no tmp notebooks
jebba
parent
395c31a82a
commit
5b1c84eb4c
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@ -1,3 +1,4 @@
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.ipynb_checkpoints
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.~lock.*#
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*.swp
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samples
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*.swp
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@ -1,774 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# wut --- What U Think? SatNOGS Observation AI.\n",
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"#\n",
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"# https://spacecruft.org/spacecruft/satnogs-wut"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# GPLv3+"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Built using Jupyter, Tensorflow, Keras"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Other data to consider adding:\n",
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"# * JSON metadata\n",
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"# * TLE\n",
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"# * Audio File (ogg)\n",
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"# https://www.tensorflow.org/io/api_docs/python/tfio/ffmpeg/AudioDataset\n",
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"# * Decoded Data (HEX, ASCII, PNG)\n",
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"# Data from external sources to consider adding:\n",
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"# * Weather"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Start\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import tensorflow.python.keras"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tensorflow.python.keras import Sequential\n",
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"from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense\n",
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"from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
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"from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\n",
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"from tensorflow.python.keras import optimizers\n",
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"from tensorflow.python.keras.preprocessing import image\n",
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"from tensorflow.python.keras.models import load_model\n",
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"from tensorflow.python.keras.preprocessing.image import load_img\n",
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"from tensorflow.python.keras.preprocessing.image import img_to_array"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tensorflow.python.keras.models import Model\n",
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"from tensorflow.python.keras.layers import Input, concatenate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Visualization\n",
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.decomposition import PCA"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Seaborn pip dependency\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Interact\n",
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"# https://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html\n",
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"from __future__ import print_function\n",
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"from ipywidgets import interact, interactive, fixed, interact_manual\n",
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"import ipywidgets as widgets"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Display Images\n",
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"from IPython.display import display, Image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Python import done\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_dir = os.path.join('data/', 'train')\n",
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"val_dir = os.path.join('data/', 'val')\n",
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"test_dir = os.path.join('data/', 'test')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_good_dir = os.path.join(train_dir, 'good')\n",
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"train_bad_dir = os.path.join(train_dir, 'bad')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"val_good_dir = os.path.join(val_dir, 'good')\n",
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"val_bad_dir = os.path.join(val_dir, 'bad')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"num_train_good = len(os.listdir(train_good_dir))\n",
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"num_train_bad = len(os.listdir(train_bad_dir))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"num_val_good = len(os.listdir(val_good_dir))\n",
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"num_val_bad = len(os.listdir(val_bad_dir))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"num_test = len(os.listdir(test_dir))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"total_train = num_train_good + num_train_bad\n",
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"total_val = num_val_good + num_val_bad"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print('total training good images:', num_train_good)\n",
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"print('total training bad images:', num_train_bad)\n",
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"print(\"--\")\n",
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"print(\"Total training images:\", total_train)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print('total validation good images:', num_val_good)\n",
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"print('total validation bad images:', num_val_bad)\n",
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"print(\"--\")\n",
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"print(\"Total validation images:\", total_val)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(\"Reduce training and validation set when testing\")\n",
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"#total_train = 500\n",
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"#total_val = 500\n",
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"print(\"Train =\")\n",
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"print(total_train)\n",
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"print(\"Validation =\")\n",
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"print(total_val)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Good results\n",
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"#batch_size = 128\n",
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"#epochs = 6\n",
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"#\n",
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"# Testing, faster more inaccurate results\n",
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"batch_size = 96\n",
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"epochs = 3"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Half size\n",
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"IMG_HEIGHT = 416\n",
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"IMG_WIDTH= 804\n",
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"# Full size, machine barfs probably needs more RAM\n",
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"#IMG_HEIGHT = 832\n",
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"#IMG_WIDTH = 1606"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_image_generator = ImageDataGenerator(\n",
