sync with remote

app.py

@@ -8,6 +8,8 @@ from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Flatten, Dense, Reshape
 from tensorflow.keras.losses import SparseCategoricalCrossentropy
 from io import StringIO
+import datetime
+
 
 # Constants for dataset information
 TRAIN_FILE = "train_images.tfrecords"
@@ -127,6 +129,12 @@ def show_predictions(dataset=None, num=1):
         prediction = create_mask(model.predict(sample_image[tf.newaxis, ...]))
         display([sample_image, sample_label, prediction])
 
+# define a callback that shows image predictions on the test set
+class DisplayCallback(tf.keras.callbacks.Callback):
+    def on_epoch_end(self, epoch, logs=None):
+        show_predictions()
+        st.write('\nSample Prediction after epoch {}\n'.format(epoch+1))
+
 # Streamlit app interface
 st.title("Cardiac Images Dataset")
 
@@ -170,4 +178,20 @@ st.title("Model Architecture")
 st.image(plot_filename, caption="Neural Network Architecture", use_container_width=True)
 
 # show a predection, as an example
-show_predictions(test_dataset)
+show_predictions(test_dataset)
+
+# setup a tensorboard callback
+logdir = os.path.join("logs", datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
+tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)
+
+if st.button("Run Model"):
+    # setup and run the model
+    EPOCHS = 20
+    STEPS_PER_EPOCH = len(list(parsed_training_dataset))
+    VALIDATION_STEPS = 26
+
+    model_history = model.fit(train_dataset, epochs=EPOCHS,
+                            steps_per_epoch=STEPS_PER_EPOCH,
+                            validation_steps=VALIDATION_STEPS,
+                            validation_data=test_dataset,
+                            callbacks=[DisplayCallback(), tensorboard_callback])    

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