1186 lines
147 KiB
Plaintext
1186 lines
147 KiB
Plaintext
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Aleksandra Jonas, Aleksandra Gronowska, Iwona Christop\n",
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"# Zadanie 9-10 - VGG16 + ResNet on train_test_sw "
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Przygotowanie danych"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"from IPython.display import Image, display"
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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": 16,
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"id": "2fe63b50",
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"metadata": {},
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"outputs": [],
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"source": [
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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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"\n",
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"required = { 'scikit-image'}\n",
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"installed = {pkg.key for pkg in pkg_resources.working_set}\n",
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"missing = required - installed\n",
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"# VGG16 requires images to be of dim = (224, 224, 3)\n",
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"newSize = (224,224)\n",
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"\n",
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"if missing: \n",
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" python = sys.executable\n",
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" subprocess.check_call([python, '-m', 'pip', 'install', *missing], stdout=subprocess.DEVNULL)\n",
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"\n",
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"def load_train_data(input_dir):\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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"\n",
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" import seaborn as sns\n",
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" import matplotlib.pyplot as plt\n",
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"\n",
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" import matplotlib\n",
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" \n",
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" image_dir = Path(input_dir)\n",
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" categories_name = []\n",
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" for file in os.listdir(image_dir):\n",
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" d = os.path.join(image_dir, file)\n",
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" if os.path.isdir(d):\n",
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" categories_name.append(file)\n",
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"\n",
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" folders = [directory for directory in image_dir.iterdir() if directory.is_dir()]\n",
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"\n",
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" train_img = []\n",
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" categories_count=[]\n",
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" labels=[]\n",
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" for i, direc in enumerate(folders):\n",
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" count = 0\n",
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" for obj in direc.iterdir():\n",
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" if os.path.isfile(obj) and os.path.basename(os.path.normpath(obj)) != 'desktop.ini':\n",
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" labels.append(os.path.basename(os.path.normpath(direc)))\n",
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" count += 1\n",
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" img = imread(obj)#zwraca ndarry postaci xSize x ySize x colorDepth\n",
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" img = img[:, :, :3]\n",
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" img = cv.resize(img, newSize, interpolation=cv.INTER_AREA)# zwraca ndarray\n",
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" img = img / 255 #normalizacja\n",
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" train_img.append(img)\n",
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" categories_count.append(count)\n",
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" X={}\n",
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" X[\"values\"] = np.array(train_img)\n",
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" X[\"categories_name\"] = categories_name\n",
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" X[\"categories_count\"] = categories_count\n",
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" X[\"labels\"]=labels\n",
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" return X\n",
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"\n",
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"def load_test_data(input_dir):\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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"\n",
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" import seaborn as sns\n",
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" import matplotlib.pyplot as plt\n",
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"\n",
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" import matplotlib\n",
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"\n",
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" image_path = Path(input_dir)\n",
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"\n",
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" labels_path = image_path.parents[0] / 'test_labels.json'\n",
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"\n",
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" jsonString = labels_path.read_text()\n",
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" objects = json.loads(jsonString)\n",
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"\n",
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" categories_name = []\n",
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" categories_count=[]\n",
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" count = 0\n",
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" c = objects[0]['value']\n",
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" for e in objects:\n",
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" if e['value'] != c:\n",
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" categories_count.append(count)\n",
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" c = e['value']\n",
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" count = 1\n",
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" else:\n",
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" count += 1\n",
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" if not e['value'] in categories_name:\n",
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" categories_name.append(e['value'])\n",
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"\n",
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" categories_count.append(count)\n",
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" \n",
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" test_img = []\n",
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"\n",
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" labels=[]\n",
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" for e in objects:\n",
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" p = image_path / e['filename']\n",
