implemneted gradient descent using tensorflow

Tensorflow/tf.py

@@ -88,7 +88,7 @@
 #%%
 X = tf.constant(housing_data_plus_bias, dtype=tf.float32,
     name="X")
-Y = tf.constant(housing.target.reshape(1,-1),dtype=tf.float32,
+Y = tf.constant(housing.target.reshape(-1,1),dtype=tf.float32,
     name='Y')
 Y.shape
 #%%
@@ -99,4 +99,24 @@
  XT), Y)
 with tf.Session() as sess:
     theta_value = theta.eval()
-    print(theta_value)
+    print(theta_value)
+
+#%%
+n_epochs = 1000
+learning_rate = 0.01
+X = tf.constant(scaled_housing_data_plus_bias, dtype=tf.float32, name="X")
+y = tf.constant(housing.target.reshape(-1, 1), dtype=tf.float32, name="y")
+theta = tf.Variable(tf.random_uniform([n + 1, 1], -1.0, 1.0), name="theta")
+y_pred = tf.matmul(X, theta, name="predictions")
+error = y_pred - y
+mse = tf.reduce_mean(tf.square(error), name="mse")
+gradients = 2/m * tf.matmul(tf.transpose(X), error)
+training_op = tf.assign(theta, theta - learning_rate * gradients)
+init = tf.global_variables_initializer()
+with tf.Session() as sess:
+    sess.run(init)
+for epoch in range(n_epochs):
+    if epoch % 100 == 0:
+        print("Epoch", epoch, "MSE =", mse.eval())
+sess.run(training_op)
+best_theta = theta.eval()