Updates to all parts of model building - moving to frozen transfer learning followed by slowed learning rate fine tuning using EfficientNets for final model.

2022-06-03 13:44:34 -04:00
parent 755fcde3a9
commit d111cdae69
13 changed files with 156 additions and 79 deletions
@@ -0,0 +1,23 @@
+import numpy as np
+from sklearn.metrics import accuracy_score, confusion_matrix, log_loss
+
+
+def get_metrics(gen, model, save_predictions_file=None):
+    model_output = model.predict(gen, verbose=True, workers=12)
+    prediction_indices = np.argmax(model_output, axis=1)
+    label_index = {v: k for k, v in gen.class_indices.items()}
+    predictions = [label_index[p] for p in prediction_indices]
+    reals = [label_index[p] for p in gen.classes]
+    acc = accuracy_score(reals, predictions)
+    ll = log_loss(gen.classes, model_output, labels=[l for l in label_index.keys()])
+    conf_mat = confusion_matrix(reals, predictions, labels=[l for l in label_index.values()])
+    # print(classification_report(reals, predictions, labels=[l for l in label_index.values()]))
+    print("Testing accuracy score is ", acc)
+    print("Confusion Matrix", conf_mat)
+    if save_predictions_file:
+        df = pd.DataFrame(columns=['fname', 'prediction', 'true_val'])
+        df['fname'] = [x for x in gen.filenames]
+        df['prediction'] = predictions
+        df["true_val"] = reals
+        df.to_csv(save_predictions_file, index=False)
+    return acc, ll