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import tensorflow as tf

import matplotlib.pyplot as plt

import pandas as pd

import pylab as pl

import numpy as np

import tensorflow as tf

import matplotlib.patches as mpatches

import matplotlib.pyplot as plt

plt.rcParams['figure.figsize'] = (20, 6)



df1 = pd.read_csv("TrainData.csv")

df2 = pd.read_csv("TestData.csv")





train_data_X = np.asanyarray(df1['ENGINE SIZE'])

train_data_Y = np.asanyarray(df1['CO2 EMISSIONS'])

test_data_X = np.asanyarray(df2['ENGINE SIZE'])

test_data_Y = np.asanyarray(df2['CO2 EMISSIONS'])



W = tf.Variable(20.0, name= 'Weight')

b = tf.Variable(30.0, name= 'Bias')

X = tf.placeholder(tf.float32, name= 'Input')

Y = tf.placeholder(tf.float32, name= 'Output')



Y = W*X + b



loss = tf.reduce_mean(tf.square(Y - train_data_Y))

optimizer = tf.train.GradientDescentOptimizer(0.05)

train = optimizer.minimize(loss)



init = tf.global_variables_initializer()

sess = tf.Session()

sess.run(init)



loss_values = 

train_data = 

for step in range(100):

    _, loss_val, a_val, b_val = sess.run([train, loss, W, b], feed_dict={X:train_data_X, Y:train_data_Y})

    loss_values.append(loss_val)

    if step % 5 == 0:

      print(step, loss_val, a_val, b_val)

      train_data.append([a_val, b_val])



plt.plot(loss_values, 'ro')

plt.show()

I am trying to make a linear regression model to detect CO2 emission by giving size of engine as input. I am using the above code in tensorflow.
1) When I use this code Weight and Bias remains unchanged. What is the problem in code?
2) Also if I want engine size and milage both as inputs. what code changes should be made

Thanks in advance

There were few mistakes in the code which are mentioned below :

• You were using placeholder Y = W*X + b, which in later section of the code was used to feed data (feed_dict={X:train_data_X, Y:train_data_Y}). You should have used another variable for prediction (not the placeholder which you were using to feed data) and then you should have been able to calculate loss function. However, required changes have been made. Check prediction= W*X + b in the below code


• You were passing complete data in feed_dict at once (feed_dict={X:train_data_X, Y:train_data_Y}). However, you need to pass single data value at a time (feed_dict={X:x, Y:y})

Below code with the needful correction should be working fine.

import tensorflow as tf

import matplotlib.pyplot as plt

import pandas as pd

import pylab as pl

import numpy as np

import tensorflow as tf

import matplotlib.patches as mpatches

import matplotlib.pyplot as plt

plt.rcParams['figure.figsize'] = (20, 6)



df1 = pd.read_csv("TrainData.csv")

df2 = pd.read_csv("TestData.csv")





train_data_X = np.asanyarray(df1['ENGINE SIZE'])

train_data_Y = np.asanyarray(df1['CO2 EMISSIONS'])

test_data_X = np.asanyarray(df2['ENGINE SIZE'])

test_data_Y = np.asanyarray(df2['CO2 EMISSIONS'])



W = tf.Variable(20.0, name= 'Weight')

b = tf.Variable(30.0, name= 'Bias')

X = tf.placeholder(tf.float32, name= 'Input')

Y = tf.placeholder(tf.float32, name= 'Output')

prediction= W*X + b





loss = tf.reduce_mean(tf.square(prediction - Y))

optimizer = tf.train.GradientDescentOptimizer(0.05)

train = optimizer.minimize(loss)

loss_values = 

train_data = 

init = tf.global_variables_initializer()

with tf.Session() as sess:

    sess.run(init)

    for step in range(100):

        for (x,y) in zip(train_data_X,train_data_Y):

            _, loss_val, a_val, b_val = sess.run([train, loss, W, b], feed_dict={X:x, Y:y})

            loss_values.append(loss_val)

        if step % 5 == 0:

            print(step, loss_val, a_val, b_val)

            train_data.append([a_val, b_val])



plt.plot(loss_values, 'ro')

plt.show()

Note : Because of incorrect choice of loss function, your loss keeps on increasing with every step.

I have mentioned a loss function below, which might work for your data. I am not sure how your data looks like, but you can give this a try if you want and let me know if this worked.

n_samples = train_data_X.shape[0]

loss = tf.reduce_sum(tf.pow(prediction - Y, 2)) / (2 * n_samples)

Response to your second query.

Assuming that your data has column name as MILEAGE, You can perform the below changes in train_data_X and test_data_X. Rest of the code will remain the same as above.

train_data_X = np.asanyarray(df1[['ENGINE SIZE','MILEAGE']])

train_data_Y = np.asanyarray(df1['CO2 EMISSIONS'])

test_data_X = np.asanyarray(df2[['ENGINE SIZE','MILEAGE']])

test_data_Y = np.asanyarray(df2['CO2 EMISSIONS'])