum of the pixel intensities. The weight is negative if that pixel
having a high intensity is evidence against the image being in that class, and
positive if it is evidence in favor.

The following diagram shows the weights one model learned for each of these
classes. Red represents negative weights, while blue represents positive
weights.

<div style="width:40%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../../images/softmax-weights.png">
</div>

We also add some extra evidence called a bias. Basically, we want to be able
to say that some things are more likely independent of the input. The result is
that the evidence for a class \\(i\\) given an input \\(x\\) is:

$$\text{evidence}_i = \sum_j W_{i,~ j} x_j + b_i$$

where \\(W_i\\) is the weights and \\(b_i\\) is the bias for class \\(i\\),
and \\(j\\) is an index for summing over the pixels in our input image \\(x\\).
We then convert the evidence tallies into our predicted probabilities
\\(y\\) using the "softmax" function:

$$y = \text{softmax}(\text{evidence})$$

Here softmax is serving as an "activation" or "link" function, shaping
the output of our linear function into the form we want -- in this case, a
probability distribution over 10 cases.
You can think of it as converting tallies
of evidence into probabilities of our input being in each class.
It's defined as:

$$\text{softmax}(x) = \text{normalize}(\exp(x))$$

If you expand that equation out, you get:

$$\text{softmax}(x)_i = \frac{\exp(x_i)}{\sum_j \exp(x_j)}$$

But it's often more helpful to think of softmax the first way: exponentiating
its inputs and then normalizing them.  The exponentiation means that one more
unit of evidence increases the weight given to any hypothesis multiplicatively.
And conversely, having one less unit of evidence means that a hypothesis gets a
fraction of its earlier weight. No hypothesis ever has zero or negative
weight. Softmax then normalizes these weights, so that they add up to one,
forming a valid probability distribution. (To get more intuition about the
softmax function, check out the
[section](http://neuralnetworksanddeeplearning.com/chap3.html#softmax) on it in
Michael Nielsen's book, complete with an interactive visualization.)

You can picture our softmax regression as looking something like the following,
although with a lot more \\(x\\)s. For each output, we compute a weighted sum of
the \\(x\\)s, add a bias, and then apply softmax.

<div style="width:55%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../../images/softmax-regression-scalargraph.png">
</div>

If we write that out as equations, we get:

<div style="width:52%; margin-left:25%; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../../images/softmax-regression-scalarequation.png">
</div>

We can "vectorize" this procedure, turning it into a matrix multiplication
and vector addition. This is helpful for computational efficiency. (It's also
a useful way to think.)

<div style="width:50%; margin:auto; margin-bottom:10px; margin-top:20px;">
<img style="width:100%" src="../../../images/softmax-regression-vectorequation.png">
</div>

More compactly, we can just write:

$$y = \text{softmax}(Wx + b)$$

Now let's turn that into something that Tensorflow can use.

## Implementing the Regression


To do efficient numerical computing in Python, we typically use libraries like
[NumPy](http://www.numpy.org/) that do expensive operations such as matrix
multiplication outside Python, using highly efficient code implemented in
another language.  Unfortunately, there can still be a lot of overhead from
switching back to Python every operation. This overhead is especially bad if you
want to run computations on GPUs or in a distributed manner, where there can be
a high cost to transferring data.

TensorFlow also does its heavy lifting outside Python, but it takes things a
step further to avoid this overhead.  Instead of running a single expensive
operation independently from Python, TensorFlow lets us describe a graph of
interacting operations that run entirely outside Python. (Approaches like this
can be seen in a few machine learning libraries.)

To use TensorFlow, first we need to import it.

```python
import tensorflow as tf
```

We describe these interacting operations by manipulating symbolic variables.
Let's create one:

```python
x = tf.placeholder(tf.float32, [None, 784])
```

`x` isn't a specific value. It's a `placeholder`, a value that we'll input when
we ask TensorFlow to run a computation. We want to be able to input any number
of MNIST images, each flattened into a 784-dimensional vector. We represent
this as a 2-D tensor of floating-point numbers, with a shape `[None, 784]`.
(Here `None` means that a dimension can be of any length.)

We also need the weights and biases for our model. We could imagine treating
these like additional inputs, but TensorFlow has an even better way to handle
it: `Variable`.  A `Variable` is a modifiable tensor that lives in TensorFlow's
graph of interacting operations. It can be used and even modified by the
computation. For machine learning applications, one generally has the model
parameters be `Variable`s.

```python
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
```

We create these `Variable`s by giving `tf.Variable` the initial value of the
`Variable`: in this case, we initialize both `W` and `b` as tensors full of
zeros. Since we are going to learn `W` and `b`, it doesn't matter very much
what they initially are.

Notice that `W` has a shape of [784, 10] because we want to multiply the
784-dimensional image vectors by it to produce 10-dimensional vectors of
evidence for the difference classes. `b` has a shape of [10] so we can add it
to the output.

We can now implement our model. It only takes one line to define it!

```python
y = tf.nn.softmax(tf.matmul(x, W) + b)
```

First, we multiply `x` by `W` with the expression `tf.matmul(x, W)`. This is
flipped from when we multiplied them in our equation, where we had \\(Wx\\), as
a small trick to deal with `x` being a 2D tensor with multiple inputs. We then
add `b`, and finally apply `tf.nn.softmax`.

