451 lines
23 KiB
Plaintext
451 lines
23 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "RaaVleVhamV5"
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},
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"outputs": [],
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"source": [
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"from IPython.display import Image, SVG, display\n",
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"from tqdm import tqdm\n",
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"import matplotlib.pyplot as plt\n",
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"import sys\n",
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"import subprocess\n",
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"import pkg_resources\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import os\n",
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"from skimage.io import imread\n",
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"import cv2 as cv\n",
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"from pathlib import Path\n",
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"import random\n",
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"from shutil import copyfile, rmtree\n",
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"import json\n",
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"import seaborn as sns\n",
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"import matplotlib\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"import tensorflow as tf\n",
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"from tensorflow import keras\n",
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"import os\n",
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"import time"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from training.data_load import load_data\n",
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"\n",
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"train_ds, test_ds, validation_ds = load_data((227, 227), './new_data_transformed/train', './new_data_transformed/test')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "se5yACYzZcmm"
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},
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"outputs": [],
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"source": [
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"def tensorboard_callback(model_name):\n",
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" return keras.callbacks.TensorBoard(os.path.join(f\"./logs/{model_name}\", time.strftime(\"run_%Y_%m_%d-%H_%M_%S\")))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "yvRRfMKbcTwu"
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},
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"outputs": [],
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"source": [
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"model = keras.models.Sequential([\n",
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" keras.layers.Conv2D(filters=96, kernel_size=(11,11), strides=(4,4), activation='relu', input_shape=(227,227,3)),\n",
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" keras.layers.BatchNormalization(),\n",
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" keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),\n",
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" keras.layers.Conv2D(filters=256, kernel_size=(5,5), strides=(1,1), activation='relu', padding=\"same\"),\n",
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" keras.layers.BatchNormalization(),\n",
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" keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),\n",
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" keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding=\"same\"),\n",
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" keras.layers.BatchNormalization(),\n",
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" keras.layers.Conv2D(filters=384, kernel_size=(3,3), strides=(1,1), activation='relu', padding=\"same\"),\n",
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" keras.layers.BatchNormalization(),\n",
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" keras.layers.Conv2D(filters=256, kernel_size=(3,3), strides=(1,1), activation='relu', padding=\"same\"),\n",
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" keras.layers.BatchNormalization(),\n",
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" keras.layers.MaxPool2D(pool_size=(3,3), strides=(2,2)),\n",
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" keras.layers.Flatten(),\n",
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" keras.layers.Dense(4096, activation='relu'),\n",
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" keras.layers.Dense(4096, activation='relu'),\n",
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" keras.layers.Dense(6, activation='softmax')\n",
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"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "eRI-u6HLcU_H",
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"outputId": "7ea0a139-c9ab-4092-e082-bea02eca4e93"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model: \"sequential\"\n",
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"_________________________________________________________________\n",
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" Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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" conv2d (Conv2D) (None, 55, 55, 96) 34944 \n",
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" \n",
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" batch_normalization (BatchN (None, 55, 55, 96) 384 \n",
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" ormalization) \n",
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" \n",
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" max_pooling2d (MaxPooling2D (None, 27, 27, 96) 0 \n",
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" ) \n",
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" \n",
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" conv2d_1 (Conv2D) (None, 27, 27, 256) 614656 \n",
