Let us build a single-layer neural net with TensorFlow step by step. In this example, we'll be using the MNIST dataset. This dataset is a set of 28 x 28 pixel grayscale images of hand written digits. This dataset consists of 55,000 training data, 10,000 test data, and 5,000 validation data. Every MNIST data point has two parts: an image of a handwritten digit and a corresponding label. The following code block loads data. one_hot=True means that the labels are one-hot encoded vectors instead of actual digits of the label. For example, if the label is 2, you will see [0,0,1,0,0,0,0,0,0,0]. This allows us to directly use it in the output layer of the network:

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

Setting up placeholders and variables is done as follows:

# All the pixels in the image (28 * 28 = 784)
features_count = 784
# there are 10 digits i.e labels
labels_count = 10
batch_size = 128
epochs = 10
learning_rate = 0.5

features = tf.placeholder(tf.float32, [None,features_count])
labels = tf.placeholder(tf.float32, [None, labels_count])

#Set the weights and biases tensors
weights = tf.Variable(tf.truncated_normal((features_count, labels_count)))
biases = tf.Variable(tf.zeros(labels_count),name='biases')

Let's set up the optimizer in TensorFlow:

loss,
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)    

Before we begin training, let's set up the variable initialization operation and an operation to measure the accuracy of our predictions, as follows:

# Linear Function WX + b
logits = tf.add(tf.matmul(features, weights),biases)

prediction = tf.nn.softmax(logits)

# Cross entropy
cross_entropy = -tf.reduce_sum(labels * tf.log(prediction), reduction_indices=1)

# Training loss
loss = tf.reduce_mean(cross_entropy)

# Initializing all variables
init = tf.global_variables_initializer()

# Determining if the predictions are accurate
is_correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(labels, 1))
# Calculating prediction accuracy
accuracy = tf.reduce_mean(tf.cast(is_correct_prediction, tf.float32))

Now we can begin training the model, as shown in the following code snippet:

#Beginning the session
with tf.Session() as sess:
   # initializing all the variables
   sess.run(init)
   total_batch = int(len(mnist.train.labels) / batch_size)
   for epoch in range(epochs):
        avg_cost = 0
        for i in range(total_batch):
            batch_x, batch_y = mnist.train.next_batch(batch_size=batch_size)
            _, c = sess.run([optimizer,loss], feed_dict={features: batch_x, labels: batch_y})
            avg_cost += c / total_batch
        print("Epoch:", (epoch + 1), "cost =", "{:.3f}".format(avg_cost))
   print(sess.run(accuracy, feed_dict={features: mnist.test.images, labels: mnist.test.labels}))