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" img = imread(p)#zwraca ndarry postaci xSize x ySize x colorDepth\n",
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" img = img[:, :, :3]\n",
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" img = cv.resize(img, newSize, interpolation=cv.INTER_AREA)# zwraca ndarray\n",
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" img = img / 255#normalizacja\n",
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" test_img.append(img)\n",
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" labels.append(e['value'])\n",
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"\n",
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" X={}\n",
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" X[\"values\"] = np.array(test_img)\n",
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" X[\"categories_name\"] = categories_name\n",
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" X[\"categories_count\"] = categories_count\n",
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" X[\"labels\"]=labels\n",
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" return X"
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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": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"def create_tf_ds(X_train, y_train_enc, X_validate, y_validate_enc, X_test, y_test_enc):\n",
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" import tensorflow as tf\n",
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" \n",
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" train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train_enc))\n",
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" validation_ds = tf.data.Dataset.from_tensor_slices((X_validate, y_validate_enc))\n",
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" test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test_enc))\n",
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"\n",
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" train_ds_size = tf.data.experimental.cardinality(train_ds).numpy()\n",
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" test_ds_size = tf.data.experimental.cardinality(test_ds).numpy()\n",
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" validation_ds_size = tf.data.experimental.cardinality(validation_ds).numpy()\n",
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"\n",
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" print(\"Training data size:\", train_ds_size)\n",
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" print(\"Test data size:\", test_ds_size)\n",
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" print(\"Validation data size:\", validation_ds_size)\n",
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"\n",
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" train_ds = (train_ds\n",
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" .shuffle(buffer_size=train_ds_size)\n",
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" .batch(batch_size=32, drop_remainder=True))\n",
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" test_ds = (test_ds\n",
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" .shuffle(buffer_size=train_ds_size)\n",
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" .batch(batch_size=32, drop_remainder=True))\n",
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" validation_ds = (validation_ds\n",
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" .shuffle(buffer_size=train_ds_size)\n",
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" .batch(batch_size=32, drop_remainder=True))\n",
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" \n",
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" return train_ds, test_ds, validation_ds"
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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": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_run_logdir(root_logdir):\n",
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" import os\n",
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" import time\n",
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"\n",
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" run_id = time.strftime(\"run_%Y_%m_%d-%H_%M_%S\")\n",
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" return os.path.join(root_logdir, run_id)"
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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": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"def diagram_setup(model_name):\n",
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" from tensorflow import keras\n",
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" import os\n",
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" \n",
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" root_logdir = os.path.join(os.curdir, f\"logs\\\\fit\\\\{model_name}\\\\\")\n",
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" \n",
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" run_logdir = get_run_logdir(root_logdir)\n",
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" tensorboard_cb = keras.callbacks.TensorBoard(run_logdir)"
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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": 20,
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"id": "cc941c5a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Data load\n",
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"data_train = load_train_data(\"./train_test_sw/train_sw\")\n",
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"values_train = data_train['values']\n",
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"labels_train = data_train['labels']\n",
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"\n",
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"data_test = load_test_data(\"./train_test_sw/test_sw\")\n",
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"X_test = data_test['values']\n",
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"y_test = data_test['labels']"
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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": 21,
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"id": "25040ac9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"X_train, X_validate, y_train, y_validate = train_test_split(values_train, labels_train, test_size=0.2, random_state=42)"
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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": 22,
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"id": "a1fe47e6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.preprocessing import LabelEncoder\n",
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"class_le = LabelEncoder()\n",
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"y_train_enc = class_le.fit_transform(y_train)\n",
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"y_validate_enc = class_le.fit_transform(y_validate)\n",
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"y_test_enc = class_le.fit_transform(y_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": 23,
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"metadata": {},
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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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"Training data size: 820\n",
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"Test data size: 259\n",
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"Validation data size: 206\n"
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]
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}
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],
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"source": [
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"train_ds, test_ds, validation_ds = create_tf_ds(X_train, y_train_enc, X_validate, y_validate_enc, X_test, y_test_enc)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## VGG16"