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" \n",
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" batch_normalization_1 (Batc (None, 27, 27, 256) 1024 \n",
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" hNormalization) \n",
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" \n",
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" max_pooling2d_1 (MaxPooling (None, 13, 13, 256) 0 \n",
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" 2D) \n",
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" \n",
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" conv2d_2 (Conv2D) (None, 13, 13, 384) 885120 \n",
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" \n",
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" batch_normalization_2 (Batc (None, 13, 13, 384) 1536 \n",
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" hNormalization) \n",
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" \n",
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" conv2d_3 (Conv2D) (None, 13, 13, 384) 1327488 \n",
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" \n",
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" batch_normalization_3 (Batc (None, 13, 13, 384) 1536 \n",
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" hNormalization) \n",
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" \n",
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" conv2d_4 (Conv2D) (None, 13, 13, 256) 884992 \n",
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" \n",
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" batch_normalization_4 (Batc (None, 13, 13, 256) 1024 \n",
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" hNormalization) \n",
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" \n",
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" max_pooling2d_2 (MaxPooling (None, 6, 6, 256) 0 \n",
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" 2D) \n",
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" \n",
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" flatten (Flatten) (None, 9216) 0 \n",
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" \n",
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" dense (Dense) (None, 4096) 37752832 \n",
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" \n",
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" dense_1 (Dense) (None, 4096) 16781312 \n",
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" \n",
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" dense_2 (Dense) (None, 6) 24582 \n",
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" \n",
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"=================================================================\n",
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"Total params: 58,311,430\n",
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"Trainable params: 58,308,678\n",
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"Non-trainable params: 2,752\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"model.compile(loss='sparse_categorical_crossentropy', optimizer=tf.optimizers.SGD(learning_rate=.001), metrics=['accuracy'])\n",
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"model.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "Jxzfxvy3cWBP",
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"outputId": "fa3b738e-a125-4b19-984a-4b1d83df771f"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/50\n",
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"45/45 [==============================] - 13s 82ms/step - loss: 1.5723 - accuracy: 0.4535 - val_loss: 1.8094 - val_accuracy: 0.1676\n",
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"Epoch 2/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.8888 - accuracy: 0.6771 - val_loss: 1.8356 - val_accuracy: 0.1733\n",
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"Epoch 3/50\n",
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"45/45 [==============================] - 3s 62ms/step - loss: 0.7100 - accuracy: 0.7292 - val_loss: 1.8753 - val_accuracy: 0.1534\n",
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"Epoch 4/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.5200 - accuracy: 0.8222 - val_loss: 1.9177 - val_accuracy: 0.1477\n",
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"Epoch 5/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.3887 - accuracy: 0.8868 - val_loss: 2.0252 - val_accuracy: 0.1591\n",
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"Epoch 6/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.2841 - accuracy: 0.9340 - val_loss: 2.0583 - val_accuracy: 0.2273\n",
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"Epoch 7/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.2114 - accuracy: 0.9569 - val_loss: 2.1366 - val_accuracy: 0.2216\n",
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"Epoch 8/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.1738 - accuracy: 0.9764 - val_loss: 2.1499 - val_accuracy: 0.3153\n",
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"Epoch 9/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.1298 - accuracy: 0.9854 - val_loss: 2.0241 - val_accuracy: 0.3693\n",
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"Epoch 10/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.1152 - accuracy: 0.9896 - val_loss: 1.5216 - val_accuracy: 0.4631\n",
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"Epoch 11/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.0893 - accuracy: 0.9958 - val_loss: 1.2411 - val_accuracy: 0.5511\n",
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"Epoch 12/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0922 - accuracy: 0.9910 - val_loss: 0.9918 - val_accuracy: 0.6477\n",
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"Epoch 13/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0665 - accuracy: 0.9972 - val_loss: 0.9535 - val_accuracy: 0.6648\n",
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"Epoch 14/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.0628 - accuracy: 0.9965 - val_loss: 0.7136 - val_accuracy: 0.7415\n",
