Tensorflow: Simple Linear Regression Using CSV Data

I am an extreme beginner at tensorflow, and i was tasked to do a simple linear regression using my csv data which contains 2 columns, Height & State of Charge(SoC), where both

Solution 1:

The error is because your are trying to iterate over tensors in for (x, y) in zip(col2, col1) which is not allowed. The other issues with the code is that you have input pipeline queues setup and then your also trying to feed in through feed_dict{}, which is wrong. Your training part should look something like this:

with tf.Session() as sess:
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
sess.run(init)

# Fit all training data
for epoch in range(training_epochs):
    _, cost_value = sess.run([optimizer,cost])

        #Display logs per epoch step
    if (epoch+1) % display_step == 0:
        c = sess.run(cost)
        print( "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
            "W=", sess.run(W), "b=", sess.run(b))

    print("Optimization Finished!")
    training_cost = sess.run(cost)
    print ("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

#Plot data after completing training
train_X = []
train_Y = []
for i in range(input_size): #Your input data size to loop through once
    X, Y = sess.run([col1, pred]) # Call pred, to get the prediction with the updated weights
    train_X.append(X)
    train_Y.append(y)
    #Graphic display
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.legend()
plt.show()

coord.request_stop()
coord.join(threads)