580 lines
53 KiB
Plaintext
580 lines
53 KiB
Plaintext
{
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"cells": [
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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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"import os\n",
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"os.chdir(os.getcwd() + '/..')"
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The autoreload extension is already loaded. To reload it, use:\n",
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" %reload_ext autoreload\n"
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]
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}
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],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2"
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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": 110,
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"metadata": {},
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"outputs": [],
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"source": [
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"from iss.tools import Config\n",
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"from iss.tools import Tools\n",
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"from iss.models import SimpleAutoEncoder\n",
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"from iss.models import SimpleConvAutoEncoder\n",
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"from iss.models import VarAutoEncoder\n",
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"from iss.models import VarConvAutoEncoder\n",
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"from iss.models.ModelTrainer import ModelTrainer\n",
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"import pandas as pd\n",
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"import datetime as dt\n",
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"import time\n",
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"import numpy as np\n",
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"from dotenv import find_dotenv, load_dotenv"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"load_dotenv(find_dotenv())\n",
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"cfg = Config(project_dir = os.getenv(\"PROJECT_DIR\"), mode = os.getenv(\"MODE\"))\n",
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"model_type = 'simple_conv'"
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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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"## 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": 82,
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"metadata": {},
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"outputs": [],
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"source": [
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"# model = VarAutoEncoder(cfg.get('models')[model_type])\n",
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"model = SimpleConvAutoEncoder(cfg.get('models')[model_type])"
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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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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"input_3 (InputLayer) (None, 27, 48, 3) 0 \n",
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"_________________________________________________________________\n",
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"encoder (Model) (None, 3, 6, 16) 25328 \n",
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"_________________________________________________________________\n",
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"decoder (Model) (None, 27, 48, 3) 13468463 \n",
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"=================================================================\n",
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"Total params: 13,493,791\n",
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"Trainable params: 13,493,337\n",
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"Non-trainable params: 454\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"model.model.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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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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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"input_3 (InputLayer) (None, 27, 48, 3) 0 \n",
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"_________________________________________________________________\n",
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"enc_conv_1 (Conv2D) (None, 27, 48, 64) 1792 \n",
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"_________________________________________________________________\n",
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"batch_normalization_8 (Batch (None, 27, 48, 64) 256 \n",
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"_________________________________________________________________\n",
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"activation_7 (Activation) (None, 27, 48, 64) 0 \n",
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"_________________________________________________________________\n",
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"max_pooling2d_4 (MaxPooling2 (None, 13, 24, 64) 0 \n",
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"_________________________________________________________________\n",
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"enc_conv_2 (Conv2D) (None, 13, 24, 32) 18464 \n",
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"_________________________________________________________________\n",
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"batch_normalization_9 (Batch (None, 13, 24, 32) 128 \n",
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"_________________________________________________________________\n",
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"activation_8 (Activation) (None, 13, 24, 32) 0 \n",
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"_________________________________________________________________\n",
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"max_pooling2d_5 (MaxPooling2 (None, 6, 12, 32) 0 \n",
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"_________________________________________________________________\n",
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"enc_conv_3 (Conv2D) (None, 6, 12, 16) 4624 \n",
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"_________________________________________________________________\n",
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"batch_normalization_10 (Batc (None, 6, 12, 16) 64 \n",
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"_________________________________________________________________\n",
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"activation_9 (Activation) (None, 6, 12, 16) 0 \n",
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"_________________________________________________________________\n",
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"max_pooling2d_6 (MaxPooling2 (None, 3, 6, 16) 0 \n",
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"=================================================================\n",
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"Total params: 25,328\n",