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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": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"diagram_setup('vgg_sw')"
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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": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"import keras,os\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Dense, Conv2D, MaxPool2D , Flatten\n",
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"from keras.preprocessing.image import ImageDataGenerator\n",
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"import numpy as np\n",
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"\n",
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"model = keras.models.Sequential([\n",
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" keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(224,224,3), padding=\"same\"),\n",
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" keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=(224,224,3), padding=\"same\"),\n",
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" keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
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" keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
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" keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
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" keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.MaxPool2D(pool_size=(2,2), strides=(2,2)),\n",
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" keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"),\n",
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" keras.layers.Flatten(),\n",
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" keras.layers.Dense(units = 4096, activation='relu'),\n",
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" keras.layers.Dense(units = 4096, activation='relu'),\n",
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" keras.layers.Dense(units = 5, 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": 26,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/jonas/Library/Python/3.9/lib/python/site-packages/keras/optimizers/optimizer_v2/adam.py:117: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
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" super().__init__(name, **kwargs)\n"
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]
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}
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],
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"source": [
|
||
|
"from keras.optimizers import Adam\n",
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"opt = Adam(lr=0.001)\n",
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"model.compile(optimizer=opt, loss=keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])"
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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": 27,
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"metadata": {},
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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, 224, 224, 64) 1792 \n",
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" \n",
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" conv2d_1 (Conv2D) (None, 224, 224, 64) 36928 \n",
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" \n",
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" max_pooling2d (MaxPooling2D (None, 112, 112, 64) 0 \n",
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" ) \n",
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" \n",
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" conv2d_2 (Conv2D) (None, 112, 112, 128) 73856 \n",
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" \n",
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" conv2d_3 (Conv2D) (None, 112, 112, 128) 147584 \n",
|
||
|
" \n",
|
||
|
" max_pooling2d_1 (MaxPooling (None, 56, 56, 128) 0 \n",
|
||
|
" 2D) \n",
|
||
|
" \n",
|
||
|
" conv2d_4 (Conv2D) (None, 56, 56, 256) 295168 \n",
|
||
|
" \n",
|
||
|
" conv2d_5 (Conv2D) (None, 56, 56, 256) 590080 \n",
|
||
|
" \n",
|
||
|
" conv2d_6 (Conv2D) (None, 56, 56, 256) 590080 \n",
|
||
|
" \n",
|
||
|
" max_pooling2d_2 (MaxPooling (None, 28, 28, 256) 0 \n",
|
||
|
" 2D) \n",
|
||
|
" \n",
|
||
|
" conv2d_7 (Conv2D) (None, 28, 28, 512) 1180160 \n",
|
||
|
" \n",
|
||
|
" conv2d_8 (Conv2D) (None, 28, 28, 512) 2359808 \n",
|
||
|
" \n",
|
||
|
" conv2d_9 (Conv2D) (None, 28, 28, 512) 2359808 \n",
|
||
|
" \n",
|
||
|
" max_pooling2d_3 (MaxPooling (None, 14, 14, 512) 0 \n",
|
||
|
" 2D) \n",
|
||
|
" \n",
|
||
|
" conv2d_10 (Conv2D) (None, 14, 14, 512) 2359808 \n",
|
||
|
" \n",
|
||
|
" conv2d_11 (Conv2D) (None, 14, 14, 512) 2359808 \n",
|
||
|
" \n",
|
||
|
" conv2d_12 (Conv2D) (None, 14, 14, 512) 2359808 \n",
|
||
|
" \n",
|
||
|
" flatten (Flatten) (None, 100352) 0 \n",
|
||
|
" \n",
|
||
|
" dense (Dense) (None, 4096) 411045888 \n",
|
||
|
" \n",
|
||
|
" dense_1 (Dense) (None, 4096) 16781312 \n",
|
||
|
" \n",
|
||
|
" dense_2 (Dense) (None, 5) 20485 \n",
|
||
|
" \n",
|
||
|
"=================================================================\n",
|
||
|
"Total params: 442,562,373\n",
|
||
|
"Trainable params: 442,562,373\n",
|
||
|
"Non-trainable params: 0\n",
|
||
|
"_________________________________________________________________\n"
|
||
|
]
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"model.summary()"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": 28,
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"name": "stdout",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
|
||
|
"Epoch 1/2\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"name": "stderr",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"/var/folders/6b/j4d60ym516x2s6wymzj707rh0000gn/T/ipykernel_8661/3543889534.py:4: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
|
||
|
" hist_vgg = model.fit_generator(steps_per_epoch=len(train_ds), generator=train_ds, validation_data= validation_ds, validation_steps=len(validation_ds), epochs=2, callbacks=[checkpoint,early])\n",
|
||
|
"2023-01-06 03:00:40.894219: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"name": "stdout",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
" 2/25 [=>............................] - ETA: 9:29 - loss: 1.5960 - accuracy: 0.2031"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"ename": "KeyboardInterrupt",
|
||
|
"evalue": "",
|
||
|
"output_type": "error",
|
||
|
"traceback": [
|
||
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||
|
"Cell \u001b[0;32mIn[28], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m checkpoint \u001b[39m=\u001b[39m ModelCheckpoint(\u001b[39m\"\u001b[39m\u001b[39mvgg16_1.h5\u001b[39m\u001b[39m\"\u001b[39m, monitor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mval_accuracy\u001b[39m\u001b[39m'\u001b[39m, verbose\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, save_best_only\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, save_weights_only\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, mode\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mauto\u001b[39m\u001b[39m'\u001b[39m, period\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m)\n\u001b[1;32m 3\u001b[0m early \u001b[39m=\u001b[39m EarlyStopping(monitor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mval_accuracy\u001b[39m\u001b[39m'\u001b[39m, min_delta\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, patience\u001b[39m=\u001b[39m\u001b[39m20\u001b[39m, verbose\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m, mode\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mauto\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m hist_vgg \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mfit_generator(steps_per_epoch\u001b[39m=\u001b[39;49m\u001b[39mlen\u001b[39;49m(train_ds), generator\u001b[39m=\u001b[39;49mtrain_ds, validation_data\u001b[39m=\u001b[39;49m validation_ds, validation_steps\u001b[39m=\u001b[39;49m\u001b[39mlen\u001b[39;49m(validation_ds), epochs\u001b[39m=\u001b[39;49m\u001b[39m2\u001b[39;49m, callbacks\u001b[39m=\u001b[39;49m[checkpoint,early])\n",
