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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 MNIST ML Beginner tutorial
This notebook is based on the tutorial found here
Get the MNIST dataset
Step1:  Softmax regression
There is an excellent explanation of the softmax regression here, however we are essentially building the following
Step2:  Training
Step3:  Validation
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Python Code:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("data/MNIST",one_hot=True)
Explanation: MNIST ML Beginner tutorial
This notebook is based on the tutorial found here
Get the MNIST dataset
End of explanation
import tensorflow as tf
# Setup our Input placeholder
x = tf.placeholder(tf.float32, [None, 784])
# Our Weights and Biases
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x,W) + b
# Define loss and optimizer
y_ = tf.placeholder(tf.float32,[None,10])
# The following is considered numerically unstable, using the line above instead
# cross_entropy = tf.reduce_mean(-(tf.reduce_sum(y_ * tf.log(y),reduction_indices=[1])))
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
Explanation: Softmax regression
There is an excellent explanation of the softmax regression here, however we are essentially building the following: <img src="https://www.tensorflow.org/images/softmax-regression-scalargraph.png"
width="300"/>
Which in matrix form looks like:
<img src="https://www.tensorflow.org/images/softmax-regression-vectorequation.png" width="300"/>
$$y = \text{softmax}(Wx + b)$$
Credit the images from the Tensorflow docs
End of explanation
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
# We train our model with batches of random data
for _ in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step,feed_dict={x:batch_xs,y_:batch_ys})
Explanation: Training
End of explanation
# We validate our model by casting our boolean (right/wrong) into integers and taking the mean
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_:mnist.test.labels}))
Explanation: Validation
End of explanation
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