Updated all models October 2024

Models/model_sk_resnet_v5.py

@@ -0,0 +1,75 @@
+import sys
+import os
+
+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
+
+
+# This is the new 2024 version
+# CM10-31-2015_OI_10_1_MPBW_65_USEOVTRUE_V3.xlsm
+
+# 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()))
+
+# RESNET50 ARCHITECTURE
+image_size = (128, 128)
+
+model_name='resnet50_20241024_270.h5.keras'
+
+train_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
+    'C:\\boneyard\\DeepLearning\\data',
+
+    label_mode="binary", 
+    subset="training",
+    validation_split=.20,   
+    image_size=image_size,
+    color_mode='grayscale',
+    batch_size=32,
+    seed=50
+)
+val_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
+    'C:\\boneyard\\DeepLearning\\data',
+    label_mode="binary",   
+    subset="validation",
+    validation_split=.20,          
+    image_size=image_size,
+    color_mode='grayscale',
+    batch_size=32,
+    seed=50
+)
+
+input_shape=(128,128,1)
+epochs=200
+initial_lrate=0.01
+batch_size=128
+patience_on_early_stop=epochs*.25
+metric='accuracy'
+
+#ReduceLROnPlateau
+reduce_lr=tensorflow.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',factor=.1,patience=5,min_lr=0.5e-6)
+
+#Optimizer
+optimizer = keras.optimizers.Adam(learning_rate=initial_lrate, beta_1=0.9, beta_2=0.999,epsilon=1e-8)
+
+# Checkpointer
+checkpointer=ModelCheckpoint(filepath=model_name,monitor=metric,verbose=1,save_best_only=True)
+
+#Early Stopping
+early_stopping=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=patience_on_early_stop)
+
+model=resnet50(input_shape,1)
+model.summary()
+model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy'])
+history = model.fit(train_ds, validation_data=val_ds, epochs=epochs, batch_size=batch_size, callbacks=[tensorboard,reduce_lr,early_stopping,checkpointer])
+# leave the following line commented out because the early stopping is in place
+#model.save(model_name)