293 lines
40 KiB
Plaintext
293 lines
40 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import shutil\n",
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"import tensorflow as tf\n",
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"from tensorflow.keras import backend as K\n",
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"from tensorflow.keras.layers import Activation, Lambda, GlobalAveragePooling2D, concatenate\n",
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"from tensorflow.keras.layers import UpSampling2D, Conv2D, Dropout, MaxPooling2D, Conv2DTranspose\n",
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"from tensorflow.keras.layers import Dense, Flatten, Input\n",
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"from tensorflow.keras.models import Model, Sequential, load_model\n",
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"from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
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"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import cv2\n",
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"import pickle\n",
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"import random\n",
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"import os"
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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": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.metrics import IOU\n",
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"from src.consts import JPG_IMAGES, RGB_DIR, MASK_DIR\n"
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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": 13,
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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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"True\n",
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"Found 9399 images belonging to 1 classes.\n",
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"Found 9399 images belonging to 1 classes.\n",
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"Found 2349 images belonging to 1 classes.\n",
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"Found 2349 images belonging to 1 classes.\n"
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]
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}
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],
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"source": [
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"\n",
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"# we create two instances with the same arguments\n",
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"print(os.path.exists('./images/rgb'))\n",
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"img_size = (512,512)\n",
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"rgb_dir = os.path.join(\"images\", RGB_DIR)\n",
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"mask_dir = os.path.join(\"images\", MASK_DIR)\n",
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"\n",
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"train_datagen = ImageDataGenerator(rescale=1 / 255.0,\n",
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" horizontal_flip=True,\n",
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" vertical_flip=True,\n",
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" validation_split=0.2)\n",
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"\n",
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"# Provide the same seed and keyword arguments to the fit and flow methods\n",
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"seed = 1\n",
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"\n",
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"image_generator = train_datagen.flow_from_directory(\n",
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" './images/rgb',\n",
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" class_mode=None,\n",
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" # class_mode='binary',\n",
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" seed=seed,\n",
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" subset='training'\n",
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" )\n",
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"mask_generator = train_datagen.flow_from_directory(\n",
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" './images/mask',\n",
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" class_mode=None,\n",
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" seed=seed,\n",
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" subset='training'\n",
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" )\n",
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"image_generator_val = train_datagen.flow_from_directory(\n",
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" './images/rgb',\n",
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" class_mode=None,\n",
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" # class_mode='binary',\n",
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" seed=seed,\n",
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" subset='validation'\n",
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" )\n",
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"mask_generator_val = train_datagen.flow_from_directory(\n",
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" './images/mask',\n",
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" class_mode=None,\n",
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" seed=seed,\n",
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" subset='validation'\n",
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" )\n",
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"\n",
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"train_gen = zip(image_generator, mask_generator)\n",
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"val_gen = zip(image_generator_val, mask_generator_val)\n"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"IMG_HEIGHT = 512\n",
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"IMG_WIDTH = 512\n",
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"# img_dir = '/images'"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"EPOCHS = 30\n",
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"batch_size = 16"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Unet model"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Unet():\n",
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" def __init__(self, num_classes=1):\n",
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" self.num_classes=num_classes\n",
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"\n",
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" def build_model(self):\n",
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" in1 = Input(shape=(IMG_HEIGHT, IMG_WIDTH, 3 ))\n",
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"\n",
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" conv1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(in1)\n",
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" conv1 = Dropout(0.2)(conv1)\n",
