diff --git a/cifar100_network_trainer.py b/cifar100_network_trainer.py
index 140515e..9d86344 100644
--- a/cifar100_network_trainer.py
+++ b/cifar100_network_trainer.py
@@ -160,7 +160,7 @@ with tf.Session() as sess:
         total_test_accuracy = 0.
         # computer test loss and accuracy and save
         with tqdm.tqdm(total=total_test_batches) as pbar_test:
-            for batch_id, (x_batch, y_batch) in enumerate(test_data):
+            for batch_idx, (x_batch, y_batch) in enumerate(test_data):
                 c_loss_value, acc = sess.run(
                     [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
                     feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
diff --git a/cifar10_network_trainer.py b/cifar10_network_trainer.py
index 6d4d46a..54f10c1 100644
--- a/cifar10_network_trainer.py
+++ b/cifar10_network_trainer.py
@@ -160,7 +160,7 @@ with tf.Session() as sess:
         total_test_accuracy = 0.
         # computer test loss and accuracy and save
         with tqdm.tqdm(total=total_test_batches) as pbar_test:
-            for batch_id, (x_batch, y_batch) in enumerate(test_data):
+            for batch_idx, (x_batch, y_batch) in enumerate(test_data):
                 c_loss_value, acc = sess.run(
                     [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
                     feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
diff --git a/emnist_network_trainer.py b/emnist_network_trainer.py
index 9f72dad..c175470 100644
--- a/emnist_network_trainer.py
+++ b/emnist_network_trainer.py
@@ -160,7 +160,7 @@ with tf.Session() as sess:
         total_test_accuracy = 0.
         # computer test loss and accuracy and save
         with tqdm.tqdm(total=total_test_batches) as pbar_test:
-            for batch_id, (x_batch, y_batch) in enumerate(test_data):
+            for batch_idx, (x_batch, y_batch) in enumerate(test_data):
                 c_loss_value, acc = sess.run(
                     [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
                     feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
diff --git a/msd10_network_trainer.py b/msd10_network_trainer.py
index 13408e1..57afdae 100644
--- a/msd10_network_trainer.py
+++ b/msd10_network_trainer.py
@@ -161,7 +161,7 @@ with tf.Session() as sess:
         total_test_accuracy = 0.
         # computer test loss and accuracy and save
         with tqdm.tqdm(total=total_test_batches) as pbar_test:
-            for batch_id, (x_batch, y_batch) in enumerate(test_data):
+            for batch_idx, (x_batch, y_batch) in enumerate(test_data):
                 c_loss_value, acc = sess.run(
                     [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
                     feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,
diff --git a/msd25_network_trainer.py b/msd25_network_trainer.py
index 202c3e1..81247c6 100644
--- a/msd25_network_trainer.py
+++ b/msd25_network_trainer.py
@@ -160,7 +160,7 @@ with tf.Session() as sess:
         total_test_accuracy = 0.
         # computer test loss and accuracy and save
         with tqdm.tqdm(total=total_test_batches) as pbar_test:
-            for batch_id, (x_batch, y_batch) in enumerate(test_data):
+            for batch_idx, (x_batch, y_batch) in enumerate(test_data):
                 c_loss_value, acc = sess.run(
                     [losses_ops["crossentropy_losses"], losses_ops["accuracy"]],
                     feed_dict={dropout_rate: dropout_rate_value, data_inputs: x_batch,