That's it. It only took us one line to define our model, after a couple short
lines of setup. That isn't because TensorFlow is designed to make a softmax
regression particularly easy: it's just a very flexible way to describe many
kinds of numerical computations, from machine learning models to physics
simulations. And once defined, our model can be run on different devices:
your computer's CPU, GPUs, and even phones!


## Training

In order to train our model, we need to define what it means for the model to be
good. Well, actually, in machine learning we typically define what it means for
a model to be bad. We call this the cost, or the loss, and it represents how far
off our model is from our desired outcome. We try to minimize that error, and
the smaller the error margin, the better our model is.

One very common, very nice function to determine the loss of a model is called
"cross-entropy." Cross-entropy arises from thinking about information
compressing codes in information theory but it winds up being an important idea
in lots of areas, from gambling to machine learning. It's defined as:

$$H_{y'}(y) = -\sum_i y'_i \log(y_i)$$

Where \\(y\\) is our predicted probability distribution, and \\(y'\\) is the true
distribution (the one-hot vector with the digit labels).  In some rough sense, the
cross-entropy is measuring how inefficient our predictions are for describing
the truth. Going into more detail about cross-entropy is beyond the scope of
this tutorial, but it's well worth
[understanding](http://colah.github.io/posts/2015-09-Visual-Information/).

To implement cross-entropy we need to first add a new placeholder to input the
correct answers:

```python
y_ = tf.placeholder(tf.float32, [None, 10])
```

Then we can implement the cross-entropy function, \\(-\sum y'\log(y)\\):

```python
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
```

First, `tf.log` computes the logarithm of each element of `y`. Next, we multiply
each element of `y_` with the corresponding element of `tf.log(y)`. Then
`tf.reduce_sum` adds the elements in the second dimension of y, due to the
`reduction_indices=[1]` parameter. Finally, `tf.reduce_mean` computes the mean
over all the examples in the batch.

Note that in the source code, we don't use this formulation, because it is
numerically unstable.  Instead, we apply
`tf.nn.softmax_cross_entropy_with_logits` on the unnormalized logits (e.g., we
call `softmax_cross_entropy_with_logits` on `tf.matmul(x, W) + b`), because this
more numerically stable function internally computes the softmax activation.  In
your code, consider using `tf.nn.softmax_cross_entropy_with_logits`
instead.

Now that we know what we want our model to do, it's very easy to have TensorFlow
train it to do so.  Because TensorFlow knows the entire graph of your
computations, it can automatically use the
[backpropagation algorithm](http://colah.github.io/posts/2015-08-Backprop/) to
efficiently determine how your variables affect the loss you ask it to
minimize. Then it can apply your choice of optimization algorithm to modify the
variables and reduce the loss.

```python
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
```

In this case, we ask TensorFlow to minimize `cross_entropy` using the
[gradient descent algorithm](https://en.wikipedia.org/wiki/Gradient_descent)
with a learning rate of 0.5. Gradient descent is a simple procedure, where
TensorFlow simply shifts each variable a little bit in the direction that
reduces the cost. But TensorFlow also provides
[many other optimization algorithms](../../../api_docs/python/train.md#optimizers):
using one is as simple as tweaking one line.

What TensorFlow actually does here, behind the scenes, is to add new operations
to your graph which implement backpropagation and gradient descent. Then it
gives you back a single operation which, when run, does a step of gradient
descent training, slightly tweaking your variables to reduce the loss.


We can now launch the model in an `InteractiveSession`:

```python
sess = tf.InteractiveSession()
```

We first have to create an operation to initialize the variables we created:

```python
tf.global_variables_initializer().run()
```


Let's train -- we'll run the training step 1000 times!

```python
for i in range(1000):
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
```

Each step of the loop, we get a "batch" of one hundred random data points from
our training set. We run `train_step` feeding in the batches data to replace
the `placeholder`s.

Using small batches of random data is called stochastic training -- in this
case, stochastic gradient descent. Ideally, we'd like to use all our data for
every step of training because that would give us a better sense of what we
should be doing, but that's expensive. So, instead, we use a different subset
every time. Doing this is cheap and has much of the same benefit.



## Evaluating Our Model

How well does our model do?

Well, first let's figure out where we predicted the correct label. `tf.argmax`
is an extremely useful function which gives you the index of the highest entry
in a tensor along some axis. For example, `tf.argmax(y,1)` is the label our
model thinks is most likely for each input, while `tf.argmax(y_,1)` is the
correct label. We can use `tf.equal` to check if our prediction matches the
truth.

```python
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
```

That gives us a list of booleans. To determine what fraction are correct, we
cast to floating point numbers and then take the mean. For example,
`[True, False, True, True]` would become `[1,0,1,1]` which would become `0.75`.

```python
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
```

Finally, we ask for our accuracy on our test data.

```python
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
```

This should be about 92%.

Is that good? Well, not really. In fact, it's pretty bad. This is because we're
using a very simple model. With some small changes, we can get to 97%. The best
models can get to over 99.7% accuracy! (For more information, have a look at
this
[list of results](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html).)

What matters is that we learned from this model. Still, if you're feeling a bit
down about these results, check out
[the next tutorial](../../../tutorials/mnist/pros/index.md) where we do a lot
better, and learn how to build more sophisticated models using TensorFlow!
