diff --git a/.gitignore b/.gitignore
index c2eade0..60a0348 100644
--- a/.gitignore
+++ b/.gitignore
@@ -145,4 +145,7 @@ _build/
 dataset
 
 # Model Artifacts (HDF5)
-*.h5
\ No newline at end of file
+*.h5
+
+# TensorBoard
+logs/
\ No newline at end of file
diff --git a/README.md b/README.md
index d85f3a1..af13ac6 100644
--- a/README.md
+++ b/README.md
@@ -19,11 +19,10 @@ from covid_xray_classification.models.xception import Small
 from covid_xray_classification.data import Downloader, Reshaper
 from pandas import read_csv
 from os.path import join
-from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard
 from tensorflow.keras.optimizers import Adam
 from tensorflow.keras.preprocessing import image_dataset_from_directory
 from tensorflow.keras.metrics import BinaryAccuracy,Precision,Recall
-from keras.callbacks import ModelCheckpoint
 
 
 # Download default dataset to default location
@@ -75,7 +74,10 @@ callbacks = [EarlyStopping("loss",
                            mode='min',
                            restore_best_weights=True),
              # Save the model at the end of each epoch.
-             ModelCheckpoint("epoch_{epoch}.h5")]
+             ModelCheckpoint("epoch_{epoch}.h5"),
+             # Log data to directory logs for TensorBoard
+             TensorBoard(
+                 log_dir="logs")]
 
 # Specify the metrics we wish to evaluate at the end of each epoch.
 metrics = ["accuracy",
@@ -127,6 +129,7 @@ model.fit(
     callbacks=callbacks,
     validation_data=val_ds)
 
+# Save our model for later use.
 model.save(join('dataset',
                 f"{model_name}.h5"))
 ```
\ No newline at end of file
diff --git a/example.py b/example.py
index c313542..aabaf50 100644
--- a/example.py
+++ b/example.py
@@ -3,11 +3,10 @@
 from covid_xray_classification.data import Downloader, Reshaper
 from pandas import read_csv
 from os.path import join
-from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard
 from tensorflow.keras.optimizers import Adam
 from tensorflow.keras.preprocessing import image_dataset_from_directory
 from tensorflow.keras.metrics import BinaryAccuracy,Precision,Recall
-from keras.callbacks import ModelCheckpoint
 
 
 # Download default dataset to default location
@@ -59,7 +58,10 @@
                            mode='min',
                            restore_best_weights=True),
              # Save the model at the end of each epoch.
-             ModelCheckpoint("epoch_{epoch}.h5")]
+             ModelCheckpoint("epoch_{epoch}.h5"),
+             # Log data to directory logs for TensorBoard
+             TensorBoard(
+                 log_dir="logs")]
 
 # Specify the metrics we wish to evaluate at the end of each epoch.
 metrics = ["accuracy",