|
||
|
"File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/keras/engine/training.py:2604\u001b[0m, in \u001b[0;36mModel.fit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m 2592\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Fits the model on data yielded batch-by-batch by a Python generator.\u001b[39;00m\n\u001b[1;32m 2593\u001b[0m \n\u001b[1;32m 2594\u001b[0m \u001b[39mDEPRECATED:\u001b[39;00m\n\u001b[1;32m 2595\u001b[0m \u001b[39m `Model.fit` now supports generators, so there is no longer any need to\u001b[39;00m\n\u001b[1;32m 2596\u001b[0m \u001b[39m use this endpoint.\u001b[39;00m\n\u001b[1;32m 2597\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 2598\u001b[0m warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m 2599\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m`Model.fit_generator` is deprecated and \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 2600\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mwill be removed in a future version. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 2601\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mPlease use `Model.fit`, which supports generators.\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 2602\u001b[0m stacklevel\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m,\n\u001b[1;32m 2603\u001b[0m )\n\u001b[0;32m-> 2604\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfit(\n\u001b[1;32m 2605\u001b[0m generator,\n\u001b[1;32m 2606\u001b[0m steps_per_epoch\u001b[39m=\u001b[39;49msteps_per_epoch,\n\u001b[1;32m 2607\u001b[0m epochs\u001b[39m=\u001b[39;49mepochs,\n\u001b[1;32m 2608\u001b[0m verbose\u001b[39m=\u001b[39;49mverbose,\n\u001b[1;32m 2609\u001b[0m callbacks\u001b[39m=\u001b[39;49mcallbacks,\n\u001b[1;32m 2610\u001b[0m validation_data\u001b[39m=\u001b[39;49mvalidation_data,\n\u001b[1;32m 2611\u001b[0m validation_steps\u001b[39m=\u001b[39;49mvalidation_steps,\n\u001b[1;32m 2612\u001b[0m validation_freq\u001b[39m=\u001b[39;49mvalidation_freq,\n\u001b[1;32m 2613\u001b[0m class_weight\u001b[39m=\u001b[39;49mclass_weight,\n\u001b[1;32m 2614\u001b[0m max_queue_size\u001b[39m=\u001b[39;49mmax_queue_size,\n\u001b[1;32m 2615\u001b[0m workers\u001b[39m=\u001b[39;49mworkers,\n\u001b[1;32m 2616\u001b[0m use_multiprocessing\u001b[39m=\u001b[39;49muse_multiprocessing,\n\u001b[1;32m 2617\u001b[0m shuffle\u001b[39m=\u001b[39;49mshuffle,\n\u001b[1;32m 2618\u001b[0m initial_epoch\u001b[39m=\u001b[39;49minitial_epoch,\n\u001b[1;32m 2619\u001b[0m )\n",
|
||
|
"File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/keras/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 67\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
|
||
|
"File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/keras/engine/training.py:1650\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1642\u001b[0m \u001b[39mwith\u001b[39;00m tf\u001b[39m.\u001b[39mprofiler\u001b[39m.\u001b[39mexperimental\u001b[39m.\u001b[39mTrace(\n\u001b[1;32m 1643\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 1644\u001b[0m epoch_num\u001b[39m=\u001b[39mepoch,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1647\u001b[0m _r\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[1;32m 1648\u001b[0m ):\n\u001b[1;32m 1649\u001b[0m callbacks\u001b[39m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m-> 1650\u001b[0m tmp_logs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_function(iterator)\n\u001b[1;32m 1651\u001b[0m \u001b[39mif\u001b[39;00m data_handler\u001b[39m.\u001b[39mshould_sync:\n\u001b[1;32m 1652\u001b[0m context\u001b[39m.\u001b[39masync_wait()\n",
|
||
|
"File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 149\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 151\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 152\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
|
||
|
"File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:880\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 877\u001b[0m compiler \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mxla\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile \u001b[39melse\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mnonXla\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 879\u001b[0m \u001b[39mwith\u001b[39;00m OptionalXlaContext(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 880\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[1;32m 882\u001b[0m new_tracing_count \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m 883\u001b[0m without_tracing \u001b[39m=\u001b[39m (tracing_count \u001b[39m==\u001b[39m new_tracing_count)\n",
|
||
|
"File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:912\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 909\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n\u001b[1;32m 910\u001b[0m \u001b[39m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[1;32m 911\u001b[0m \u001b[39m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[0;32m--> 912\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_no_variable_creation_fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds) \u001b[39m# pylint: disable=not-callable\u001b[39;00m\n\u001b[1;32m 913\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_variable_creation_fn \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 914\u001b[0m \u001b[39m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[1;32m 915\u001b[0m \u001b[39m# in parallel.\u001b[39;00m\n\u001b[1;32m 916\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock\u001b[39m.\u001b[39mrelease()\n",
|
||
|
"File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py:134\u001b[0m, in \u001b[0;36mTracingCompiler.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_lock:\n\u001b[1;32m 132\u001b[0m (concrete_function,\n\u001b[1;32m 133\u001b[0m filtered_flat_args) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[0;32m--> 134\u001b[0m \u001b[39mreturn\u001b[39;00m concrete_function\u001b[39m.\u001b[39;49m_call_flat(\n\u001b[1;32m 135\u001b[0m filtered_flat_args, captured_inputs\u001b[39m=\u001b[39;49mconcrete_function\u001b[39m.\u001b[39;49mcaptured_inputs)\n",
|
||
|
"File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:1745\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1741\u001b[0m possible_gradient_type \u001b[39m=\u001b[39m gradients_util\u001b[39m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m 1742\u001b[0m \u001b[39mif\u001b[39;00m (possible_gradient_type \u001b[39m==\u001b[39m gradients_util\u001b[39m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m 1743\u001b[0m \u001b[39mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m 1744\u001b[0m \u001b[39m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1745\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_build_call_outputs(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_inference_function\u001b[39m.\u001b[39;49mcall(\n\u001b[1;32m 1746\u001b[0m ctx, args, cancellation_manager\u001b[39m=\u001b[39;49mcancellation_manager))\n\u001b[1;32m 1747\u001b[0m forward_backward \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m 1748\u001b[0m args,\n\u001b[1;32m 1749\u001b[0m possible_gradient_type,\n\u001b[1;32m 1750\u001b[0m executing_eagerly)\n\u001b[1;32m 1751\u001b[0m forward_function, args_with_tangents \u001b[39m=\u001b[39m forward_backward\u001b[39m.\u001b[39mforward()\n",
|
||
|
