121 lines
3.8 KiB
Python
121 lines
3.8 KiB
Python
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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print(os.getcwd())
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model_name='../Weights/resnet50.h5'
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# *************************************************** T E S T I N G ********************************************
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# This gets a 0. This is a good result
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#image_path='C:/boneyard/DeepLearning/IndividualValidationCases/BAND_0_Test_BollingerBand.jpg'
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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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# 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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#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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#image_path='C:/boneyard/DeepLearning/IndividualValidationCases/SIG_0_Test_BollingerBand.jpg'
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# *****************************************************************************************************************
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# test_image = tensorflow.keras.preprocessing.image.load_img(image_path,color_mode='grayscale')
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# test_array = keras.preprocessing.image.img_to_array(test_image)
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# print(test_array.shape)
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# batch_test_array = np.array([test_array])
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# print(batch_test_array.shape)
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# Load the model
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model = keras.models.load_model(model_name)
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# image_path='C:/boneyard/DeepLearning/IndividualValidationCases/SIG_0_Test_BollingerBand.jpg'
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# test_image = tensorflow.keras.preprocessing.image.load_img(image_path,color_mode='grayscale')
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# test_array = keras.preprocessing.image.img_to_array(test_image)
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# print(test_array.shape)
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# batch_test_array = np.array([test_array])
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# print(batch_test_array.shape)
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# predictions=model.predict(batch_test_array)
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# print('Actual Prediction(s):',predictions)
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# threshold_output = np.where(predictions > 0.5, 1, 0)
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# print('Threshold output:',threshold_output)
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# All files ending with .txt
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#files=glob.glob("C:/boneyard/DeepLearning/ModelInputData/*.jpg")
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files=glob.glob("C:/boneyard/DeepLearning/IndividualValidationCases/*.jpg")
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#files=glob.glob("C:/boneyard/DeepLearning/data/VolatilityPriceContraction/*.jpg")
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for file in files:
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test_image = tensorflow.keras.preprocessing.image.load_img(file,color_mode='grayscale')
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test_array = keras.preprocessing.image.img_to_array(test_image)
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# print(test_array.shape)
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batch_test_array = np.array([test_array])
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# print(batch_test_array.shape)
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predictions=model.predict(batch_test_array)
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# print('Actual Prediction(s):',predictions)
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# threshold_output = np.where(predictions < 0.00000005, 1, 0)
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if type(predictions) == list:
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average_prediction = sum(predictions)/len(predictions)
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threshold_output = np.where(average_prediction > 0.5, 1, 0)
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else :
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threshold_output = np.where(predictions > 0.5, 1, 0)
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# threshold_output = np.where(predictions > 0.5, 1, 0)
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sstart=file.rfind("\\")+1
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send=file.find("_")
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symbol=file[int(sstart):int(send)]
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# print('%s,%s' % (symbol,threshold_output[0][0]))
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print('%s,%s -->%s %s' % (symbol,predictions,threshold_output,file))
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#model.summary()
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# predictions=model.predict(batch_test_array)
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# print('Actual Prediction(s):',predictions)
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# threshold_output = np.where(predictions > 0.5, 1, 0)
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# print('Threshold output:',threshold_output)
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