ml server
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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-predict --- What U Think? SatNOGS Observation AI, makes predictions.\n",
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"#\n",
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"# https://spacecruft.org/spacecruft/satnogs-wut\n",
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"# Based on data/train and data/val directories builds a wut.h5 file.\n",
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"# Reads wut.h5 and tests files in data/test/unvetted/"
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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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"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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"# 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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"model = load_model('data/models/wut-DUV-201912.tf')"
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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_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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"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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"# Good results\n",
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"#batch_size = 128\n",
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"#epochs = 6\n",
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"# Testing, faster more inaccurate results\n",
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"batch_size = 32\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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"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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"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_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_test_images[0:1])"
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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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"prediction = model.predict(\n",
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" x=test_data_gen,\n",
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" verbose=0\n",
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")\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"predictions=[]"
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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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"prediction_bool = (prediction >0.8)\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"predictions = prediction_bool.astype(int)\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"if prediction_bool[0] == False:\n",
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" rating = 'bad'\n",
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"else:\n",
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" rating = 'good'\n",
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"print('Observation: %s' % (rating))"
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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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"# The End"
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]
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}
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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": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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