gistfile1.txt

with tf.Session() as sess:
    # Set up all the tensors, variables, and operations.
    input = tf.constant(x_with_bias)
    target = tf.constant(np.transpose([y]).astype(np.float32))
    weights = tf.Variable(tf.random_normal([2, 1], 0, 0.1))

    tf.initialize_all_variables().run()

    yhat = tf.matmul(input, weights)
    yerror = tf.sub(yhat, target)
    loss = tf.nn.l2_loss(yerror)
  
    update_weights = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
  
    for _ in range(training_steps):
        # Repeatedly run the operations, updating the TensorFlow variable.
        update_weights.run()
        losses.append(loss.eval())

    # Training is done, get the final values for the graphs
    betas = weights.eval()
    yhat = yhat.eval()