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

6_convert_keras_to_tflite.py

@@ -0,0 +1,41 @@
+import os
+from glob import glob
+from pathlib import Path
+
+import pandas as pd
+import tensorflow as tf
+from keras.preprocessing.image import ImageDataGenerator
+from tensorflow import keras
+
+from modeling_utils import get_metrics
+
+# TODO: Move these to a config for the project
+input_shape = (224, 224, 3)
+batch_size = 32
+
+single_gen = ImageDataGenerator().flow_from_directory(
+    './single_image_test_set',
+    target_size=(input_shape[0], input_shape[1]),
+    batch_size=batch_size,
+    shuffle=False
+)
+
+pd.DataFrame(sorted([f.name for f in os.scandir("./data/train") if f.is_dir()])).to_csv("./models/tflite/labels.txt",
+                                                                                        index=False, header=False)
+
+for file in glob("./models/keras/*.hdf5"):
+    path = Path(file)
+    tflite_file = f'./models/tflite/models/{path.name[:-5] + ".tflite"}'
+    if not Path(tflite_file).exists():
+        keras_model = keras.models.load_model(file)
+
+        converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
+        tflite_model = converter.convert()
+        with open(tflite_file, 'wb') as f:
+            f.write(tflite_model)
+        # interpreter = tf.lite.Interpreter(model_path=tflite_file)
+        # single_acc, single_ll = get_metrics(single_gen, keras_model)
+        # tf_single_acc, tf_single_ll = get_metrics(single_gen, tflite_model)
+        #
+        # print(single_acc, tf_single_acc)
+        # print(single_ll, tf_single_ll)