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satnogs-wut
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cleanup wut-web

master 0.67
ml server 2020-01-21 19:26:06 -07:00
parent b6af3f41d8
commit e0df22c385
1 changed files with 28 additions and 196 deletions
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@ -6,19 +6,10 @@
"metadata": {},
"outputs": [],
"source": [
"# wut-predict --- What U Think? SatNOGS Observation AI, makes predictions.\n",
"# wut-web --- What U Think? Web App: SatNOGS Observation AI, makes predictions.\n",
"#\n",
"# https://spacecruft.org/spacecruft/satnogs-wut\n",
"# Based on data/train and data/val directories builds a wut.h5 file.\n",
"# Reads wut.h5 and tests files in data/test/unvetted/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"# GPLv3+"
]
},
@ -28,42 +19,10 @@
"metadata": {},
"outputs": [],
"source": [
"# Built using Jupyter, Tensorflow, Keras"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow.python.keras"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import os\n",
"import numpy as np\n",
"import tensorflow.python.keras\n",
"from tensorflow.python.keras import Sequential\n",
"from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense\n",
"from tensorflow.python.keras.preprocessing.image import ImageDataGenerator\n",
@ -72,60 +31,19 @@
"from tensorflow.python.keras.preprocessing import image\n",
"from tensorflow.python.keras.models import load_model\n",
"from tensorflow.python.keras.preprocessing.image import load_img\n",
"from tensorflow.python.keras.preprocessing.image import img_to_array"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.python.keras.preprocessing.image import img_to_array\n",
"from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D\n",
"# Visualization\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.decomposition import PCA"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"# Seaborn pip dependency\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"# Interact\n",
"# https://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html\n",
"from __future__ import print_function\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\n",
"import ipywidgets as widgets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import ipywidgets as widgets\n",
"# Display Images\n",
"from IPython.display import display, Image"
]
@ -136,24 +54,13 @@
"metadata": {},
"outputs": [],
"source": [
"model = load_model('data/models/wut-DUV-201912.tf')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_dir = os.path.join('data/', 'test')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"IMG_HEIGHT = 416\n",
"IMG_WIDTH= 804\n",
"batch_size = 32\n",
"base_dir = ('/srv/satnogs/data')\n",
"test_dir = os.path.join(base_dir, 'test')\n",
"model_file = os.path.join(base_dir, 'models', 'wut-DUV-201912.tf')\n",
"print(model_file)\n",
"num_test = len(os.listdir(test_dir))"
]
},
@ -163,26 +70,7 @@
"metadata": {},
"outputs": [],
"source": [
"# Good results\n",
"#batch_size = 128\n",
"#epochs = 6\n",
"# Testing, faster more inaccurate results\n",
"batch_size = 32\n",
"epochs = 3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Half size\n",
"IMG_HEIGHT = 416\n",
"IMG_WIDTH= 804\n",
"# Full size, machine barfs probably needs more RAM\n",
"#IMG_HEIGHT = 832\n",
"#IMG_WIDTH = 1606"
"model = load_model(model_file)"
]
},
{
@ -193,15 +81,7 @@
"source": [
"test_image_generator = ImageDataGenerator(\n",
" rescale=1./255\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
")\n",
"test_data_gen = test_image_generator.flow_from_directory(batch_size=batch_size,\n",
" directory=test_dir,\n",
" target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
@ -214,16 +94,7 @@
"metadata": {},
"outputs": [],
"source": [
"sample_test_images, _ = next(test_data_gen)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 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",
"sample_test_images, _ = next(test_data_gen)\n",
"def plotImages(images_arr):\n",
" fig, axes = plt.subplots(1, 3, figsize=(20,20))\n",
" axes = axes.flatten()\n",
@ -231,16 +102,9 @@
" ax.imshow(img)\n",
" ax.axis('off')\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plotImages(sample_test_images[0:1])"
" plt.show()\n",
"\n",
"plotImages(sample_test_images[0:3])"
]
},
{
@ -252,42 +116,10 @@
"prediction = model.predict(\n",
" x=test_data_gen,\n",
" verbose=0\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"predictions=[]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prediction_bool = (prediction >0.8)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"predictions = prediction_bool.astype(int)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
")\n",
"predictions=[]\n",
"prediction_bool = (prediction >0.8)\n",
"predictions = prediction_bool.astype(int)\n",
"if prediction_bool[0] == False:\n",
" rating = 'bad'\n",
"else:\n",