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" conv1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv1)\n",
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" pool1 = MaxPooling2D((2, 2))(conv1)\n",
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"\n",
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" conv2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool1)\n",
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" conv2 = Dropout(0.2)(conv2)\n",
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" conv2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv2)\n",
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" pool2 = MaxPooling2D((2, 2))(conv2)\n",
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"\n",
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" conv3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool2)\n",
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" conv3 = Dropout(0.2)(conv3)\n",
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" conv3 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv3)\n",
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" pool3 = MaxPooling2D((2, 2))(conv3)\n",
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"\n",
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" conv4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool3)\n",
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" conv4 = Dropout(0.2)(conv4)\n",
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" conv4 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv4)\n",
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"\n",
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" up1 = concatenate([UpSampling2D((2, 2))(conv4), conv3], axis=-1)\n",
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" conv5 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up1)\n",
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" conv5 = Dropout(0.2)(conv5)\n",
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" conv5 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv5)\n",
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" \n",
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" up2 = concatenate([UpSampling2D((2, 2))(conv5), conv2], axis=-1)\n",
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" conv6 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up2)\n",
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" conv6 = Dropout(0.2)(conv6)\n",
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" conv6 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv6)\n",
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"\n",
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" up2 = concatenate([UpSampling2D((2, 2))(conv6), conv1], axis=-1)\n",
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" conv7 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(up2)\n",
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" conv7 = Dropout(0.2)(conv7)\n",
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" conv7 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv7)\n",
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" segmentation = Conv2D(self.num_classes, (1, 1), activation='sigmoid', name='seg')(conv7)\n",
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" #segmentation = Conv2D(3, (1, 1), activation='sigmoid', name='seg')(conv7)\n",
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" model = Model(inputs=[in1], outputs=[segmentation])\n",
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"\n",
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" return model\n",
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"\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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"from src.loss import jaccard_loss"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"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"
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]
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},
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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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"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",
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" history = model.fit_generator(train_gen,\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/30\n"
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]
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},
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{
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"ename": "InvalidArgumentError",
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"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\\interactiveshell.py\", line 3012, in run_cell_async\n has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3191, in run_ast_nodes\n if await self.run_code(code, result, async_=asy):\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3251, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"C:\\Users\\masob\\AppData\\Local\\Temp\\ipykernel_21092\\68410389.py\", line 22, in <module>\n history = model.fit_generator(train_gen,\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 2209, in fit_generator\n return self.fit(\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 64, in error_handler\n return fn(*args, **kwargs)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1384, in fit\n tmp_logs = self.train_function(iterator)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1021, in train_function\n return step_function(self, iterator)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1010, in step_function\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1000, in run_step\n outputs = model.train_step(data)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 863, in train_step\n self.optimizer.minimize(loss, self.trainable_variables, tape=tape)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\optimizer_v2\\optimizer_v2.py\", line 530, in minimize\n grads_and_vars = self._compute_gradients(\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\optimizer_v2\\optimizer_v2.py\", line 583, in _compute_gradients\n grads_and_vars = self._get_gradients(tape, loss, var_list, grad_loss)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\optimizer_v2\\optimizer_v2.py\", line 464, in _get_gradients\n grads = tape.gradient(loss, var_list, grad_loss)\nNode: 'gradient_tape/model_6/concatenate_18/ConcatOffset'\nAll dimensions except 3 must match. Input 1 has shape [32 64 64 128] and doesn't match input 0 with shape [32 128 128 128].\n\t [[{{node gradient_tape/model_6/concatenate_18/ConcatOffset}}]] [Op:__inference_train_function_8358]",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)",
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"\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",