"File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py:378\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 376\u001b[0m \u001b[39mwith\u001b[39;00m _InterpolateFunctionError(\u001b[39mself\u001b[39m):\n\u001b[1;32m 377\u001b[0m \u001b[39mif\u001b[39;00m cancellation_manager \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 378\u001b[0m outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39;49mexecute(\n\u001b[1;32m 379\u001b[0m \u001b[39mstr\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msignature\u001b[39m.\u001b[39;49mname),\n\u001b[1;32m 380\u001b[0m num_outputs\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_num_outputs,\n\u001b[1;32m 381\u001b[0m inputs\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m 382\u001b[0m attrs\u001b[39m=\u001b[39;49mattrs,\n\u001b[1;32m 383\u001b[0m ctx\u001b[39m=\u001b[39;49mctx)\n\u001b[1;32m 384\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 385\u001b[0m outputs \u001b[39m=\u001b[39m execute\u001b[39m.\u001b[39mexecute_with_cancellation(\n\u001b[1;32m 386\u001b[0m \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msignature\u001b[39m.\u001b[39mname),\n\u001b[1;32m 387\u001b[0m num_outputs\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_outputs,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 390\u001b[0m ctx\u001b[39m=\u001b[39mctx,\n\u001b[1;32m 391\u001b[0m cancellation_manager\u001b[39m=\u001b[39mcancellation_manager)\n",
|
||
|
"File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tensorflow/python/eager/execute.py:52\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 51\u001b[0m ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 52\u001b[0m tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39;49mTFE_Py_Execute(ctx\u001b[39m.\u001b[39;49m_handle, device_name, op_name,\n\u001b[1;32m 53\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 54\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 55\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
|
||
|
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
|
||
|
]
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
|
||
|
"checkpoint = ModelCheckpoint(\"vgg16_1.h5\", monitor='val_accuracy', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)\n",
|
||
|
"early = EarlyStopping(monitor='val_accuracy', min_delta=0, patience=20, verbose=1, mode='auto')\n",
|
||
|
"hist_vgg = model.fit_generator(steps_per_epoch=len(train_ds), generator=train_ds, validation_data= validation_ds, validation_steps=len(validation_ds), epochs=2, callbacks=[checkpoint,early])"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"data": {
|
||
|
"image/png": "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
|
||
|
"text/plain": [
|
||
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
]
|
||
|
},
|
||
|
"metadata": {},
|
||
|
"output_type": "display_data"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"import matplotlib.pyplot as plt\n",
|
||
|
"plt.plot(hist_vgg.history[\"accuracy\"])\n",
|
||
|
"plt.plot(hist_vgg.history['val_accuracy'])\n",
|
||
|
"plt.plot(hist_vgg.history['loss'])\n",
|
||
|
"plt.plot(hist_vgg.history['val_loss'])\n",
|
||
|
"plt.title(\"Model accuracy\")\n",
|
||
|
"plt.ylabel(\"Value\")\n",
|
||
|
"plt.xlabel(\"Epoch\")\n",
|
||
|
"plt.legend([\"Accuracy\",\"Validation Accuracy\",\"Loss\",\"Validation Loss\"])\n",
|
||
|
"plt.show()"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"attachments": {},
|
||
|
"cell_type": "markdown",
|
||
|
"metadata": {},
|
||
|
"source": [
|
||
|
"## ResNet50"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"metadata": {},
|
||
|
"outputs": [
|
||
|
{
|
||
|
"name": "stdout",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"Model: \"model_1\"\n",
|
||
|
"__________________________________________________________________________________________________\n",
|
||
|
" Layer (type) Output Shape Param # Connected to \n",
|
||
|
"==================================================================================================\n",
|
||
|
" input_3 (InputLayer) [(None, 224, 224, 3 0 [] \n",
|
||
|
" )] \n",
|
||
|
" \n",
|
||
|
" conv1_pad (ZeroPadding2D) (None, 230, 230, 3) 0 ['input_3[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv1_conv (Conv2D) (None, 112, 112, 64 9472 ['conv1_pad[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv1_bn (BatchNormalization) (None, 112, 112, 64 256 ['conv1_conv[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv1_relu (Activation) (None, 112, 112, 64 0 ['conv1_bn[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" pool1_pad (ZeroPadding2D) (None, 114, 114, 64 0 ['conv1_relu[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" pool1_pool (MaxPooling2D) (None, 56, 56, 64) 0 ['pool1_pad[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block1_1_conv (Conv2D) (None, 56, 56, 64) 4160 ['pool1_pool[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block1_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block1_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv2_block1_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block1_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv2_block1_2_conv (Conv2D) (None, 56, 56, 64) 36928 ['conv2_block1_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block1_2_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block1_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv2_block1_2_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block1_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv2_block1_0_conv (Conv2D) (None, 56, 56, 256) 16640 ['pool1_pool[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block1_3_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block1_2_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block1_0_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block1_0_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv2_block1_3_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block1_3_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv2_block1_add (Add) (None, 56, 56, 256) 0 ['conv2_block1_0_bn[0][0]', \n",
|
||
|
" 'conv2_block1_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block1_out (Activation) (None, 56, 56, 256) 0 ['conv2_block1_add[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block2_1_conv (Conv2D) (None, 56, 56, 64) 16448 ['conv2_block1_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block2_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block2_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv2_block2_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block2_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv2_block2_2_conv (Conv2D) (None, 56, 56, 64) 36928 ['conv2_block2_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block2_2_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block2_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv2_block2_2_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block2_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv2_block2_3_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block2_2_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block2_3_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block2_3_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv2_block2_add (Add) (None, 56, 56, 256) 0 ['conv2_block1_out[0][0]', \n",
|
||
|
" 'conv2_block2_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block2_out (Activation) (None, 56, 56, 256) 0 ['conv2_block2_add[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block3_1_conv (Conv2D) (None, 56, 56, 64) 16448 ['conv2_block2_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block3_1_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block3_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv2_block3_1_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block3_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv2_block3_2_conv (Conv2D) (None, 56, 56, 64) 36928 ['conv2_block3_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block3_2_bn (BatchNormal (None, 56, 56, 64) 256 ['conv2_block3_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv2_block3_2_relu (Activatio (None, 56, 56, 64) 0 ['conv2_block3_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv2_block3_3_conv (Conv2D) (None, 56, 56, 256) 16640 ['conv2_block3_2_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block3_3_bn (BatchNormal (None, 56, 56, 256) 1024 ['conv2_block3_3_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv2_block3_add (Add) (None, 56, 56, 256) 0 ['conv2_block2_out[0][0]', \n",
