Custom_EfficientNetModel_Quantization.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyMBw8v4SQpZKx4ZoRVfarUb"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mYa8ErN8fHit","executionInfo":{"status":"ok","timestamp":1705681329993,"user_tz":-60,"elapsed":82171,"user":{"displayName":"Yomyom Cawom","userId":"04427174372377591962"}},"outputId":"ee77e537-c03f-452a-dfac-5cedb1f51318"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["import tensorflow as tf\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","from tensorflow.keras.applications.convnext import ConvNeXtBase\n","from tensorflow.keras.layers import GlobalAveragePooling2D, Dense\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import ModelCheckpoint\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import confusion_matrix, classification_report\n","import matplotlib.pyplot as plt\n","import pickle\n","import seaborn as sns\n","import time\n","from google.colab import drive\n","\n","# Mount Google Drive\n","drive.mount(\"/content/drive\", force_remount=True)\n","\n","# Define the path to the saved model\n","saved_model_path = r'/content/drive/MyDrive/R&D Project/Models/Customized_EfficientNetV2_Model_Unfrozen.tf'\n","\n","# Load the saved model\n","model = tf.keras.models.load_model(saved_model_path)"]},{"cell_type":"code","source":["# Convert the model to a quantized version\n","converter = tf.lite.TFLiteConverter.from_keras_model(model)\n","converter.optimizations = [tf.lite.Optimize.DEFAULT]\n","\n","quantized_model = converter.convert()\n","\n","# Save the quantized model\n","quantized_model_path = r'/content/drive/MyDrive/R&D Project/Models/Quantized_Customized_EfficientNetV2_Model.tflite'\n","with open(quantized_model_path, 'wb') as f:\n","    f.write(quantized_model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bmiQ6_uhgL-_","executionInfo":{"status":"ok","timestamp":1705681490121,"user_tz":-60,"elapsed":160133,"user":{"displayName":"Yomyom Cawom","userId":"04427174372377591962"}},"outputId":"8ce2d8e0-0321-4200-9835-7f4b9f504c1a"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:absl:`efficientnetv2-m_input` is not a valid tf.function parameter name. Sanitizing to `efficientnetv2_m_input`.\n","WARNING:absl:`efficientnetv2-m_input` is not a valid tf.function parameter name. Sanitizing to `efficientnetv2_m_input`.\n","WARNING:absl:`efficientnetv2-m_input` is not a valid tf.function parameter name. Sanitizing to `efficientnetv2_m_input`.\n"]}]}]}