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"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/WIDZENIE%20KOMPUTEROWE/Projekt%20ON%20CLOUD/cloud-detection-challenge/venv/lib/site-packages/keras/engine/training.py?line=2210'>2211</a>\u001b[0m steps_per_epoch\u001b[39m=\u001b[39;49msteps_per_epoch,\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=2211'>2212</a>\u001b[0m epochs\u001b[39m=\u001b[39;49mepochs,\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=2212'>2213</a>\u001b[0m verbose\u001b[39m=\u001b[39;49mverbose,\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=2213'>2214</a>\u001b[0m callbacks\u001b[39m=\u001b[39;49mcallbacks,\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=2214'>2215</a>\u001b[0m validation_data\u001b[39m=\u001b[39;49mvalidation_data,\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=2215'>2216</a>\u001b[0m validation_steps\u001b[39m=\u001b[39;49mvalidation_steps,\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=2216'>2217</a>\u001b[0m validation_freq\u001b[39m=\u001b[39;49mvalidation_freq,\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=2217'>2218</a>\u001b[0m class_weight\u001b[39m=\u001b[39;49mclass_weight,\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=2218'>2219</a>\u001b[0m max_queue_size\u001b[39m=\u001b[39;49mmax_queue_size,\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=2219'>2220</a>\u001b[0m workers\u001b[39m=\u001b[39;49mworkers,\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=2220'>2221</a>\u001b[0m use_multiprocessing\u001b[39m=\u001b[39;49muse_multiprocessing,\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=2221'>2222</a>\u001b[0m shuffle\u001b[39m=\u001b[39;49mshuffle,\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=2222'>2223</a>\u001b[0m initial_epoch\u001b[39m=\u001b[39;49minitial_epoch)\n",
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"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",
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"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",
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"\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\\site-packages\\IPython\\core\\interactiveshell.py\", line 3012, in run_cell_async\n has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3191, in run_ast_nodes\n if await self.run_code(code, result, async_=asy):\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3251, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"C:\\Users\\masob\\AppData\\Local\\Temp\\ipykernel_21092\\68410389.py\", line 22, in <module>\n history = model.fit_generator(train_gen,\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 2209, in fit_generator\n return self.fit(\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 64, in error_handler\n return fn(*args, **kwargs)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1384, in fit\n tmp_logs = self.train_function(iterator)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1021, in train_function\n return step_function(self, iterator)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1010, in step_function\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 1000, in run_step\n outputs = model.train_step(data)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\engine\\training.py\", line 863, in train_step\n self.optimizer.minimize(loss, self.trainable_variables, tape=tape)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\optimizer_v2\\optimizer_v2.py\", line 530, in minimize\n grads_and_vars = self._compute_gradients(\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\optimizer_v2\\optimizer_v2.py\", line 583, in _compute_gradients\n grads_and_vars = self._get_gradients(tape, loss, var_list, grad_loss)\n File \"c:\\Users\\masob\\Desktop\\STUDIA\\WIDZENIE KOMPUTEROWE\\Projekt ON CLOUD\\cloud-detection-challenge\\venv\\lib\\site-packages\\keras\\optimizer_v2\\optimizer_v2.py\", line 464, in _get_gradients\n grads = tape.gradient(loss, var_list, grad_loss)\nNode: 'gradient_tape/model_6/concatenate_18/ConcatOffset'\nAll dimensions except 3 must match. Input 1 has shape [32 64 64 128] and doesn't match input 0 with shape [32 128 128 128].\n\t [[{{node gradient_tape/model_6/concatenate_18/ConcatOffset}}]] [Op:__inference_train_function_8358]"
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]
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}
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],
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"source": [
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"model = Unet(num_classes=1).build_model()\n",
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"\n",
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"compile_params ={\n",
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" 'loss':jaccard_loss(smooth=90), \n",
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" 'optimizer':'rmsprop',\n",
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" 'metrics':[IOU]\n",
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" }\n",
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" \n",
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" \n",
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"model.compile(**compile_params)\n",
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"\n",
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"tf.keras.utils.plot_model(model, show_shapes=True)\n",
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"\n",
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"model_name = \"models/unet.h5\"\n",
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"modelcheckpoint = ModelCheckpoint(model_name,\n",
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" monitor='val_loss',\n",
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" mode='auto',\n",
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" verbose=1,\n",
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" save_best_only=True)\n",
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"\n",
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"\n",
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"history = model.fit_generator(train_gen,\n",
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" validation_data=val_gen,\n",
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" epochs=EPOCHS,\n",
|
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" steps_per_epoch=100,\n",
|
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" validation_steps = 100,\n",
|
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" shuffle=True,\n",
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")"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "1be0d4179f0eed29061aece4de36a3b887fc5990f515aa1f08bfa33b9ed547bc"
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},
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"kernelspec": {
|
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"display_name": "Python 3.9.7 64-bit ('widzenie-komputerowe-env': conda)",
|
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"language": "python",
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|
"name": "python3"
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},
|
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"language_info": {
|
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"codemirror_mode": {
|
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.0"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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