|
||
|
" 'conv2_block3_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv2_block3_out (Activation) (None, 56, 56, 256) 0 ['conv2_block3_add[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block1_1_conv (Conv2D) (None, 28, 28, 128) 32896 ['conv2_block3_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block1_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block1_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block1_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block1_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv3_block1_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block1_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block1_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block1_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block1_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block1_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv3_block1_0_conv (Conv2D) (None, 28, 28, 512) 131584 ['conv2_block3_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block1_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block1_2_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block1_0_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block1_0_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block1_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block1_3_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block1_add (Add) (None, 28, 28, 512) 0 ['conv3_block1_0_bn[0][0]', \n",
|
||
|
" 'conv3_block1_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block1_out (Activation) (None, 28, 28, 512) 0 ['conv3_block1_add[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block2_1_conv (Conv2D) (None, 28, 28, 128) 65664 ['conv3_block1_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block2_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block2_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block2_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block2_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv3_block2_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block2_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block2_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block2_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block2_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block2_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv3_block2_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block2_2_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block2_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block2_3_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block2_add (Add) (None, 28, 28, 512) 0 ['conv3_block1_out[0][0]', \n",
|
||
|
" 'conv3_block2_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block2_out (Activation) (None, 28, 28, 512) 0 ['conv3_block2_add[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block3_1_conv (Conv2D) (None, 28, 28, 128) 65664 ['conv3_block2_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block3_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block3_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block3_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block3_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv3_block3_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block3_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block3_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block3_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block3_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block3_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv3_block3_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block3_2_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block3_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block3_3_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block3_add (Add) (None, 28, 28, 512) 0 ['conv3_block2_out[0][0]', \n",
|
||
|
" 'conv3_block3_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block3_out (Activation) (None, 28, 28, 512) 0 ['conv3_block3_add[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block4_1_conv (Conv2D) (None, 28, 28, 128) 65664 ['conv3_block3_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block4_1_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block4_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block4_1_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block4_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv3_block4_2_conv (Conv2D) (None, 28, 28, 128) 147584 ['conv3_block4_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block4_2_bn (BatchNormal (None, 28, 28, 128) 512 ['conv3_block4_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block4_2_relu (Activatio (None, 28, 28, 128) 0 ['conv3_block4_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv3_block4_3_conv (Conv2D) (None, 28, 28, 512) 66048 ['conv3_block4_2_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block4_3_bn (BatchNormal (None, 28, 28, 512) 2048 ['conv3_block4_3_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv3_block4_add (Add) (None, 28, 28, 512) 0 ['conv3_block3_out[0][0]', \n",
|
||
|
" 'conv3_block4_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv3_block4_out (Activation) (None, 28, 28, 512) 0 ['conv3_block4_add[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block1_1_conv (Conv2D) (None, 14, 14, 256) 131328 ['conv3_block4_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block1_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block1_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block1_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block1_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block1_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block1_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block1_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block1_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block1_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block1_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block1_0_conv (Conv2D) (None, 14, 14, 1024 525312 ['conv3_block4_out[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block1_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block1_2_relu[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block1_0_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block1_0_conv[0][0]'] \n",
|
||
|
" ization) ) \n",
|
||
|
" \n",
|
||
|
" conv4_block1_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block1_3_conv[0][0]'] \n",
|
||
|
" ization) ) \n",
|
||
|
" \n",
|
||
|
" conv4_block1_add (Add) (None, 14, 14, 1024 0 ['conv4_block1_0_bn[0][0]', \n",
|
||
|
" ) 'conv4_block1_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block1_out (Activation) (None, 14, 14, 1024 0 ['conv4_block1_add[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block2_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block1_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block2_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block2_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block2_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block2_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block2_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block2_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block2_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block2_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block2_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block2_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block2_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block2_2_relu[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block2_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block2_3_conv[0][0]'] \n",
