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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 Data Set - Basic Approach
Get the MNIST Data
Step1:  Alternative sources of the data just in case
Step2:  Visualizing the Data
Step3:  Create the Model
Step4:  Loss and Optimizer
Step5:  Create Session
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Python Code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
Explanation: MNIST Data Set - Basic Approach
Get the MNIST Data
End of explanation
type(mnist)
mnist.train.images
mnist.train.num_examples
mnist.test.num_examples
mnist.validation.num_examples
Explanation: Alternative sources of the data just in case: 
http://yann.lecun.com/exdb/mnist/
https://github.com/mrgloom/MNIST-dataset-in-different-formats
End of explanation
import matplotlib.pyplot as plt
%matplotlib inline
mnist.train.images[1].shape
plt.imshow(mnist.train.images[1].reshape(28,28))
plt.imshow(mnist.train.images[1].reshape(28,28),cmap='gist_gray')
mnist.train.images[1].max()
plt.imshow(mnist.train.images[1].reshape(784,1))
plt.imshow(mnist.train.images[1].reshape(784,1),cmap='gist_gray',aspect=0.02)
Explanation: Visualizing the Data
End of explanation
x = tf.placeholder(tf.float32,shape=[None,784])
# 10 because 0-9 possible numbers
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
# Create the Graph
y = tf.matmul(x,W) + b 
Explanation: Create the Model
End of explanation
y_true = tf.placeholder(tf.float32,[None,10])
# Cross Entropy
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_true, logits=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5)
train = optimizer.minimize(cross_entropy)
Explanation: Loss and Optimizer
End of explanation
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    
    # Train the model for 1000 steps on the training set
    # Using built in batch feeder from mnist for convenience
    
    for step in range(1000):
        
        batch_x , batch_y = mnist.train.next_batch(100)
        
        sess.run(train,feed_dict={x:batch_x,y_true:batch_y})
        
    # Test the Train Model
    matches = tf.equal(tf.argmax(y,1),tf.argmax(y_true,1))
    
    acc = tf.reduce_mean(tf.cast(matches,tf.float32))
    
    print(sess.run(acc,feed_dict={x:mnist.test.images,y_true:mnist.test.labels}))
Explanation: Create Session
End of explanation
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