jupyter
ml server
parent
2cc2f87962
commit
e033515ace
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@ -2,15 +2,14 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"#!/usr/bin/python3\n",
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"# wut-ml\n",
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"# wut-ml.ipynb\n",
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"#\n",
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"# Vet a SatNOGS image using machine learning (guessing).\n",
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"# It will vet the image located at test/unvetted/waterfall.png.\n",
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"# It will vet the image located at data/test/unvetted/waterfall.png.\n",
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"#\n",
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"# Note, there is an issue to fix where it will vet everything\n",
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"# under the data/test directory, so fix that. For now, just delete\n",
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@ -24,7 +23,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -40,7 +39,7 @@
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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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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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@ -57,7 +56,114 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"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": 6,
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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": 7,
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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": 8,
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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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"# XXX\n",
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"from tensorflow.python.keras.models import Model\n",
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"from tensorflow.python.keras.layers import Input, concatenate\n",
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"#from tensorflow.python.keras.optimizers import Adam\n",
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"\n",
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"# XXX Plot\n",
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"from tensorflow.python.keras.utils import plot_model\n",
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"from tensorflow.python.keras.callbacks import ModelCheckpoint"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"## for visualizing\n",
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"#%pylab\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": 11,
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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": 12,
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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": 13,
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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": 14,
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"metadata": {},
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"outputs": [
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{
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@ -69,37 +175,24 @@
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}
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],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"import tensorflow.python.keras\n",
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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\n",
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"\n",
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"# XXX\n",
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"from tensorflow.python.keras.models import Model\n",
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"from tensorflow.python.keras.layers import Input, concatenate\n",
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"#from tensorflow.python.keras.optimizers import Adam\n",
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"\n",
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"# XXX Plot\n",
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"from tensorflow.python.keras.utils import plot_model\n",
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"from tensorflow.python.keras.callbacks import ModelCheckpoint\n",
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"## for visualizing \n",
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"import matplotlib.pyplot as plt, numpy as np\n",
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"from sklearn.decomposition import PCA\n",
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"\n",
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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": 33,
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Should create interactive slider, I guess not\n",
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"#def f(x):\n",
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"# return x\n",
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"#interact(f, x=10);"
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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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@ -115,57 +208,103 @@
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"# TODO:\n",
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"# * Pre-process image\n",
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"print(\"datagen\")\n",
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"datagen = ImageDataGenerator(\n",
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"\tfeaturewise_center=False,\n",
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"\tsamplewise_center=False,\n",
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"\tfeaturewise_std_normalization=False,\n",
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"\tsamplewise_std_normalization=False,\n",
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"\tzca_whitening=False,\n",
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"\tzca_epsilon=1e-06,\n",
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"\trescale=1./255,\n",
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"\tshear_range=0.0,\n",
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"\tzoom_range=0.0,\n",
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"\trotation_range=0,\n",
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"\twidth_shift_range=0.0,\n",
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"\theight_shift_range=0.0,\n",
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"\tbrightness_range=None,\n",
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"\tchannel_shift_range=0.0,\n",
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"\tfill_mode='nearest',\n",
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"\tcval=0.0,\n",
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"\thorizontal_flip=False,\n",
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"\tvertical_flip=False,\n",
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"\tpreprocessing_function=None,\n",
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"\tdata_format='channels_last',\n",
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"\tvalidation_split=0.0,\n",
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"\tdtype='float32')"
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"\n",
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"datagen = ImageDataGenerator(dtype='float32', zca_epsilon=1e-06, data_format='channels_last')\n",
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"\n",
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"#datagen = ImageDataGenerator(\n",
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"#\tfeaturewise_center=False,\n",
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"#\tsamplewise_center=False,\n",
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"#\tfeaturewise_std_normalization=False,\n",
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"#\tsamplewise_std_normalization=False,\n",
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"#\tzca_whitening=False,\n",
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"#\tzca_epsilon=1e-06,\n",
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"#\trescale=1./255,\n",
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"#\tshear_range=0.0,\n",
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"#\tzoom_range=0.0,\n",
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"#\trotation_range=0,\n",
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"#\twidth_shift_range=0.0,\n",
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"#\theight_shift_range=0.0,\n",
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"#\tbrightness_range=None,\n",
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"#\tchannel_shift_range=0.0,\n",
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"#\tfill_mode='nearest',\n",
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"#\tcval=0.0,\n",
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"#\thorizontal_flip=False,\n",
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"#\tvertical_flip=False,\n",
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"#\tpreprocessing_function=None,\n",
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"#\tdata_format='channels_last',\n",
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"#\tvalidation_split=0.0,\n",
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"#\tdtype='float32')"
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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": 34,
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"execution_count": 17,
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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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"datagen.flow\n",
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"Found 761 images belonging to 4 classes.\n",
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"Found 741 images belonging to 3 classes.\n",
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"import data/train/\n",
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"Found 761 images belonging to 4 classes.\n"
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]
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}
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],
