Added sacred

ml_training.py

@@ -4,12 +4,33 @@ import tensorflow as tf
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import StandardScaler
 import sys
+import sacred
+from sacred.observers import FileStorageObserver
 
-def main():
+ex = sacred.Experiment("Training model")
+ex.observers.append(FileStorageObserver('training_experiment'))
+
+
+@ex.config
+def get_config():
     no_of_epochs = 10
     if len(sys.argv) == 2:
         no_of_epochs = int(sys.argv[1])
 
+
+@ex.capture
+def evaluate_model(model, test_x, test_y):
+    test_loss, test_acc, test_rec = model.evaluate(test_x, test_y, verbose=1)
+    # print("Accuracy:", test_acc)
+    # print("Loss:", test_loss)
+    # print("Recall:", test_rec)
+    return f"Accuracy: {test_acc}, Loss: {test_loss}, Recall: {test_rec}"
+
+
+@ex.main
+def main(no_of_epochs, _run):
+    # no_of_epochs = get_config()
+
     scaler = StandardScaler()
     feature_names = ["BMI", "SleepTime", "Sex", "Diabetic", "PhysicalActivity", "Smoking", "AlcoholDrinking"]
 
@@ -61,11 +82,10 @@ def main():
     model.fit(train_X, train_Y, epochs=no_of_epochs)
     model.save("trained_model")
 
-    test_loss, test_acc, test_rec = model.evaluate(test_X, test_Y, verbose=1)
+    metrics = evaluate_model(model, test_X, test_Y)
-    print("Accuracy:", test_acc)
+    _run.log_scalar("model.eval", metrics)
-    print("Loss:", test_loss)
+    ex.add_artifact("trained_model/saved_model.pb")
-    print("Recall:", test_rec)
+    ex.add_artifact("trained_model/keras_metadata.pb")
 
 
-if __name__ == '__main__':
+ex.run()
-    main()