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feat: adding resnet and formatting updates

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This commit is contained in:
Lucas Oskorep
2023-04-06 00:37:46 -04:00
parent ce5939d8a9
commit dc427837f6
12 changed files with 87 additions and 37 deletions
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6_convert_keras_to_tflite.py
+13 -10
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@@ -5,7 +5,7 @@ from pathlib import Path
import pandas as pd
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from tensorflow import keras
import tensorflow as tf
# TODO: Move these to a config for the project
input_shape = (224, 224, 3)
@@ -25,16 +25,19 @@ 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)
print(tflite_file)
keras_model = tf.keras.models.load_model(file)
keras_model.summary()
print(keras_model.input)
print(keras_model.layers)
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
tflite_model = converter.convert()
with open(tflite_file, 'wb') as f:
f.write(tflite_model)
# TODO: Verify the model performance after converting to TFLITE
# 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)
# TODO: Verify the model performance after converting to TFLITE
# 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)