|
||
|
" ization) ) \n",
|
||
|
" \n",
|
||
|
" conv4_block2_add (Add) (None, 14, 14, 1024 0 ['conv4_block1_out[0][0]', \n",
|
||
|
" ) 'conv4_block2_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block2_out (Activation) (None, 14, 14, 1024 0 ['conv4_block2_add[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block3_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block2_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block3_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block3_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block3_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block3_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block3_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block3_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block3_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block3_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block3_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block3_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block3_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block3_2_relu[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block3_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block3_3_conv[0][0]'] \n",
|
||
|
" ization) ) \n",
|
||
|
" \n",
|
||
|
" conv4_block3_add (Add) (None, 14, 14, 1024 0 ['conv4_block2_out[0][0]', \n",
|
||
|
" ) 'conv4_block3_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block3_out (Activation) (None, 14, 14, 1024 0 ['conv4_block3_add[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block4_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block3_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block4_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block4_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block4_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block4_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block4_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block4_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block4_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block4_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block4_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block4_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block4_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block4_2_relu[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block4_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block4_3_conv[0][0]'] \n",
|
||
|
" ization) ) \n",
|
||
|
" \n",
|
||
|
" conv4_block4_add (Add) (None, 14, 14, 1024 0 ['conv4_block3_out[0][0]', \n",
|
||
|
" ) 'conv4_block4_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block4_out (Activation) (None, 14, 14, 1024 0 ['conv4_block4_add[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block5_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block4_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block5_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block5_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block5_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block5_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block5_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block5_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block5_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block5_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block5_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block5_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block5_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block5_2_relu[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block5_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block5_3_conv[0][0]'] \n",
|
||
|
" ization) ) \n",
|
||
|
" \n",
|
||
|
" conv4_block5_add (Add) (None, 14, 14, 1024 0 ['conv4_block4_out[0][0]', \n",
|
||
|
" ) 'conv4_block5_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block5_out (Activation) (None, 14, 14, 1024 0 ['conv4_block5_add[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block6_1_conv (Conv2D) (None, 14, 14, 256) 262400 ['conv4_block5_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block6_1_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block6_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block6_1_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block6_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block6_2_conv (Conv2D) (None, 14, 14, 256) 590080 ['conv4_block6_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block6_2_bn (BatchNormal (None, 14, 14, 256) 1024 ['conv4_block6_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv4_block6_2_relu (Activatio (None, 14, 14, 256) 0 ['conv4_block6_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv4_block6_3_conv (Conv2D) (None, 14, 14, 1024 263168 ['conv4_block6_2_relu[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv4_block6_3_bn (BatchNormal (None, 14, 14, 1024 4096 ['conv4_block6_3_conv[0][0]'] \n",
|
||
|
" ization) ) \n",
|
||
|
" \n",
|
||
|
" conv4_block6_add (Add) (None, 14, 14, 1024 0 ['conv4_block5_out[0][0]', \n",
|
||
|
" ) 'conv4_block6_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv4_block6_out (Activation) (None, 14, 14, 1024 0 ['conv4_block6_add[0][0]'] \n",
|
||
|
" ) \n",
|
||
|
" \n",
|
||
|
" conv5_block1_1_conv (Conv2D) (None, 7, 7, 512) 524800 ['conv4_block6_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block1_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block1_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv5_block1_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block1_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv5_block1_2_conv (Conv2D) (None, 7, 7, 512) 2359808 ['conv5_block1_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block1_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block1_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv5_block1_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block1_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv5_block1_0_conv (Conv2D) (None, 7, 7, 2048) 2099200 ['conv4_block6_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block1_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block1_2_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block1_0_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block1_0_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv5_block1_3_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block1_3_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv5_block1_add (Add) (None, 7, 7, 2048) 0 ['conv5_block1_0_bn[0][0]', \n",
|
||
|
" 'conv5_block1_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block1_out (Activation) (None, 7, 7, 2048) 0 ['conv5_block1_add[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block2_1_conv (Conv2D) (None, 7, 7, 512) 1049088 ['conv5_block1_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block2_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block2_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv5_block2_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block2_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv5_block2_2_conv (Conv2D) (None, 7, 7, 512) 2359808 ['conv5_block2_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block2_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block2_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv5_block2_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block2_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv5_block2_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block2_2_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block2_3_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block2_3_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv5_block2_add (Add) (None, 7, 7, 2048) 0 ['conv5_block1_out[0][0]', \n",
|
||
|
