performing linear regression using tensorflow 2.0 - tensorflow

Question

How to perform linear regression in Tensorflow 2.0? An example or a tutorial link will be appreciated. All the tutorials on YouTube are using tensorflow 1

Answer 1

Example 1: regression using Tensorflow 2.0.0 :
import tensorflow as tf
# tensorflow 2.0.0
class Model:
def __init__(self):
self.W = tf.Variable(7.0) # initial value for model parameter W
self.b = tf.Variable(0.0) #initial value for model bias b
def model(self, x):
return self.W * x + self.b
def loss(predicted_label, target_label):
return tf.reduce_mean(tf.square(predicted_label - target_label))
def train(self,inputs, outputs, learning_rate):
with tf.GradientTape() as t:
current_loss = Model.loss(self.model(inputs), outputs)
#backpropagation
dW, db = t.gradient(current_loss, [self.W, self.b])
self.W.assign_sub(learning_rate * dW)
self.b.assign_sub(learning_rate * db)
return current_loss
def run(self):
import matplotlib.pyplot as plt
# Generate train data when true W=2.0 and b=3.0
TRUE_W = 2.0
TRUE_b = 3.0
NUM_INSTANCES = 500 # number of tarin data
inputs = tf.random.normal(shape=[NUM_INSTANCES])
noise = tf.random.normal(shape=[NUM_INSTANCES])
outputs = inputs * TRUE_W + TRUE_b + noise
print("Model before train (red dots):")
plt.scatter(inputs, outputs, c='b')
plt.scatter(inputs, self.model(inputs), c='r')
plt.show()
epochs = range(50)
for epoch in epochs:
current_loss=self.train(inputs, outputs, learning_rate=0.1)
if epoch%10==0:
print('Epoch %2d: loss=%2.5f' %
(epoch, current_loss))
print("Model after train (red dots):")
plt.scatter(inputs, outputs, c='b')
plt.scatter(inputs, self.model(inputs), c='r')
plt.show()
ob=Model()
ob.run()
Example 2: regression using Tensorflow 2.0.0 and keras optimizer:
import tensorflow as tf
#Tensorflow 2.0.0
class Model:
def __init__(self):
self.W = tf.Variable(5.0)
self.b = tf.Variable(0.0)
def model(self):
return self.W * self.inputs + self.b
def loss(self):
return tf.reduce_mean(tf.square(self.model() - self.outputs))
def run(self):
import matplotlib.pyplot as plt
# Generate train data when true W=4.0 and b=1.0
TRUE_W = 2.0
TRUE_b = 3.0
NUM_INSTANCES = 500 # number of tarin data
print("Model befor train (red dots):")
self.inputs = tf.random.normal(shape=[NUM_INSTANCES])
noise = tf.random.normal(shape=[NUM_INSTANCES])
self.outputs = self.inputs * TRUE_W + TRUE_b + noise
plt.scatter(self.inputs, self.outputs, c='b')
plt.scatter(self.inputs, self.model(), c='r')
plt.show()
opt = tf.keras.optimizers.Adam(learning_rate=0.1)
epochs = range(50)
for epoch in epochs:
opt.minimize(self.loss, var_list=[self.W,self.b])
current_loss=self.loss()
if epoch%10==0:
print('Epoch %2d: loss=%2.5f' %
(epoch, current_loss))
print("Model after train (red dots):")
plt.scatter(self.inputs, self.outputs, c='b')
plt.scatter(self.inputs, self.model(), c='r')
plt.show()
ob=Model()
ob.run()
Hope this helps.

Answer 2

I have made an example according to this: https://www.geeksforgeeks.org/linear-regression-using-tensorflow/ just in TF2:
import numpy as np
import tensorflow as tf
#tf.enable_v2_behavior()
import matplotlib.pyplot as plt
np.random.seed(101)
tf.set_random_seed(101)
x = np.linspace(0, 50, 50)
y = np.linspace(0, 50, 50)
# Adding noise to the random linear data
x += np.random.uniform(-4, 4, 50)
y += np.random.uniform(-4, 4, 50)
n = len(x) # Number of data points
plt.scatter(x, y)
plt.xlabel('x')
plt.xlabel('y')
plt.title("Training Data")
plt.show()
x=tf.constant(x, dtype=tf.float32)
y=tf.constant(y, dtype=tf.float32)
W = tf.Variable(np.random.randn(), name = "W")
b = tf.Variable(np.random.randn(), name = "b")
learning_rate = 0.01
training_epochs = 1000
def y_pred(x):
y_pred = tf.add(tf.multiply(x, W), b)
return y_pred
# Mean Squared Error Cost Function
def cost():
cost = tf.reduce_sum(tf.pow(y_pred(x)-y, 2)) / (2 * n)
return cost
# Adam Optimizer
optimizer = tf.keras.optimizers.Adam(learning_rate)
pred=y_pred(x) #run to initialize weight and bias
trainable_vars=[W,b]
for epoch in range(training_epochs):
optimizer.minimize(cost, trainable_vars)
pred=y_pred(x)
if (epoch + 1) % 50 == 0:
c = cost()
print("Epoch", (epoch + 1), ": cost =", c.numpy(), "W =", W.numpy(), "b =", b.numpy())
plt.plot(x, y, 'ro', label ='Original data')
plt.plot(x, pred, label ='Fitted line')
plt.title('Linear Regression Result')
plt.legend()
plt.show()
Basically, there is no sessions and it is easier. :) SGD optimizer was working poorly, so I used Adam.