some cleanup, tweaks
ml server
parent
4d23a231b0
commit
0213f93029
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@ -21,43 +21,11 @@
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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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"from __future__ import print_function\n",
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"import os\n",
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"import datetime"
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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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"import datetime\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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@ -66,70 +34,21 @@
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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.preprocessing.image import img_to_array\n",
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"from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D\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"
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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.layers import Input, concatenate\n",
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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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"from sklearn.decomposition import PCA\n",
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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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"import seaborn as sns\n",
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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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"import ipywidgets as widgets\n",
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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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@ -140,16 +59,11 @@
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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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"ENCODING='GMSK'"
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"ENCODING='GMSK'\n",
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"batch_size = 128\n",
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"epochs = 4\n",
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"IMG_WIDTH = 416\n",
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"IMG_HEIGHT = 803"
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]
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},
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{
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@ -158,56 +72,17 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"train_dir = os.path.join('/srv/satnogs/data/txmodes', ENCODING )\n",
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"train_dir = os.path.join('/srv/satnogs/data/txmodes', ENCODING, 'train')\n",
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"val_dir = os.path.join('/srv/satnogs/data/txmodes', ENCODING, '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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"val_dir = os.path.join('/srv/satnogs/data/txmodes', ENCODING, 'val')\n",
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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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"train_bad_dir = os.path.join(train_dir, 'bad')\n",
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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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"val_bad_dir = os.path.join(val_dir, 'bad')\n",
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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_train_bad = len(os.listdir(train_bad_dir))\n",
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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_val_bad = len(os.listdir(val_bad_dir))\n",
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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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@ -220,39 +95,19 @@
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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(\"--\")\n",
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"print(\"Total training images:\", total_train)\n",
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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)\n",
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"print(\"Reduce training and validation set when testing\")\n",
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"#total_train = 100\n",
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"#total_val = 100\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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"batch_size = 128\n",
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"epochs = 16"
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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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"IMG_WIDTH = 416\n",
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"IMG_HEIGHT = 803"
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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)\n",
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"#print(\"Reduce training and validation set when testing\")\n",
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"total_train = 100\n",
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"total_val = 100\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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@ -363,7 +218,8 @@
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"log_dir = \"logs\"\n",
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"#log_dir=\"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
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"#tensorboard_callback = tensorflow.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
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"tensorboard_callback = tensorflow.keras.callbacks.TensorBoard(log_dir=log_dir)"
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"#tensorboard_callback = tensorflow.keras.callbacks.TensorBoard(log_dir=log_dir)\n",
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"tensorboard_callback = tensorflow.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, write_graph=True, write_images=True, embeddings_freq=1, update_freq='batch')"
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]
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},
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{
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@ -391,9 +247,14 @@
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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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"#wutoptimizer = 'adam'\n",
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"wutoptimizer = tensorflow.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=True)\n",
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"wutloss = 'binary_crossentropy'\n",
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"#wutmetrics = 'accuracy'\n",
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"wutmetrics = ['accuracy']\n",
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"model.compile(optimizer=wutoptimizer,\n",
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" loss=wutloss,\n",
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" metrics=[wutmetrics])"
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]
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},
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{
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@ -429,14 +290,16 @@
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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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"history = model.fit(\n",
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" train_data_gen,\n",
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" steps_per_epoch=total_train // batch_size,\n",
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" epochs=epochs,\n",
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" verbose=1,\n",
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" callbacks=[tensorboard_callback],\n",
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" validation_data=val_data_gen,\n",
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" validation_steps=total_val // batch_size,\n",
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" callbacks=[tensorboard_callback],\n",
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" verbose=1\n",
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" shuffle=True,\n",
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" use_multiprocessing=False\n",
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")"
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]
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},
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