implemented tensorboard

Tensorflow/tf.py

@@ -246,4 +246,60 @@ def fetch_batch(epoch, batch_index, batch_size):
     best_theta_restored = theta.eval() 
 print(best_theta_restored)
 #%%
-np.allclose(best_theta, best_theta_restored)
+np.allclose(best_theta, best_theta_restored)
+
+#%%
+''' implementing tensorboard'''
+reset_graph()
+
+from datetime import datetime
+now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
+root_logdir = "tf.logs"
+logdir = "{}/run-{}/".format(root_logdir, now)
+
+#%%
+n_epochs = 1000
+learning_rate = 0.01
+X = tf.placeholder(tf.float32,shape=(None,n+1),name="X")
+Y = tf.placeholder(tf.float32,shape=(None,1),name="Y")
+theta = tf.Variable(tf.random_uniform([n+1,1],
+-1.0,1.0,seed=42),name="theta")
+Y_pred = tf.matmul(X,theta,name="predictions")
+error = Y_pred - Y
+mse = tf.reduce_mean(tf.square(error), name="mse")
+optimizer = tf.train.GradientDescentOptimizer(learning_rate=
+learning_rate)
+training_op = optimizer.minimize(mse)
+
+init = tf.global_variables_initializer()
+
+#%%
+mse_summary = tf.summary.scalar("MSE",mse)
+file_writer = tf.summary.FileWriter(logdir,
+tf.get_default_graph())
+
+#%%
+n_epochs = 10
+batch_size = 100
+n_batches = int(np.ceil(m / batch_size))
+
+#%%
+with tf.Session() as sess:
+    sess.run(init)
+    for epoch in range(n_epochs):
+        for batch_index in range(n_batches):
+            X_batch,Y_batch = fetch_batch(epoch,batch_index
+            ,batch_size)
+            if batch_index % 10 == 0:
+                summary_str = mse_summary.eval(feed_dict=
+                {X: X_batch, Y: Y_batch})
+                step = epoch * n_batches + batch_index
+                file_writer.add_summary(summary_str, step)
+            sess.run(training_op, feed_dict={X: X_batch, 
+            Y: Y_batch})
+
+    best_theta = theta.eval()
+
+#%%
+file_writer.close()
+print(best_theta)