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unet added
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{
"cells": [
{
"cell_type": "code",
working generators + model tests
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"execution_count": 1,
unet added
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"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"import tensorflow as tf\n",
"from tensorflow.keras import backend as K\n",
"from tensorflow.keras.layers import Activation, Lambda, GlobalAveragePooling2D, concatenate\n",
"from tensorflow.keras.layers import UpSampling2D, Conv2D, Dropout, MaxPooling2D, Conv2DTranspose\n",
"from tensorflow.keras.layers import Dense, Flatten, Input\n",
"from tensorflow.keras.models import Model, Sequential, load_model\n",
"from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
working generators + model tests
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"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
unet added
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"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import cv2\n",
"import pickle\n",
"import random\n",
"import os"
]
},
{
"cell_type": "code",
working generators + model tests
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"execution_count": 2,
unet added
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"metadata": {},
"outputs": [],
"source": [
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"from src.metrics import IOU\n",
"from src.consts import JPG_IMAGES, RGB_DIR, MASK_DIR\n"
unet added
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]
},
{
"cell_type": "code",
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"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"Found 9399 images belonging to 1 classes.\n",
"Found 9399 images belonging to 1 classes.\n",
"Found 2349 images belonging to 1 classes.\n",
"Found 2349 images belonging to 1 classes.\n"
]
}
],
"source": [
"\n",
"# we create two instances with the same arguments\n",
"print(os.path.exists('./images/rgb'))\n",
"img_size = (512,512)\n",
"rgb_dir = os.path.join(\"images\", RGB_DIR)\n",
"mask_dir = os.path.join(\"images\", MASK_DIR)\n",
"\n",
"train_datagen = ImageDataGenerator(rescale=1 / 255.0,\n",
" horizontal_flip=True,\n",
" vertical_flip=True,\n",
" validation_split=0.2)\n",
"\n",
"# Provide the same seed and keyword arguments to the fit and flow methods\n",
"seed = 1\n",
"\n",
"image_generator = train_datagen.flow_from_directory(\n",
" './images/rgb',\n",
" class_mode=None,\n",
" # class_mode='binary',\n",
" seed=seed,\n",
" subset='training'\n",
" )\n",
"mask_generator = train_datagen.flow_from_directory(\n",
" './images/mask',\n",
" class_mode=None,\n",
" seed=seed,\n",
" subset='training'\n",
" )\n",
"image_generator_val = train_datagen.flow_from_directory(\n",
" './images/rgb',\n",
" class_mode=None,\n",
" # class_mode='binary',\n",
" seed=seed,\n",
" subset='validation'\n",
" )\n",
"mask_generator_val = train_datagen.flow_from_directory(\n",
" './images/mask',\n",
" class_mode=None,\n",
" seed=seed,\n",
" subset='validation'\n",
" )\n",
"\n",
"train_gen = zip(image_generator, mask_generator)\n",
"val_gen = zip(image_generator_val, mask_generator_val)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
unet added
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"metadata": {},
"outputs": [],
"source": [
"IMG_HEIGHT = 512\n",
"IMG_WIDTH = 512\n",
working generators + model tests
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"# img_dir = '/images'"
unet added
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]
},
{
"cell_type": "code",
working generators + model tests
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"execution_count": 5,
unet added
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"metadata": {},
"outputs": [],
"source": [
"EPOCHS = 30\n",
"batch_size = 16"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Unet model"
]
},
{
"cell_type": "code",
working generators + model tests
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"execution_count": 6,
unet added
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"metadata": {},
"outputs": [],
"source": [
"class Unet():\n",
" def __init__(self, num_classes=1):\n",
" self.num_classes=num_classes\n",
"\n",
" def build_model(self):\n",
" in1 = Input(shape=(IMG_HEIGHT, IMG_WIDTH, 3 ))\n",
"\n",
" conv1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(in1)\n",
" conv1 = Dropout(0.2)(conv1)\n",
" conv1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv1)\n",
" pool1 = MaxPooling2D((2, 2))(conv1)\n",
"\n",
" conv2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool1)\n",
" conv2 = Dropout(0.2)(conv2)\n",
" conv2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv2)\n",
" pool2 = MaxPooling2D((2, 2))(conv2)\n",
"\n",
" conv3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool2)\n",
" conv3 = Dropout(0.2)(conv3)\n",
" conv3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv3)\n",
" pool3 = MaxPooling2D((2, 2))(conv3)\n",
"\n",
" conv4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool3)\n",
" conv4 = Dropout(0.2)(conv4)\n",
" conv4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv4)\n",
"\n",
" up1 = concatenate([UpSampling2D((2, 2))(conv4), conv3], axis=-1)\n",
" conv5 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up1)\n",
" conv5 = Dropout(0.2)(conv5)\n",
" conv5 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv5)\n",
" \n",
" up2 = concatenate([UpSampling2D((2, 2))(conv5), conv2], axis=-1)\n",
" conv6 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up2)\n",
" conv6 = Dropout(0.2)(conv6)\n",
" conv6 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv6)\n",
"\n",
" up2 = concatenate([UpSampling2D((2, 2))(conv6), conv1], axis=-1)\n",
" conv7 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up2)\n",
" conv7 = Dropout(0.2)(conv7)\n",
" conv7 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv7)\n",
" segmentation = Conv2D(self.num_classes, (1, 1), activation='sigmoid', name='seg')(conv7)\n",