" 'conv5_block2_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block2_out (Activation) (None, 7, 7, 2048) 0 ['conv5_block2_add[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block3_1_conv (Conv2D) (None, 7, 7, 512) 1049088 ['conv5_block2_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block3_1_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block3_1_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv5_block3_1_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block3_1_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv5_block3_2_conv (Conv2D) (None, 7, 7, 512) 2359808 ['conv5_block3_1_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block3_2_bn (BatchNormal (None, 7, 7, 512) 2048 ['conv5_block3_2_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv5_block3_2_relu (Activatio (None, 7, 7, 512) 0 ['conv5_block3_2_bn[0][0]'] \n",
|
||
|
" n) \n",
|
||
|
" \n",
|
||
|
" conv5_block3_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 ['conv5_block3_2_relu[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block3_3_bn (BatchNormal (None, 7, 7, 2048) 8192 ['conv5_block3_3_conv[0][0]'] \n",
|
||
|
" ization) \n",
|
||
|
" \n",
|
||
|
" conv5_block3_add (Add) (None, 7, 7, 2048) 0 ['conv5_block2_out[0][0]', \n",
|
||
|
" 'conv5_block3_3_bn[0][0]'] \n",
|
||
|
" \n",
|
||
|
" conv5_block3_out (Activation) (None, 7, 7, 2048) 0 ['conv5_block3_add[0][0]'] \n",
|
||
|
" \n",
|
||
|
" flatten_6 (Flatten) (None, 100352) 0 ['conv5_block3_out[0][0]'] \n",
|
||
|
" \n",
|
||
|
" dense_13 (Dense) (None, 5) 501765 ['flatten_6[0][0]'] \n",
|
||
|
" \n",
|
||
|
"==================================================================================================\n",
|
||
|
"Total params: 24,089,477\n",
|
||
|
"Trainable params: 501,765\n",
|
||
|
"Non-trainable params: 23,587,712\n",
|
||
|
"__________________________________________________________________________________________________\n",
|
||
|
"Epoch 1/5\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"name": "stderr",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"/var/folders/_h/ljwht4gd7lb99rm1hm78h7_00000gn/T/ipykernel_13133/3879957867.py:45: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
|
||
|
" r = model.fit_generator(\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"name": "stdout",
|
||
|
"output_type": "stream",
|
||
|
"text": [
|
||
|
"25/25 [==============================] - 90s 3s/step - loss: 4.3387 - accuracy: 0.2100 - val_loss: 2.1355 - val_accuracy: 0.1875\n",
|
||
|
"Epoch 2/5\n",
|
||
|
"25/25 [==============================] - 84s 3s/step - loss: 1.9657 - accuracy: 0.2550 - val_loss: 2.3121 - val_accuracy: 0.2188\n",
|
||
|
"Epoch 3/5\n",
|
||
|
"25/25 [==============================] - 84s 3s/step - loss: 1.8658 - accuracy: 0.2600 - val_loss: 1.4832 - val_accuracy: 0.3073\n",
|
||
|
"Epoch 4/5\n",
|
||
|
"25/25 [==============================] - 83s 3s/step - loss: 1.7074 - accuracy: 0.2775 - val_loss: 1.5045 - val_accuracy: 0.3698\n",
|
||
|
"Epoch 5/5\n",
|
||
|
"25/25 [==============================] - 85s 3s/step - loss: 1.9758 - accuracy: 0.2800 - val_loss: 1.7073 - val_accuracy: 0.2812\n"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"data": {
|
||
|
"image/png": "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
|
||
|
"text/plain": [
|
||
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
]
|
||
|
},
|
||
|
"metadata": {},
|
||
|
"output_type": "display_data"
|
||
|
},
|
||
|
{
|
||
|
"ename": "KeyError",
|
||
|
"evalue": "'acc'",
|
||
|
"output_type": "error",
|
||
|
"traceback": [
|
||
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
|
||
|
"Cell \u001b[0;32mIn [55], line 60\u001b[0m\n\u001b[1;32m 57\u001b[0m plt\u001b[39m.\u001b[39msavefig(\u001b[39m'\u001b[39m\u001b[39mLossVal_loss\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 59\u001b[0m \u001b[39m# accuracies\u001b[39;00m\n\u001b[0;32m---> 60\u001b[0m plt\u001b[39m.\u001b[39mplot(r\u001b[39m.\u001b[39;49mhistory[\u001b[39m'\u001b[39;49m\u001b[39macc\u001b[39;49m\u001b[39m'\u001b[39;49m], label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mtrain acc\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 61\u001b[0m plt\u001b[39m.\u001b[39mplot(r\u001b[39m.\u001b[39mhistory[\u001b[39m'\u001b[39m\u001b[39mval_acc\u001b[39m\u001b[39m'\u001b[39m], label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mval acc\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 62\u001b[0m plt\u001b[39m.\u001b[39mlegend()\n",
|
||
|
"\u001b[0;31mKeyError\u001b[0m: 'acc'"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"data": {
|
||
|
"text/plain": [
|
||
|
"<Figure size 640x480 with 0 Axes>"
|
||
|
]
|
||
|
},
|
||
|
"metadata": {},
|
||
|
"output_type": "display_data"
|
||
|
}
|
||
|
],
|
||
|
"source": [
|
||
|
"from keras.layers import Input, Lambda, Dense, Flatten\n",
|
||
|
"from keras.models import Model\n",
|
||
|
"from keras.applications import ResNet50\n",
|
||
|
"from keras.preprocessing import image\n",
|
||
|
"from keras.preprocessing.image import ImageDataGenerator\n",
|
||
|
"from keras.models import Sequential\n",
|
||
|
"import numpy as np\n",
|
||
|
"from glob import glob\n",
|
||
|
"import matplotlib.pyplot as plt\n",
|
||
|
"\n",
|
||
|
"# re-size all the images to this\n",
|
||
|
"IMAGE_SIZE = [224, 224]\n",
|
||
|
"\n",
|
||
|
"# add preprocessing layer to the front of resnet\n",
|
||
|
"resnet = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
|
||
|
"\n",
|
||
|
"# don't train existing weights\n",
|
||
|
"for layer in resnet.layers:\n",
|
||
|
" layer.trainable = False\n",
|
||
|
" \n",
|
||
|
" # useful for getting number of classes\n",
|
||
|
"classes = 5\n",
|
||
|
" \n",
|
||
|
"\n",
|
||
|
"# our layers - you can add more if you want\n",
|
||
|
"x = Flatten()(resnet.output)\n",
|
||
|
"# x = Dense(1000, activation='relu')(x)\n",
|
||
|
"prediction = Dense(5, activation='softmax')(x)"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"# create a model object\n",
|
||
|
"model = Model(inputs=resnet.input, outputs=prediction)\n",
|
||
|
"\n",
|
||
|
"# view the structure of the model\n",
|
||
|
"model.summary()"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"# tell the model what cost and optimization method to use\n",
|
||
|
"model.compile(\n",
|
||
|
" loss='sparse_categorical_crossentropy',\n",
|
||
|
" optimizer='adam',\n",
|
||
|
" metrics=['accuracy']\n",
|
||
|
")\n",
|
||
|
"\n",
|
||
|
"#train_ds_vgg_sw, test_ds_vgg_sw, validation_ds_vgg_sw\n",
|
||
|
"# fit the model\n",
|
||
|
"r = model.fit_generator(\n",
|
||
|
" train_ds,\n",
|
||
|
" validation_data=validation_ds,\n",
|
||
|
" epochs=100,\n",
|
||
|
" steps_per_epoch=len(train_ds),\n",
|
||
|
" validation_steps=len(validation_ds)\n",
|
||
|
")"
|
||
|
]
|
||
|
},
|
||
|
{
|
||
|
"cell_type": "code",
|
||
|
"execution_count": null,
|
||
|
"metadata": {},
|
||
|
"outputs": [],
|
||
|
"source": [
|
||
|
"# loss\n",
|
||
|
"plt.plot(r.history[\"accuracy\"])\n",
|
||
|
"plt.plot(r.history['val_accuracy'])\n",
|
||
|
"plt.plot(r.history['loss'])\n",
|
||
|
"plt.plot(r.history['val_loss'])\n",
|
||
|
"plt.title(\"Model accuracy\")\n",
|
||
|
"plt.ylabel(\"Value\")\n",
|
||
|
"plt.xlabel(\"Epoch\")\n",
|
||
|
"plt.legend([\"Accuracy\",\"Validation Accuracy\",\"Loss\",\"Validation Loss\"])\n",
|
||
|
"plt.show()\n",
|
||
|
"\n",
|
||
|
"model.save('resnet_1.h5')"
|
||
|
]
|
||
|
}
|
||
|
],
|
||
|
"metadata": {
|
||
|
"kernelspec": {
|
||
|
"display_name": "Python 3",
|
||
|
"language": "python",
|
||
|
"name": "python3"
|
||
|
},
|
||
|
"language_info": {
|
||
|
"codemirror_mode": {
|
||
|
"name": "ipython",
|
||
|
"version": 3
|
||
|
},
|
||
|
"file_extension": ".py",
|
||
|
"mimetype": "text/x-python",
|
||
|
"name": "python",
|
||
|
"nbconvert_exporter": "python",
|
||
|
"pygments_lexer": "ipython3",
|
||
|
"version": "3.9.6"
|
||
|
},
|
||
|
"orig_nbformat": 4,
|
||
|
"vscode": {
|
||
|
"interpreter": {
|
||
|
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
|
||
|
}
|
||
|
}
|
||
|
},
|
||
|
"nbformat": 4,
|
||
|
"nbformat_minor": 2
|
||
|
}
|