Updated all models October 2024

Models/model_sk_vgg16.py

@@ -1,49 +1,32 @@
 import sys
 import os
 
-from keras.layers.pooling import MaxPool2D 
-
-sys.path.append('c:/git/keras')
-sys.path.append('c:/git/absl')
-
-# installed 
-# py -mpip install numpy
-# py -mpip show numpy 
-# py -mpip install tensorflow
-# py -mpip show tensorflow
-# py -mpip install matplotlib
-# c:\users\skess\appdata\local\programs\python\python39\lib\site-packages
-
+from keras.optimizers import SGD
+from keras.optimizers import Adam
+from tensorflow.keras.callbacks import TensorBoard
 import keras
-from keras.optimizers import adam_v2
-from tensorflow.keras.optimizers import Adam
-from keras.datasets import cifar10
-from keras.preprocessing.image import ImageDataGenerator
-from keras.models import Sequential
-from keras.utils import np_utils
-from keras.layers import Dense
-from keras.layers import Activation
-from keras.layers import Flatten
-from keras.layers import Dropout
-from keras.layers import BatchNormalization
-from keras.layers import Conv2D
-from keras.layers import MaxPooling2D
-from keras.layers import AveragePooling2D
+from keras.src.legacy.preprocessing.image import ImageDataGenerator
 from keras.callbacks import ModelCheckpoint
 from tensorflow.keras.callbacks import EarlyStopping
-from keras import regularizers
-from keras import optimizers
-from matplotlib import pyplot
+from keras.layers import Flatten
+from keras.layers import Dense
+from keras.layers import MaxPool2D
+from keras.layers import Dropout
+from keras.models import Model
+from keras.layers import AveragePooling2D
+from keras.models import Sequential
 import numpy as np
 import tensorflow
-
+from inception_module import *
+import math
+from time import time
 
 # VGG 16 ARCHITECTIURE
 
 image_size = (128, 128)
 
-model_name='vgg16'
-model_weights_file='model_vgg16.h5'
+#model_name='vgg16'
+model_weights_file='model_vgg16.h5.keras'
 
 train_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
     'C:\\boneyard\\DeepLearning\\data',
@@ -76,6 +59,9 @@ learning_rate=.00001
 batch_size=256
 epochs=200
 
+# Create a TensorBoard instance with the path to the logs directory.  Before training, in Terminal, run tensorboard --logdir=logs/
+tensorboard = TensorBoard(log_dir='logs/{}'.format(time()))
+
 # Build the network based on AlexNet but using 6 convolutional and 1 Fully Connected
 # Inspired by VGGNet we will add a pooling layer after every two convolutional layers
 
@@ -165,11 +151,8 @@ model.add(Dropout(.50))
 #SIGMOID
 model.add(Dense(1,activation='sigmoid'))
 
-
 model.summary()
 
-
-
 # File path to save the file.  Only save the weights if there is an improvement.
 checkpointer=ModelCheckpoint(filepath=model_weights_file,verbose=1,save_best_only=True)
 
@@ -178,7 +161,9 @@ checkpointer=ModelCheckpoint(filepath=model_weights_file,verbose=1,save_best_onl
 # Adam optimizer with learning rate .0001
 #optimizer=adam_v2.Adam(learning_rate=10e-6)
 #optimizer=adam_v2.Adam(learning_rate=10e-6)
-optimizer=adam_v2.Adam(learning_rate=learning_rate)
+#optimizer=adam_v2.Adam(learning_rate=learning_rate)
+#Optimizer
+optimizer = keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.9, beta_2=0.999,epsilon=1e-8)
 
 # Cross entropy loss function
 #model.compile(loss='categorical_crossentropy', optimizer=optimizer,metrics=['accuracy'])
@@ -190,17 +175,17 @@ model.compile(loss='binary_crossentropy', optimizer=optimizer,metrics=['accuracy
 # The callback to checkpointer saves the model wights.  Other callback can be added...like a stopping function
 # history=model.fit_generator(dataGen.flow(x_train,y_train,batch_size=batch_size),callbacks=[checkpointer],steps_per_epoch=x_train.shape[0] //
 # batch_size,epochs=epochs,verbose=2,validation_data=(x_valid,y_valid))
-history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,batch_size=batch_size,verbose=2,callbacks=[checkpointer])
+history=model.fit(train_ds, epochs=epochs, validation_data=val_ds,batch_size=batch_size,verbose=2,callbacks=[tensorboard,checkpointer])
 
 # Here is the evaluation part
-score=model.evaluate(x=train_ds,verbose=1)
-print('\n',     'Test Accuracy:', score[1])
-model.save(model_name)
+# score=model.evaluate(x=train_ds,verbose=1)
+# print('\n',     'Test Accuracy:', score[1])
+# model.save(model_name)
 
 # This loads the best weights we saved from the fitting excercise and displays the accuracy of the model
-model.load_weights(model_weights_file)
-score=model.evaluate(train_ds,verbose=1)
-print('\n',     'Test Accuracy:', score[1])
+# model.load_weights(model_weights_file)
+# score=model.evaluate(train_ds,verbose=1)
+# print('\n',     'Test Accuracy:', score[1])
 
 # scores=model.evaluate(x_test,y_test,batch_size=128,verbose=1)
 # print('\nTest result: %.3f loss: %.3f' % (scores[1]*100,scores[0]))
@@ -211,10 +196,10 @@ print('\n',     'Test Accuracy:', score[1])
 # print(threshold_output)
 
 # plot the learning curves
-pyplot.plot(history.history['accuracy'],label='train')
-pyplot.plot(history.history['val_accuracy'],label='validation')
-pyplot.legend()
-pyplot.show()
+# pyplot.plot(history.history['accuracy'],label='train')
+# pyplot.plot(history.history['val_accuracy'],label='validation')
+# pyplot.legend()
+# pyplot.show()