" #segmentation = Conv2D(3, (1, 1), activation='sigmoid', name='seg')(conv7)\n",
" model = Model(inputs=[in1], outputs=[segmentation])\n",
"\n",
" return model\n",
"\n",
" "
]
},
{
"cell_type": "code",
working generators + model tests
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"execution_count": 7,
unet added
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"metadata": {},
"outputs": [],
"source": [
"from src.loss import jaccard_loss"
]
},
{
"cell_type": "code",
working generators + model tests
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"execution_count": 15,
unet added
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"metadata": {},
"outputs": [
{
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"name": "stdout",
"output_type": "stream",
"text": [
"You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model/model_to_dot to work.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\masob\\AppData\\Local\\Temp\\ipykernel_21092\\68410389.py:22: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
" history = model.fit_generator(train_gen,\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/30\n"
]
},
{
"ename": "InvalidArgumentError",
"evalue": "Graph execution error:\n\nDetected at node 'gradient_tape/model_6/concatenate_18/ConcatOffset' defined at (most recent call last):\n File \"C:\\Users\\masob\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 197, in _run_module_as_main\n return _run_code(code, main_globals, None,\n File \"C:\\Users\\masob\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 87, in _run_code\n exec(code, run_globals)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\traitlets\\config\\application.py\", line 846, in launch_instance\n app.start()\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 677, in start\n self.io_loop.start()\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 199, in start\n self.asyncio_loop.run_forever()\n File \"C:\\Users\\masob\\AppData\\Local\\Programs\\Python\\Python39\\lib\\asyncio\\base_events.py\", line 596, in run_forever\n self._run_once()\n File \"C:\\Users\\masob\\AppData\\Local\\Programs\\Python\\Python39\\lib\\asyncio\\base_events.py\", line 1890, in _run_once\n handle._run()\n File \"C:\\Users\\masob\\AppData\\Local\\Programs\\Python\\Python39\\lib\\asyncio\\events.py\", line 80, in _run\n self._context.run(self._callback, *self._args)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 461, in dispatch_queue\n await self.process_one()\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 450, in process_one\n await dispatch(*args)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 357, in dispatch_shell\n await result\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 652, in execute_request\n reply_content = await reply_content\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 359, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 532, in run_cell\n return super().run_cell(*args, **kwargs)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2768, in run_cell\n result = self._run_cell(\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2814, in _run_cell\n return runner(coro)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n coro.send(None)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\IPython\\core\\inter
unet added
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"output_type": "error",
"traceback": [
working generators + model tests
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mc:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\unet.ipynb Cell 9'\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=13'>14</a>\u001b[0m model_name \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mmodels/unet.h5\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=14'>15</a>\u001b[0m modelcheckpoint \u001b[39m=\u001b[39m ModelCheckpoint(model_name,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=15'>16</a>\u001b[0m monitor\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mval_loss\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=16'>17</a>\u001b[0m mode\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mauto\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=17'>18</a>\u001b[0m verbose\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=18'>19</a>\u001b[0m save_best_only\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=21'>22</a>\u001b[0m history \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mfit_generator(train_gen,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=22'>23</a>\u001b[0m validation_data\u001b[39m=\u001b[39;49mval_gen,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=23'>24</a>\u001b[0m epochs\u001b[39m=\u001b[39;49mEPOCHS,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=24'>25</a>\u001b[0m steps_per_epoch\u001b[39m=\u001b[39;49m\u001b[39m100\u001b[39;49m,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=25'>26</a>\u001b[0m validation_steps \u001b[39m=\u001b[39;49m \u001b[39m100\u001b[39;49m,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=26'>27</a>\u001b[0m shuffle\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/unet.ipynb#ch0000007?line=27'>28</a>\u001b[0m )\n",
"File \u001b[1;32mc:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py:2209\u001b[0m, in \u001b[0;36mModel.fit_generator\u001b[1;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[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2197'>2198</a>\u001b[0m \u001b[39m\"\"\"Fits the model on data yielded batch-by-batch by a Python generator.\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2198'>2199</a>\u001b[0m \n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2199'>2200</a>\u001b[0m \u001b[39mDEPRECATED:\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2200'>2201</a>\u001b[0m \u001b[39m `Model.fit` now supports generators, so there is no longer any need to use\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2201'>2202</a>\u001b[0m \u001b[39m this endpoint.\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2202'>2203</a>\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2203'>2204</a>\u001b[0m warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2204'>2205</a>\u001b[0m \u001b[39m'\u001b[39m\u001b[39m`Model.fit_generator` is deprecated and \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2205'>2206</a>\u001b[0m \u001b[39m'\u001b[39m\u001b[39mwill be removed in a future version. \u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2206'>2207</a>\u001b[0m \u001b[39m'\u001b[39m\u001b[39mPlease use `Model.fit`, which supports generators.\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2207'>2208</a>\u001b[0m stacklevel\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)\n\u001b[1;32m-> <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2208'>2209</a>\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfit(\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2209'>2210</a>\u001b[0m generator,\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA
