ml server
parent
1a30e049be
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"outputs": [],
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@ -37,24 +37,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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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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"Start\n"
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]
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}
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],
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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": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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@ -63,7 +55,7 @@
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"cell_type": "code",
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"execution_count": 5,
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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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@ -81,7 +73,7 @@
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{
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"cell_type": "code",
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"execution_count": 6,
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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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"cell_type": "code",
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"execution_count": 7,
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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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@ -107,7 +99,7 @@
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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@ -123,7 +115,7 @@
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{
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"cell_type": "code",
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"execution_count": 9,
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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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@ -137,7 +129,7 @@
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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{
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"cell_type": "code",
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"execution_count": 11,
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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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@ -160,7 +152,7 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -170,24 +162,16 @@
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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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"execution_count": null,
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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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"Python import done\n"
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]
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}
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],
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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": 14,
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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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@ -199,17 +183,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": null,
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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\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# https://keras.io/preprocessing/image/\n",
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"# TODO:\n",
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@ -245,18 +221,18 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_dir = os.path.join('data/', 'train')\n",
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"val_dir = os.path.join('data/', 'val')\n",
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"test_dir = os.path.join('data/', 'test')"
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"test_dir = os.path.join('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": 17,
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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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@ -267,7 +243,7 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -278,7 +254,17 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#data/test/unvetted/waterfall.png\n",
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"test_img = os.path.join(test_dir, '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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -289,7 +275,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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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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@ -300,7 +286,7 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -309,7 +295,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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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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@ -321,20 +307,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": null,
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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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"total training good images: 5754\n",
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"total training bad images: 1355\n",
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"--\n",
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"Total training images: 7109\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print('total training good images:', num_train_good)\n",
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"print('total training bad images:', num_train_bad)\n",
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@ -345,20 +320,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"execution_count": null,
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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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"total validation good images: 5735\n",
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"total validation bad images: 1364\n",
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"--\n",
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"Total validation images: 7099\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"print('total validation good images:', num_val_good)\n",
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"print('total validation bad images:', num_val_bad)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"execution_count": null,
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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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"Train =\n",
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"7109\n",
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"Validation =\n",
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"7099\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"#print(\"Reduce training and validation set\")\n",
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"#total_train = 250\n",
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"#total_val = 200\n",
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"print(\"Reduce training and validation set\")\n",
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"total_train = 50\n",
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"total_val = 50\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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},
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{
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"cell_type": "code",
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"execution_count": 1,
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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(Image('data/test/unvetted/waterfall.png'))"
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"print(test_img)\n",
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"display(Image(test_img))"
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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": 27,
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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 = 256\n",
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"epochs = 32"
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"batch_size = 16\n",
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"epochs = 2"
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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": 28,
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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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},
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{
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"cell_type": "code",
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"execution_count": 29,
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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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@ -439,7 +393,7 @@