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" rescale=1./255\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"val_image_generator = ImageDataGenerator(\n",
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" rescale=1./255\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"test_image_generator = ImageDataGenerator(\n",
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" rescale=1./255\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,\n",
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" directory=train_dir,\n",
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" shuffle=True,\n",
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" target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
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" class_mode='binary')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"val_data_gen = val_image_generator.flow_from_directory(batch_size=batch_size,\n",
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" directory=val_dir,\n",
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" target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
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" class_mode='binary')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(test_dir)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"test_data_gen = test_image_generator.flow_from_directory(batch_size=batch_size,\n",
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" directory=test_dir,\n",
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" target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
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" class_mode='binary')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sample_train_images, _ = next(train_data_gen)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sample_val_images, _ = next(val_data_gen)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sample_test_images, _ = next(test_data_gen)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# This function will plot images in the form of a grid with 1 row and 3 columns where images are placed in each column.\n",
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"def plotImages(images_arr):\n",
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" fig, axes = plt.subplots(1, 3, figsize=(20,20))\n",
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" axes = axes.flatten()\n",
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" for img, ax in zip( images_arr, axes):\n",
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" ax.imshow(img)\n",
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" ax.axis('off')\n",
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" plt.tight_layout()\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plotImages(sample_train_images[0:3])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plotImages(sample_val_images[0:3])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"plotImages(sample_test_images[0:3])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = Sequential([\n",
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" Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),\n",
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" MaxPooling2D(),\n",
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" Conv2D(32, 3, padding='same', activation='relu'),\n",
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" MaxPooling2D(),\n",
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" Conv2D(64, 3, padding='same', activation='relu'),\n",
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" MaxPooling2D(),\n",
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" Flatten(),\n",
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" Dense(512, activation='relu'),\n",
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" Dense(1, activation='sigmoid')\n",
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"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.compile(optimizer='adam',\n",
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" loss='binary_crossentropy',\n",
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" metrics=['accuracy'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"Image.LOAD_TRUNCATED_IMAGES = True"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"history = model.fit_generator(\n",
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" train_data_gen,\n",
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||||
" steps_per_epoch=total_train // batch_size,\n",
|
||||
" epochs=epochs,\n",
|
||||
" validation_data=val_data_gen,\n",
|
||||
" validation_steps=total_val // batch_size,\n",
|
||||
" verbose=1\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"acc = history.history['accuracy']\n",
|
||||
"val_acc = history.history['val_accuracy']\n",
|
||||
"\n",
|
||||
"loss = history.history['loss']\n",
|
||||
"val_loss = history.history['val_loss']\n",
|
||||
"\n",
|
||||
"epochs_range = range(epochs)\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(8, 8))\n",
|
||||
"plt.subplot(1, 2, 1)\n",
|
||||
"plt.plot(epochs_range, acc, label='Training Accuracy')\n",
|
||||
"plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
|
||||
"plt.legend(loc='lower right')\n",
|
||||
"plt.title('Training and Validation Accuracy')\n",
|
||||
"\n",
|
||||
"plt.subplot(1, 2, 2)\n",
|
||||
"plt.plot(epochs_range, loss, label='Training Loss')\n",
|
||||
"plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
|
||||
"plt.legend(loc='upper right')\n",
|
||||
"plt.title('Training and Validation Loss')\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"TRAINING info\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(train_dir)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(train_good_dir)\n",
|
||||
"print(train_bad_dir)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(train_image_generator)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(train_data_gen)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#print(sample_train_images)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(history)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.save('data/wut-CW.h5')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# https://keras.io/models/sequential/\n",
|
||||
"print(\"predict\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pred=model.predict_generator(test_data_gen,\n",
|
||||
"steps=1,\n",
|
||||
"verbose=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prediction = model.predict(\n",
|
||||
" x=test_data_gen,\n",
|
||||
" verbose=2\n",
|
||||
")\n",
|
||||
"print(\"end predict\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predictions=[]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Show prediction score\n",
|
||||
"print(prediction)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prediction_bool = (prediction >0.8)\n",
|
||||
"print(prediction_bool)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"predictions = prediction_bool.astype(int)\n",
|
||||
"print(predictions)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Make final prediction\n",
|
||||
"# XXX, display name, display all of them with mini waterfall, etc.\n",
|
||||
"if prediction_bool[0] == False:\n",
|
||||
" rating = 'bad'\n",
|
||||
"else:\n",
|
||||
" rating = 'good'\n",
|
||||
"print('Observation: %s' % (rating))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if prediction_bool[1] == False:\n",
|
||||
" rating = 'bad'\n",
|
||||
"else:\n",
|
||||
" rating = 'good'\n",
|
||||
"print('Observation: %s' % (rating))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if prediction_bool[2] == False:\n",
|
||||
" rating = 'bad'\n",
|
||||
"else:\n",
|
||||
" rating = 'good'\n",
|
||||
"print('Observation: %s' % (rating))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# The End"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
Loading…
Reference in New Issue