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"Epoch 15/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.0624 - accuracy: 0.9972 - val_loss: 0.6692 - val_accuracy: 0.7642\n",
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"Epoch 16/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.0534 - accuracy: 0.9979 - val_loss: 0.6320 - val_accuracy: 0.7898\n",
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"Epoch 17/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.0436 - accuracy: 0.9993 - val_loss: 0.6613 - val_accuracy: 0.7841\n",
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"Epoch 18/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.0401 - accuracy: 0.9972 - val_loss: 0.6103 - val_accuracy: 0.7869\n",
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"Epoch 19/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0314 - accuracy: 0.9993 - val_loss: 0.6222 - val_accuracy: 0.8040\n",
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"Epoch 20/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0308 - accuracy: 0.9986 - val_loss: 0.6182 - val_accuracy: 0.7841\n",
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"Epoch 21/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.0309 - accuracy: 0.9993 - val_loss: 0.6246 - val_accuracy: 0.7926\n",
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"Epoch 22/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0256 - accuracy: 1.0000 - val_loss: 0.6276 - val_accuracy: 0.7983\n",
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"Epoch 23/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0259 - accuracy: 1.0000 - val_loss: 0.6332 - val_accuracy: 0.8011\n",
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"Epoch 24/50\n",
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"45/45 [==============================] - 3s 62ms/step - loss: 0.0223 - accuracy: 1.0000 - val_loss: 0.6229 - val_accuracy: 0.7955\n",
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"Epoch 25/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0225 - accuracy: 1.0000 - val_loss: 0.6089 - val_accuracy: 0.8011\n",
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"Epoch 26/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0217 - accuracy: 1.0000 - val_loss: 0.6463 - val_accuracy: 0.7926\n",
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"Epoch 27/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.0221 - accuracy: 1.0000 - val_loss: 0.6237 - val_accuracy: 0.8068\n",
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"Epoch 28/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0196 - accuracy: 1.0000 - val_loss: 0.6484 - val_accuracy: 0.7898\n",
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"Epoch 29/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0204 - accuracy: 0.9993 - val_loss: 0.6200 - val_accuracy: 0.7926\n",
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"Epoch 30/50\n",
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"45/45 [==============================] - 3s 60ms/step - loss: 0.0194 - accuracy: 0.9993 - val_loss: 0.6186 - val_accuracy: 0.8040\n",
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"Epoch 31/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0171 - accuracy: 1.0000 - val_loss: 0.6418 - val_accuracy: 0.8068\n",
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"Epoch 32/50\n",
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"45/45 [==============================] - 3s 62ms/step - loss: 0.0172 - accuracy: 1.0000 - val_loss: 0.6234 - val_accuracy: 0.8011\n",
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"Epoch 33/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0168 - accuracy: 1.0000 - val_loss: 0.6278 - val_accuracy: 0.8068\n",
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"Epoch 34/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0153 - accuracy: 1.0000 - val_loss: 0.6527 - val_accuracy: 0.7898\n",
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"Epoch 35/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0138 - accuracy: 1.0000 - val_loss: 0.6198 - val_accuracy: 0.7926\n",
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"Epoch 36/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.6359 - val_accuracy: 0.7869\n",
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"Epoch 37/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0130 - accuracy: 1.0000 - val_loss: 0.6123 - val_accuracy: 0.8153\n",
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"Epoch 38/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0116 - accuracy: 1.0000 - val_loss: 0.6061 - val_accuracy: 0.8040\n",
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"Epoch 39/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.6256 - val_accuracy: 0.8011\n",
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"Epoch 40/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0142 - accuracy: 0.9993 - val_loss: 0.6386 - val_accuracy: 0.8011\n",
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"Epoch 41/50\n",
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"45/45 [==============================] - 3s 62ms/step - loss: 0.0119 - accuracy: 1.0000 - val_loss: 0.6137 - val_accuracy: 0.8040\n",
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"Epoch 42/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0100 - accuracy: 1.0000 - val_loss: 0.6392 - val_accuracy: 0.8068\n",
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"Epoch 43/50\n",
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"45/45 [==============================] - 3s 62ms/step - loss: 0.0098 - accuracy: 1.0000 - val_loss: 0.6461 - val_accuracy: 0.8097\n",
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"Epoch 44/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0113 - accuracy: 1.0000 - val_loss: 0.6131 - val_accuracy: 0.8125\n",
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"Epoch 45/50\n",
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"45/45 [==============================] - 3s 61ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.6376 - val_accuracy: 0.8125\n",
|
||
|
|
"Epoch 46/50\n",
|
||
|
|
"45/45 [==============================] - 3s 61ms/step - loss: 0.0117 - accuracy: 0.9986 - val_loss: 0.6414 - val_accuracy: 0.7841\n",
