📊Add support for TensorBoard in the example

.gitignore

@@ -145,4 +145,7 @@ _build/
 dataset
 
 # Model Artifacts (HDF5)
-*.h5
+*.h5
+
+# TensorBoard
+logs/

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"))
 ```

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",