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"Trainable params: 25,104\n",
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"Non-trainable params: 224\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"model.encoder_model.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.save()"
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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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"## Collection Manager"
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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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"source": [
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"from iss.data.CollectionManager import CollectionManagerFromDirectory"
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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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"source": [
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"collection = CollectionManagerFromDirectory(config = cfg)"
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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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"## Data Loader"
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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": 67,
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"metadata": {},
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"outputs": [],
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"source": [
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"from iss.models.DataLoader import ImageDataGeneratorWrapper"
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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": 72,
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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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"Found 9537 images belonging to 1 classes.\n",
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"Found 2725 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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"data_loader = ImageDataGeneratorWrapper(cfg, model = model_type)"
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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": 113,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 114,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"10911744"
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]
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},
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"execution_count": 114,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 115,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(9472, 3, 6, 16)"
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]
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},
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"execution_count": 115,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"preds.shape"
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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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"## Model Trainer"
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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": 31,
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"metadata": {},
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"outputs": [],
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"source": [
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"trainer2 = ModelTrainer(model, data_loader, cfg.get('models')[model_type], callbacks=[])"
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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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"data": {
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"text/plain": [
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"[<keras.callbacks.CSVLogger at 0x7fb0feab8d68>,\n",
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" <keras.callbacks.ModelCheckpoint at 0x7fb0feab8be0>,\n",
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" <iss.models.Callbacks.DisplayPictureCallback at 0x7fb0feab82e8>]"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"trainer2.callbacks"
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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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"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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"2.2.4\n",
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"Epoch 1/3\n",
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"20/20 [==============================] - 66s 3s/step - loss: 0.5066 - val_loss: 0.6479\n",
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"ok\n"
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]
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},
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{
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"data": {
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"image/png": "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\n",
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"text/plain": [
|
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"<PIL.Image.Image image mode=RGB size=48x27 at 0x7FB0FC2685F8>"
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]
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},
|
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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\n",
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"text/plain": [
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"<PIL.Image.Image image mode=RGB size=48x27 at 0x7FB0FC268B38>"
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]
|
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},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 2/3\n",
|
|
"20/20 [==============================] - 48s 2s/step - loss: 0.4333 - val_loss: 0.5987\n",
|
|
"ok\n"
|
|
]
|
|
},
|
|
{
|
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"data": {
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"image/png": "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\n",
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"text/plain": [
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"<PIL.Image.Image image mode=RGB size=48x27 at 0x7FB0FC2685F8>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<PIL.Image.Image image mode=RGB size=48x27 at 0x7FB0FC2682E8>"
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]
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},
|
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"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 3/3\n",
|
|
"20/20 [==============================] - 48s 2s/step - loss: 0.4231 - val_loss: 0.4515\n",
|
|
"ok\n"
|
|
]
|
|
},
|
|
{
|
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"data": {
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"text/plain": [
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"<PIL.Image.Image image mode=RGB size=48x27 at 0x7FB0FC268668>"
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]
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},
|
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"metadata": {},
|
|
"output_type": "display_data"
|
|
},
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{
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"data": {
|
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"image/png": "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\n",