|
||
|
|
"Epoch 47/50\n",
|
||
|
|
"45/45 [==============================] - 3s 61ms/step - loss: 0.0094 - accuracy: 1.0000 - val_loss: 0.6224 - val_accuracy: 0.8068\n",
|
||
|
|
"Epoch 48/50\n",
|
||
|
|
"45/45 [==============================] - 3s 61ms/step - loss: 0.0095 - accuracy: 1.0000 - val_loss: 0.5973 - val_accuracy: 0.8153\n",
|
||
|
|
"Epoch 49/50\n",
|
||
|
|
"45/45 [==============================] - 3s 62ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.6366 - val_accuracy: 0.8068\n",
|
||
|
|
"Epoch 50/50\n",
|
||
|
|
"45/45 [==============================] - 3s 61ms/step - loss: 0.0093 - accuracy: 1.0000 - val_loss: 0.6526 - val_accuracy: 0.8040\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<keras.callbacks.History at 0x7efd9022ee80>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model.fit(train_ds,\n",
|
||
|
|
" epochs=50,\n",
|
||
|
|
" validation_data=validation_ds,\n",
|
||
|
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" validation_freq=1,\n",
|
||
|
|
" callbacks=[tensorboard_callback(\"AlexNet\")])"
|
||
|
|
]
|
||
|
|
},
|
||
|
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{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"id": "hrbrA2t3Zv4d",
|
||
|
|
"outputId": "6874f3db-eacb-4c69-ed72-6616db55303d"
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\n",
|
||
|
|
"***** TensorBoard Uploader *****\n",
|
||
|
|
"\n",
|
||
|
|
"This will upload your TensorBoard logs to https://tensorboard.dev/ from\n",
|
||
|
|
"the following directory:\n",
|
||
|
|
"\n",
|
||
|
|
"logs/AlexNet\n",
|
||
|
|
"\n",
|
||
|
|
"This TensorBoard will be visible to everyone. Do not upload sensitive\n",
|
||
|
|
"data.\n",
|
||
|
|
"\n",
|
||
|
|
"Your use of this service is subject to Google's Terms of Service\n",
|
||
|
|
"<https://policies.google.com/terms> and Privacy Policy\n",
|
||
|
|
"<https://policies.google.com/privacy>, and TensorBoard.dev's Terms of Service\n",
|
||
|
|
"<https://tensorboard.dev/policy/terms/>.\n",
|
||
|
|
"\n",
|
||
|
|
"This notice will not be shown again while you are logged into the uploader.\n",
|
||
|
|
"To log out, run `tensorboard dev auth revoke`.\n",
|
||
|
|
"\n",
|
||
|
|
"Continue? (yes/NO) yes\n",
|
||
|
|
"\n",
|
||
|
|
"Please visit this URL to authorize this application: https://accounts.google.com/o/oauth2/auth?response_type=code&client_id=373649185512-8v619h5kft38l4456nm2dj4ubeqsrvh6.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=openid+https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fuserinfo.email&state=4s45OaCdh7CXhhMYUBHIgm6u6oiMTE&prompt=consent&access_type=offline\n",
|
||
|
|
"Enter the authorization code: 4/1AWtgzh59_rKzQT7gGHgAyOnRtMf7ppSZuYlb-25UkSRY4IdlSjRmYmH_AfE\n",
|
||
|
|
"\n",
|
||
|
|
"Upload started and will continue reading any new data as it's added to the logdir.\n",
|
||
|
|
"\n",
|
||
|
|
"To stop uploading, press Ctrl-C.\n",
|
||
|
|
"\n",
|
||
|
|
"New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/0xVh6RMqTQiv0BCdbdLLBg/\n",
|
||
|
|
"\n",
|
||
|
|
"\u001b[1m[2023-02-01T08:10:10]\u001b[0m Started scanning logdir.\n",
|
||
|
|
"\u001b[1m[2023-02-01T08:10:11]\u001b[0m Total uploaded: 300 scalars, 0 tensors, 1 binary objects (75.6 kB)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"Interrupted. View your TensorBoard at https://tensorboard.dev/experiment/0xVh6RMqTQiv0BCdbdLLBg/\n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"/usr/local/bin/tensorboard\", line 8, in <module>\n",
|
||
|
|
" sys.exit(run_main())\n",
|
||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/main.py\", line 46, in run_main\n",
|
||
|
|
" app.run(tensorboard.main, flags_parser=tensorboard.configure)\n",
|
||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/absl/app.py\", line 308, in run\n",
|
||
|
|
" _run_main(main, args)\n",
|
||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/absl/app.py\", line 254, in _run_main\n",
|
||
|
|
" sys.exit(main(argv))\n",
|
||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/program.py\", line 276, in main\n",
|
||
|
|
" return runner(self.flags) or 0\n",
|
||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py\", line 692, in run\n",
|
||
|
|
" return _run(flags, self._experiment_url_callback)\n",
|
||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py\", line 125, in _run\n",
|
||
|
|
" intent.execute(server_info, channel)\n",
|
||
|
|
" File \"/usr/local/lib/python3.8/dist-packages/tensorboard/uploader/uploader_subcommand.py\", line 508, in execute\n",
|
||
|
|
" sys.stdout.write(end_message + \"\\n\")\n",
|
||
|
|
"KeyboardInterrupt\n",
|
||
|
|
"^C\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"!tensorboard dev upload --logdir logs/AlexNet --name AlexNetFish"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"id": "VdNLX57nc_Gs",
|
||
|
|
"outputId": "4353b693-12cd-45ac-f249-8b287281754b"
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"9/9 [==============================] - 0s 32ms/step - loss: 0.9122 - accuracy: 0.7188\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"[0.9122310280799866, 0.71875]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model.evaluate(test_ds)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/",
|
||
|
|
"height": 36
|
||
|
|
},
|
||
|
|
"id": "aaPsyfODLBgo",
|
||
|
|
"outputId": "7a1c3dd3-a662-4e6d-9cba-28aef9067d73"
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from training.model_freeze import freeze_model\n",
|
||
|
|
"\n",
|
||
|
|
"freeze_model(model, './frozen_models', \"frozen_alex_net\")"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"accelerator": "GPU",
|
||
|
|
"colab": {
|
||
|
|
"machine_shape": "hm",
|
||
|
|
"provenance": []
|
||
|
|
},
|
||
|
|
"gpuClass": "standard",
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "um",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"name": "python",
|
||
|
|
"version": "3.9.15 | packaged by conda-forge | (main, Nov 22 2022, 08:41:22) [MSC v.1929 64 bit (AMD64)]"
|
||
|
|
},
|
||
|
|
"vscode": {
|
||
|
|
"interpreter": {
|
||
|
|
"hash": "876e189cbbe99a9a838ece62aae1013186c4bb7e0254a10cfa2f9b2381853efb"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 0
|
||
|
|
}
|