wut-ml-load wut-ml-save
ml server
parent
f67ebdb9d4
commit
2707edb725
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@ -27,9 +27,12 @@ do
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echo -n "$OBSID "
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echo -n "Vet: $VET "
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# Get Machine Learning Result
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WUT_VET=`./wut $OBSID | cut -f 2 -d " "`
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echo -n "Wut: $WUT_VET "
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if [ $VET = $WUT_VET ] ; then
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WUT_VETS=`./wut $OBSID`
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WUT_VET=`echo $WUT_VETS | cut -f 2 -d " "`
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WUT_RATE=`echo $WUT_VETS | cut -f 1 -d " "`
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echo -n "$WUT_VET, "
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echo -n "$WUT_RATE, "
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if [ "$VET" = "$WUT_VET" ] ; then
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let CORRECT=$CORRECT+1
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else
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let INCORRECT=$INCORRECT+1
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2
wut-ml
2
wut-ml
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@ -54,7 +54,7 @@ model.compile(loss='binary_crossentropy',
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optimizer='rmsprop',
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metrics=['accuracy'])
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model.fit(x=train_it, validation_data=val_it, epochs=1, verbose=2, workers=8, use_multiprocessing=True)
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model.fit(x=train_it, validation_data=val_it, epochs=16, verbose=2, workers=16, use_multiprocessing=True)
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prediction = model.predict(x=test_it, batch_size=None, verbose=0, steps=None, use_multiprocessing=True)
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print(prediction)
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@ -0,0 +1,60 @@
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#!/usr/bin/python3
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# wut-ml
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#
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# Vet a SatNOGS image using machine learning (guessing).
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# It will vet the image located at test/unvetted/waterfall.png.
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#
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# Note, there is an issue to fix where it will vet everything
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# under the data/test directory, so fix that. For now, just delete
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# everything else. :)
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#
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# Usage:
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# wut-ml
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# Example:
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# wut-ml
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import os
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import numpy as np
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import tensorflow.python.keras
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from tensorflow.python.keras import Sequential
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from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense
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from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
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from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
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from tensorflow.python.keras import optimizers
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from tensorflow.python.keras.preprocessing import image
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from tensorflow.python.keras.models import load_model
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from tensorflow.python.keras.preprocessing.image import load_img
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from tensorflow.python.keras.preprocessing.image import img_to_array
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model = load_model('wut.h5')
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img_width=256
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img_height=256
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model = Sequential()
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model.add(Convolution2D(32, 3, 3, input_shape=(img_width, img_height,3)))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Convolution2D(32, 3, 3))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Convolution2D(64, 3, 3))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Flatten())
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model.add(Dense(64))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(1))
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model.add(Activation('sigmoid'))
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model.compile(loss='binary_crossentropy',
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optimizer='rmsprop',
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metrics=['accuracy'])
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prediction = model.predict(x=test_it, batch_size=None, verbose=0, steps=None, use_multiprocessing=True, workers=16)
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print(prediction)
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if prediction[0][0] == 1:
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rating = 'bad'
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else:
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rating = 'good'
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print('Observation: %s' % (rating))
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@ -0,0 +1,60 @@
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#!/usr/bin/python3
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# wut-ml
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#
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# Vet a SatNOGS image using machine learning (guessing).
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# It will vet the image located at test/unvetted/waterfall.png.
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#
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# Note, there is an issue to fix where it will vet everything
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# under the data/test directory, so fix that. For now, just delete
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# everything else. :)
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#
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# Usage:
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# wut-ml
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# Example:
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# wut-ml
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import os
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import numpy as np
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import tensorflow.python.keras
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from tensorflow.python.keras import Sequential
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from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense
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from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
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from tensorflow.python.keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D
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from tensorflow.python.keras import optimizers
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from tensorflow.python.keras.preprocessing import image
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from tensorflow.python.keras.models import load_model
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from tensorflow.python.keras.preprocessing.image import load_img
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from tensorflow.python.keras.preprocessing.image import img_to_array
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datagen = ImageDataGenerator()
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train_it = datagen.flow_from_directory('data/train/', class_mode='binary')
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val_it = datagen.flow_from_directory('data/val/', class_mode='binary')
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test_it = datagen.flow_from_directory('data/test/', class_mode='binary')
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batchX, batchy = train_it.next()
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print('Batch shape=%s, min=%.3f, max=%.3f' % (batchX.shape, batchX.min(), batchX.max()))
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img_width=256
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img_height=256
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model = Sequential()
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model.add(Convolution2D(32, 3, 3, input_shape=(img_width, img_height,3)))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Convolution2D(32, 3, 3))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Convolution2D(64, 3, 3))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Flatten())
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model.add(Dense(64))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(1))
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model.add(Activation('sigmoid'))
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model.compile(loss='binary_crossentropy',
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optimizer='rmsprop',
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metrics=['accuracy'])
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model.fit(x=train_it, validation_data=val_it, epochs=1, verbose=2, workers=16, use_multiprocessing=True)
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model.save('wut.h5')
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@ -36,7 +36,7 @@ while [ $OBSID -gt $OBSIDMIN ]
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--http2 --ipv4 \
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--silent \
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--output $OBSID.json \
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"$APIURL/observations/?id=$OBSID&ground_station=&satellite__norad_cat_id=&transmitter_uuid=&transmitter_mode=&transmitter_type=&vetted_status=&vetted_user=&start=&end=" && sleep `echo $((0 + RANDOM % 1))`
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"$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 % 11))`
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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'`
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WATERFILE=`basename $WATERURL`
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[ ! -f $WATERFILE ] && \
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@ -46,7 +46,7 @@ while [ $OBSID -gt $OBSIDMIN ]
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--continue-at - \
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--remote-time \
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--output $WATERFILE \
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$WATERURL && sleep `echo $((0 + RANDOM % 2))`
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$WATERURL && sleep `echo $((0 + RANDOM % 11))`
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cd ..
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let OBSID=$OBSID-1
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done
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