"File \u001b[1;32mc:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py:67\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/utils/traceback_utils.py?line=64'>65</a>\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/utils/traceback_utils.py?line=65'>66</a>\u001b[0m filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[1;32m---> <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/utils/traceback_utils.py?line=66'>67</a>\u001b[0m \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/utils/traceback_utils.py?line=67'>68</a>\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/utils/traceback_utils.py?line=68'>69</a>\u001b[0m \u001b[39mdel\u001b[39;00m filtered_tb\n",
"File \u001b[1;32mc:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/tensorflow/python/eager/execute.py?line=51'>52</a>\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/tensorflow/python/eager/execute.py?line=52'>53</a>\u001b[0m ctx\u001b[39m.\u001b[39mensure_initialized()\n\u001b[1;32m---> <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/tensorflow/python/eager/execute.py?line=53'>54</a>\u001b[0m tensors \u001b[39m=\u001b[39m pywrap_tfe\u001b[39m.\u001b[39mTFE_Py_Execute(ctx\u001b[39m.\u001b[39m_handle, device_name, op_name,\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/tensorflow/python/eager/execute.py?line=54'>55</a>\u001b[0m inputs, attrs, num_outputs)\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/tensorflow/python/eager/execute.py?line=55'>56</a>\u001b[0m \u001b[39mexcept\u001b[39;00m core\u001b[39m.\u001b[39m_NotOkStatusException \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m <a href='file:///c%3A/Users/masob/Desktop/STUDIA/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/tensorflow/python/eager/execute.py?line=56'>57</a>\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[1;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node 'gradient_tape/model_6/concatenate_18/ConcatOffset' defined at (most recent call last):\n File \"C:\\Users\\masob\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 197, in _run_module_as_main\n return _run_code(code, main_globals, None,\n File \"C:\\Users\\masob\\AppData\\Local\\Programs\\Python\\Python39\\lib\\runpy.py\", line 87, in _run_code\n exec(code, run_globals)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in <module>\n app.launch_new_instance()\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\traitlets\\config\\application.py\", line 846, in launch_instance\n app.start()\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 677, in start\n self.io_loop.start()\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 199, in start\n self.asyncio_loop.run_forever()\n File \"C:\\Users\\masob\\AppData\\Local\\Programs\\Python\\Python39\\lib\\asyncio\\base_events.py\", line 596, in run_forever\n self._run_once()\n File \"C:\\Users\\masob\\AppData\\Local\\Programs\\Python\\Python39\\lib\\asyncio\\base_events.py\", line 1890, in _run_once\n handle._run()\n File \"C:\\Users\\masob\\AppData\\Local\\Programs\\Python\\Python39\\lib\\asyncio\\events.py\", line 80, in _run\n self._context.run(self._callback, *self._args)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 461, in dispatch_queue\n await self.process_one()\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 450, in process_one\n await dispatch(*args)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 357, in dispatch_shell\n await result\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 652, in execute_request\n reply_content = await reply_content\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 359, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 532, in run_cell\n return super().run_cell(*args, **kwargs)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2768, in run_cell\n result = self._run_cell(\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2814, in _run_cell\n return runner(coro)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 129, in _pseudo_sync_runner\n coro.send(None)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\s
unet added
2022-02-16 16:51:52 +01:00
]
}
],
"source": [
"model = Unet(num_classes=1).build_model()\n",
"\n",
"compile_params ={\n",
" 'loss':jaccard_loss(smooth=90), \n",
" 'optimizer':'rmsprop',\n",
" 'metrics':[IOU]\n",
" }\n",
" \n",
" \n",
"model.compile(**compile_params)\n",
"\n",
"tf.keras.utils.plot_model(model, show_shapes=True)\n",
"\n",
"model_name = \"models/unet.h5\"\n",
"modelcheckpoint = ModelCheckpoint(model_name,\n",
" monitor='val_loss',\n",
" mode='auto',\n",
" verbose=1,\n",
" save_best_only=True)\n",
"\n",
"\n",
"history = model.fit_generator(train_gen,\n",
" validation_data=val_gen,\n",
" epochs=EPOCHS,\n",
" steps_per_epoch=100,\n",
" validation_steps = 100,\n",
" shuffle=True,\n",
")"
]
}
],
"metadata": {
"interpreter": {
"hash": "1be0d4179f0eed29061aece4de36a3b887fc5990f515aa1f08bfa33b9ed547bc"
},
"kernelspec": {
"display_name": "Python 3.9.7 64-bit ('widzenie-komputerowe-env': conda)",
"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",
working generators + model tests
2022-02-16 22:08:44 +01:00
"version": "3.9.0"
unet added
2022-02-16 16:51:52 +01:00
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in New Issue Copy Permalink