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -450,17 +404,9 @@
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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": null,
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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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"Found 7109 images belonging to 2 classes.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,\n",
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" directory=train_dir,\n",
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": null,
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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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"Found 7099 images belonging to 2 classes.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"val_data_gen = val_image_generator.flow_from_directory(batch_size=batch_size,\n",
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" directory=val_dir,\n",
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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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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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sample_val_images, _ = next(val_data_gen)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#sample_val_images, _ = next(val_data_gen)\n",
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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": 35,
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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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},
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{
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"cell_type": "code",
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"execution_count": 2,
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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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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#plotImages(sample_val_images[:3])"
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"plotImages(sample_val_images[: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": 38,
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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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},
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{
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"cell_type": "code",
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"execution_count": 39,
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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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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": null,
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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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"Model: \"sequential\"\n",
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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"conv2d (Conv2D) (None, 416, 804, 16) 448 \n",
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"_________________________________________________________________\n",
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"max_pooling2d (MaxPooling2D) (None, 208, 402, 16) 0 \n",
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"_________________________________________________________________\n",
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"conv2d_1 (Conv2D) (None, 208, 402, 32) 4640 \n",
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"_________________________________________________________________\n",
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"max_pooling2d_1 (MaxPooling2 (None, 104, 201, 32) 0 \n",
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"_________________________________________________________________\n",
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"conv2d_2 (Conv2D) (None, 104, 201, 64) 18496 \n",
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"_________________________________________________________________\n",
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"max_pooling2d_2 (MaxPooling2 (None, 52, 100, 64) 0 \n",
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"_________________________________________________________________\n",
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"flatten (Flatten) (None, 332800) 0 \n",
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"_________________________________________________________________\n",
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"dense (Dense) (None, 512) 170394112 \n",
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"_________________________________________________________________\n",
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"dense_1 (Dense) (None, 1) 513 \n",
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"=================================================================\n",
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"Total params: 170,418,209\n",
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"Trainable params: 170,418,209\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"model.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": null,
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"metadata": {},
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||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -628,20 +541,7 @@
|
|||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1/32\n",
|
||||
"27/27 [==============================] - 3214s 119s/step - loss: 2.8449 - accuracy: 0.8087 - val_loss: 2.9460 - val_accuracy: 0.8079\n",
|
||||
"Epoch 2/32\n",
|
||||
"27/27 [==============================] - 3196s 118s/step - loss: 2.8857 - accuracy: 0.8121 - val_loss: 2.9460 - val_accuracy: 0.8079\n",
|
||||
"Epoch 3/32\n",
|
||||
"13/27 [=============>................] - ETA: 22:18 - loss: 2.9072 - accuracy: 0.8104"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"history = model.fit_generator(\n",
|
||||
" train_data_gen,\n",
|
||||
|
|
@ -664,6 +564,7 @@
|
|||
"loss = history.history['loss']\n",
|
||||
"val_loss = history.history['val_loss']\n",
|
||||
"\n",
|
||||
"''\n",
|
||||
"epochs_range = range(epochs)\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(8, 8))\n",
|
||||
|
|
@ -693,7 +594,86 @@
|
|||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
"source": [
|
||||
"print(train_dir)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(train_good_dir)\n",
|
||||
"print(train_bad_dir)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(train_image_generator)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(train_data_gen)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(sample_train_images,)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(history)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_generator.reset()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_datagen=ImageDataGenerator(rescale=1./255.)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"test_generator=test_datagen.flow_from_directory(\n",
|
||||
" directory=\"data/test/\",\n",
|
||||
" target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
|
||||
" class_mode='binary'\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
|
|
@ -702,22 +682,40 @@
|
|||
"outputs": [],
|
||||
"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_dir,\n",
|
||||
"\tbatch_size=None,\n",
|
||||
"\tverbose=1,\n",
|
||||
"\tsteps=None,\n",
|
||||
"\tcallbacks=None,\n",
|
||||
"\tmax_queue_size=10,\n",
|
||||
"\tworkers=16,\n",
|
||||
"\tuse_multiprocessing=True)"
|
||||
"print(\"predict\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pred=model.predict_generator(test_generator,\n",
|
||||
"steps=4,\n",
|
||||
"verbose=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prediction = model.predict(\n",
|
||||
" x=test_generator,\n",
|
||||
" verbose=2\n",
|
||||
")\n",
|
||||
"print(\"end predict\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@
|
|||
# https://archive.org/details/satnogs?sort=-publicdate
|
||||
# Run:
|
||||
# wut-audio-archive 1292461 1368693
|
||||
# wut-audio-archive 1346458 1368693
|
||||
#
|
||||
# XXX Should check input is sane...
|
||||
|
||||
|
|
|
|||
|
|
@ -15,6 +15,8 @@
|
|||
#
|
||||
# So to get mostly all of the observations in December, 2019, run:
|
||||
# wut-water-range 1292461 1470525
|
||||
# Resume:
|
||||
# wut-water-range 1355760 1470525
|
||||
#
|
||||
# XXX Should check input is sane...
|
||||
|
||||
|
|
@ -36,7 +38,7 @@ while [ $OBSID -lt $OBSIDMAX ]
|
|||
--http2 --ipv4 \
|
||||
--silent \
|
||||
--output $OBSID.json \
|
||||
"$APIURL/observations/?id=$OBSID&ground_station=&satellite__norad_cat_id=&transmitter_uuid=&transmitter_mode=&transmitter_type=&vetted_status=&vetted_user=&start=&end=" && sleep `echo $((0 + RANDOM % 6))`
|
||||
"$APIURL/observations/?id=$OBSID&ground_station=&satellite__norad_cat_id=&transmitter_uuid=&transmitter_mode=&transmitter_type=&vetted_status=&vetted_user=&start=&end=" && sleep `echo $((0 + RANDOM % 3))`
|
||||
WATERURL=`cat $OBSID.json | jq --compact-output '.[0] | {waterfall}' | cut -f 2- -d : | sed -e 's/}//g' -e 's/http:/https:/g' -e 's/"//g'`
|
||||
WATERFILE=`basename $WATERURL`
|
||||
[ ! -f "$WATERFILE" ] && \
|
||||
|
|
|
|||
Loading…
Reference in New Issue