Code cleanup.
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@@ -1,27 +1,3 @@
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# import sys
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# import os
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# import glob
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# from keras.layers.pooling import MaxPool2D
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# sys.path.append('c:/git/keras')
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# sys.path.append('c:/git/absl')
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# installed
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# py -mpip install numpy
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# py -mpip show numpy
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# py -mpip install tensorflow
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# py -mpip show tensorflow
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# py -mpip install matplotlib
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# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
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# import keras
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# from keras.preprocessing.image import ImageDataGenerator
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# import numpy as np
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# import tensorflow
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#model_name='lenet5.h5'
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import sys
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import sys
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import os
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import os
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import glob
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import glob
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@@ -52,7 +28,7 @@ model_name='Weights/resnet50_20241024_270.h5.keras'
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# This gets 6.3385902e-12 [0]. I kind of expect this.
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# This gets 6.3385902e-12 [0]. I kind of expect this.
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#image_path='C:/boneyard/DeepLearning/IndividualValidationCases/NVCR_0_Test_BollingerBand.jpg'
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#image_path='C:/boneyard/DeepLearning/IndividualValidationCases/NVCR_0_Test_BollingerBand.jpg'
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# This gets a 0. This is a good result because ABX_3_Training_BollingerBand.jpg exhibits the pattern we are looking for and is in teh Training set
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# This gets a 0. This is a good result because ABX_3_Training_BollingerBand.jpg exhibits the pattern we are looking for and is in the Training set
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#image_path='C:/boneyard/DeepLearning/IndividualValidationCases/ABX_3_Training_BollingerBand.jpg'
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#image_path='C:/boneyard/DeepLearning/IndividualValidationCases/ABX_3_Training_BollingerBand.jpg'
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# This gets 0.9911055 This is a good result because SIG_0_Test_BollingerBand contains a downturn
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# This gets 0.9911055 This is a good result because SIG_0_Test_BollingerBand contains a downturn
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