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"source": [
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"print(\"import data/train/\")\n",
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"train_it = datagen.flow_from_directory('data/train/', 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": 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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"import data/val/\n",
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"Found 741 images belonging to 3 classes.\n"
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]
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}
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],
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"source": [
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"print(\"import data/val/\")\n",
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"val_it = datagen.flow_from_directory('data/val/', 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": 19,
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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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"import data/test/\n",
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"Found 1 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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"print(\"datagen.flow\")\n",
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"train_it = datagen.flow_from_directory('data/train/', class_mode='binary')\n",
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"val_it = datagen.flow_from_directory('data/val/', class_mode='binary')\n",
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"print(\"import data/test/\")\n",
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"test_it = datagen.flow_from_directory('data/test/', 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": 35,
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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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"#display(Image('data/test/unvetted/waterfall.png'))"
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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": 21,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"train_it.next()\n",
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"Batch shape=(32, 256, 256, 3), min=0.000, max=1.000\n"
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"Batch shape=(32, 256, 256, 3), min=0.000, max=255.000\n"
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]
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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": 36,
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"execution_count": 22,
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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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"Batch shape=(32, 256, 256, 3), min=0.000, max=1.000\n"
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"Batch shape=(32, 256, 256, 3), min=0.000, max=255.000\n"
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]
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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": 37,
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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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"Batch shape=(1, 256, 256, 3), min=0.000, max=1.000\n"
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"Batch shape=(1, 256, 256, 3), min=0.000, max=255.000\n"
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]
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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": 38,
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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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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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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": 40,
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"execution_count": 26,
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"metadata": {},
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"outputs": [
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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": 41,
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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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": 42,
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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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": 43,
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"execution_count": 29,
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"metadata": {},
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"outputs": [
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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": 44,
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"execution_count": 30,
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"metadata": {},
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"outputs": [
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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": 45,
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"execution_count": 31,
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"metadata": {},
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"outputs": [
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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": 46,
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"execution_count": 32,
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"metadata": {},
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"outputs": [
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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": 47,
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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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": 48,
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"execution_count": 34,
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"metadata": {},
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"outputs": [
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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": 49,
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"execution_count": 35,
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"metadata": {},
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"outputs": [
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{
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},
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{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -470,7 +609,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -489,7 +628,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 52,
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -507,7 +646,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -526,7 +665,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -544,7 +683,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -562,7 +701,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -588,7 +727,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"execution_count": 43,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -596,8 +735,8 @@
|
|||
"output_type": "stream",
|
||||
"text": [
|
||||
"Fit Start\n",
|
||||
"Train for 24 steps, validate for 24 steps\n",
|
||||
"24/24 [==============================] - 60s 3s/step - loss: 0.0000e+00 - accuracy: 0.1209 - val_loss: 0.0000e+00 - val_accuracy: 0.0013\n",
|
||||
"Train for 4 steps, validate for 4 steps\n",
|
||||
"4/4 [==============================] - 34s 9s/step - loss: 0.0000e+00 - accuracy: 0.1328 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00\n",
|
||||
"Fit Done\n"
|
||||
]
|
||||
}
|
||||
|
|
@ -618,8 +757,8 @@
|
|||
"\tclass_weight=None,\n",
|
||||
"\tsample_weight=None,\n",
|
||||
"\tinitial_epoch=0,\n",
|
||||
"\tsteps_per_epoch=None,\n",
|
||||
"\tvalidation_steps=None,\n",
|
||||
"\tsteps_per_epoch=4,\n",
|
||||
"\tvalidation_steps=4,\n",
|
||||
"\tvalidation_freq=1,\n",
|
||||
"\tmax_queue_size=10,\n",
|
||||
"\tworkers=16,\n",
|
||||
|
|
@ -629,7 +768,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -638,7 +777,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -656,7 +795,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -671,13 +810,14 @@
|
|||
"# TODO:\n",
|
||||
"# * Generate output to visualize training/validating/testing.\n",
|
||||
"# Plot, fail\n",
|
||||
"#%matplotlib inline\n",
|
||||
"print(\"Plot\")\n",
|
||||
"#plot_model(test_it,dpi=72)"
|
||||
"#plot_model(test_it)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"execution_count": 47,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -685,18 +825,22 @@
|
|||
"output_type": "stream",
|
||||
"text": [
|
||||
"predict\n",
|
||||
"1/1 - 0s\n"
|
||||
"1/2 [==============>...............] - ETA: 0sWARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches (in this case, 2 batches). You may need to use the repeat() function when building your dataset.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# https://keras.io/models/sequential/\n",
|
||||
"print(\"predict\")\n",
|
||||
"\n",
|
||||
"#prediction = model.predict(x=test_it, batch_size=None, verbose=1, steps=None, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False)\n",
|
||||
"#prediction = model.predict(x=test_it, batch_size=None, verbose=1, steps=None, use_multiprocessing=True)\n",
|
||||
"\n",
|
||||
"prediction = model.predict(\n",
|
||||
"\tx=test_it,\n",
|
||||
"\tbatch_size=None,\n",
|
||||
"\tverbose=1,\n",
|
||||
"\tsteps=None,\n",
|
||||
"\tsteps=2,\n",
|
||||
"\tcallbacks=None,\n",
|
||||
"\tmax_queue_size=10,\n",
|
||||
"\tworkers=16,\n",
|
||||
|
|
@ -705,15 +849,14 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[1.]]\n",
|
||||
"Observation: bad\n"
|
||||
"[[1.]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -724,9 +867,17 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Observation: bad\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Make final prediction\n",
|
||||
"if prediction[0][0] == 1:\n",
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@
|
|||
# wut-dl-sort [Minimum Observation ID] [Maximum Observation ID]
|
||||
# Example:
|
||||
# wut-dl-sort 1467000 1470000
|
||||
# wut-dl-sort 1292461 1470525
|
||||
#
|
||||
# * Takes the files in the download/ dir.
|
||||
# * Looks at the JSON files to see if it is :good", "bad", or "failed".
|
||||
|
|
|
|||
Loading…
Reference in New Issue