|
|
"text/plain": [
|
|
"<PIL.Image.Image image mode=RGB size=48x27 at 0x7FB0FC2682E8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"trainer2.train()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Prediction"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from iss.tools.tools import Tools "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"sample = data_loader.get_test_generator().next()[0][1]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 34,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"prediction = model.predict_one(x = sample)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<PIL.Image.Image image mode=RGB size=80x45 at 0x7FDC38132BE0>"
|
|
]
|
|
},
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"Tools.display_one_picture_scaled(sample)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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EkyukFoTAG7vDfNgJuwEHi84pBxUm2G5eitmBiAZGZEVBoUZ7fLldaP6q4Lxya6loECYf/Mzt0FgWSmeRGuBPEZBDtK8WMIGzDLVuSrxxBvFPBelsseYVXvLbi1WPfpoesgPRqeSowzLeYmpS/IPtyzqqucr1GLk7bKJcPmGLi9Aue4kgiuKeFijDFlhb6+BgfIsHFUg6E+dT4VFpiUvSHQkYOUBiaNvnpoJRhoa+l+wBrIE1ermVXiAyOYTC/JPB+TN7DF2ijS52WZCBhW4J2cXPpXck8KeYAcpIF9l5HDSjZZPOtb8LyKRQikDqMj9bxbyDhf1rHzZEp8av07AXYjhvNLVKy0b4ronqKYKbDxeuYSWo6d494AexUkoVUnT6wp+qTyqbC+3sZcFXyAIkdOBdY3dUOYY5sJWlwinQcJpDJMQbowvIDCtVZWESiNcN1Mi8ThSH6QILoEvqlCOCuJCCFVygNgjCRHjsJK9nJWvVIVZam48HYZkUNagFtKwBG9KC8a9GUXyzAwU8ULFqICZ2lvk1ZYz8e/Dih4sKaK1QaezWoIbehRAVfZFf1VDA2R9oJc9u8iLxpzfJCeE3CDLjUUJWBE4jCGn2tKw6TaD0qxOBEhAXqsJJjHx+r0eEuxlgDyM3jwL6pZdZFIGen98mf8FZCW0f7opV1QAR0zoTmZHLatBI6xPsZdhu+KAhhIf5+V8mhGfvJ1aNTEjaIzxMlvCYBQzIEKut8FzKE/+0UoJeleedqr5AhTzpFYEfAoTkuiYGFImntOQmKGZCA2cAERHJPRvmyshlgAjLTJwBbBwYIQS0w8Fg6kKk5FmyTkJaLG/FTA8IGYpkqLrgwN80qGBrB8txkdXc+DnZUA9GBK7oVkn+pAkNfV8Liafggbg7THIU4JVS7P2JKTA+/JftCxMFYDDVnjAwAz6HQmuTKKZQqUp1m6UM8PPDQBmCQhVMQxHsXSTW9V0OAAWElllSynoMFHGerzTto/osrlSeTGlrZwViT1sswnt/S+2B8fFBg4BJbLFsdEngF2Q+uLvw8c/eMpwQnDqInyJCLXv3lBWutbuxegM0C4bWtpH7k8IdFhvVo+7CPO2/8ZnyxIVDD4VALXYxmMD55bicZXcpiTeFIcr7+mwHtyRNkNRRsEkrO1ZRWhp+GBqZHrof7Ia3drkAt3QqquXDDZGVKl5YaaBm0vL7TRgmj0JZf89uabfIl8ibpawm0TjhuYUzUvKNF+/G6AmKNZXwcDbW2V/AEd/KguTigKjs5+A7S6qgiYCdzolbqHMhHTbInQIP1/ckc7tsCx7SBurblZ1fxrtU4cbN9S2glTooOcQVyQrmw0D+Gs/vnQfkNQtP6yFv5Xr/C63ymPw/o9rHU1oe0MQAAAAASUVORK5CYII=\n",
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"text/plain": [
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"<PIL.Image.Image image mode=RGB size=80x45 at 0x7FDC40091DD8>"
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]
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},
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"Tools.display_index_picture_scaled(prediction)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
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"metadata": {},
|
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"outputs": [
|
|
{
|
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"data": {
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"text/plain": [
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"array([[[ 2. , 2. , 2. ],\n",
|
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" [ 2. , 2. , 2. ],\n",
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" [ 2. , 2. , 2. ],\n",
|
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" ..., \n",
|
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" [ 23. , 23. , 23. ],\n",
|
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" [ 27.00000191, 21. , 21. ],\n",
|
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" [ 76. , 71. , 75. ]],\n",
|
|
"\n",
|
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" [[ 2. , 2. , 2. ],\n",
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" [ 2. , 2. , 2. ],\n",
|
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" [ 2. , 2. , 2. ],\n",
|
|
" ..., \n",
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" [ 65. , 59.00000381, 59.00000381],\n",
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" [ 72. , 67. , 71. ],\n",
|
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" [ 91. , 89. , 92. ]],\n",
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"\n",
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" [[ 2. , 2. , 2. ],\n",
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" [ 3.00000024, 3.00000024, 3.00000024],\n",
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" [ 2. , 2. , 2. ],\n",
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" ..., \n",
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" [ 74. , 64. , 65. ],\n",
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" [ 66. , 56.00000381, 57.00000381],\n",
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" [ 63.00000381, 58.00000381, 55.00000381]],\n",
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"\n",
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" ..., \n",
|
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" [[ 251.00001526, 254.00001526, 255. ],\n",
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" [ 248.00001526, 251.00001526, 255. ],\n",
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" [ 248.00001526, 249.00001526, 253.00001526],\n",
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" ..., \n",
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" [ 216.00001526, 228.00001526, 244.00001526],\n",
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" [ 199.00001526, 215.00001526, 228.00001526],\n",
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" [ 161. , 179. , 199.00001526]],\n",
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"\n",
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" [[ 250.00001526, 255. , 255. ],\n",
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" [ 230.00001526, 239.00001526, 248.00001526],\n",
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" [ 243.00001526, 251.00001526, 254.00001526],\n",
|
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" ..., \n",
|
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" [ 160. , 174. , 187. ],\n",
|
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" [ 154. , 166. , 180. ],\n",
|
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" [ 217.00001526, 231.00001526, 244.00001526]],\n",
|
|
"\n",
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" [[ 247.00001526, 255. , 255. ],\n",
|
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" [ 247.00001526, 255. , 255. ],\n",
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" [ 236.00001526, 243.00001526, 251.00001526],\n",
|
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" ..., \n",
|
|
" [ 132. , 148. , 171. ],\n",
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|
" [ 182. , 198.00001526, 213.00001526],\n",
|
|
" [ 203.00001526, 210.00001526, 220.00001526]]], dtype=float32)"
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]
|
|
},
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
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"source": [
|
|
"sample*255"
|
|
]
|
|
},
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|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
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|
"source": []
|
|
}
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|
],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": "3.6.8"
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}
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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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