Get the test runner operating again.

Models/model_host.py

@@ -1,43 +1,5 @@
-#import sys
-#import os
-#import glob
-#import socket
-#from keras.backend import GraphExecutionFunction
-
-#sys.path.append('c:/git/keras')
-#sys.path.append('c:/git/absl')
-
-# This upgrades all modules
-#py -mpip freeze | %{$_.split('==')[0]} | %{py -mpip install --upgrade $_}
-
-
-# The following worked to install CUDA for using tensorflow with GPU
-# 1) Install CUDA 11.8 Toolkit from here   
-#    https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Windows&target_arch=x86_64&target_version=11&target_type=exe_local
-# 2) The binaries should wind up in C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin
-# 3) Put the above path in the PATH environment variable
-# 4) Also download cuDNN from here https://developer.nvidia.com/cudnn because tensorflow will look for cudnn64_8.dll which is in that package
-#    This download is just a bunch of DLLs with no install program.  I left it in downloads folder.
-# 5) put C:\download\cuDNN\cudnn-windows-x86_64-8.9.7.29_cuda11-archive\bin in the PATH
-# 6) Run the check_gpu.py program and ensure no errors loading DLLs
-
-
-
-
-# installed version
-# py -m pip install keras==2.6.0
-# py -mpip install flask==2.0.1
-# py -mpip install numpy==1.19.5
-# py -mpip show numpy 
-# py -mpip install tensorflow==2.6.0
-# py -mpip show tensorflow
-# py -mpip install matplotlib==3.4.3
-# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
-
 import keras
-#from keras.preprocessing.image import ImageDataGenerator
 from keras.src.legacy.preprocessing.image import ImageDataGenerator
-
 from flask import Flask, render_template, send_file, request
 import numpy as np
 import tensorflow

Models/model_testrunner.py

@@ -1,11 +1,11 @@
-import sys
-import os
-import glob
+# import sys
+# import os
+# import glob
 
-from keras.layers.pooling import MaxPool2D 
+# from keras.layers.pooling import MaxPool2D 
 
-sys.path.append('c:/git/keras')
-sys.path.append('c:/git/absl')
+# sys.path.append('c:/git/keras')
+# sys.path.append('c:/git/absl')
 
 # installed 
 # py -mpip install numpy
@@ -15,15 +15,34 @@ sys.path.append('c:/git/absl')
 # py -mpip install matplotlib
 # c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
 
-import keras
-from keras.preprocessing.image import ImageDataGenerator
-import numpy as np
-import tensorflow
+# import keras
+# from keras.preprocessing.image import ImageDataGenerator
+# import numpy as np
+# import tensorflow
 
 #model_name='lenet5.h5'
+
+import sys
+import os
+import glob
+from keras.optimizers import SGD
+from keras.optimizers import Adam
+from tensorflow.keras.callbacks import TensorBoard
+import keras
+from keras.src.legacy.preprocessing.image import ImageDataGenerator
+from keras.callbacks import ModelCheckpoint
+from tensorflow.keras.callbacks import EarlyStopping
+import numpy as np
+import tensorflow
+from resnet50 import *
+import math
+from time import time
+
+
 print(os.getcwd())
 
-model_name='../Weights/resnet50.h5'
+path = os.getcwd()
+model_name='Weights/resnet50_20241024_270.h5.keras'
 
 # *************************************************** T E S T I N G ********************************************
 
@@ -49,7 +68,10 @@ model_name='../Weights/resnet50.h5'
 
 # Load the model
 
-model = keras.models.load_model(model_name)
+real_path = os.path.realpath(path + '/' + model_name)
+
+print('Loading {model_name}'.format(model_name=real_path))
+model = keras.models.load_model(real_path)
 
 # image_path='C:/boneyard/DeepLearning/IndividualValidationCases/SIG_0_Test_BollingerBand.jpg'
 # test_image = tensorflow.keras.preprocessing.image.load_img(image_path,color_mode='grayscale')
@@ -72,8 +94,6 @@ model = keras.models.load_model(model_name)
 files=glob.glob("C:/boneyard/DeepLearning/IndividualValidationCases/*.jpg")
 #files=glob.glob("C:/boneyard/DeepLearning/data/VolatilityPriceContraction/*.jpg")
 
-
-
 for file in files:
     test_image = tensorflow.keras.preprocessing.image.load_img(file,color_mode='grayscale')
     test_array = keras.preprocessing.image.img_to_array(test_image)