93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
import sys
|
|
import os
|
|
|
|
from keras.optimizers import SGD
|
|
from keras.optimizers import Adam
|
|
from tensorflow.keras.callbacks import TensorBoard
|
|
import keras
|
|
#from keras.models import Model
|
|
from keras.src.legacy.preprocessing.image import ImageDataGenerator
|
|
from keras.callbacks import ModelCheckpoint
|
|
#from tensorflow.keras.callbacks import ReduceLROnPlateau
|
|
from tensorflow.keras.callbacks import EarlyStopping
|
|
#from keras import regularizers
|
|
#from keras import optimizers
|
|
import numpy as np
|
|
import tensorflow
|
|
from resnet50 import *
|
|
import math
|
|
from time import time
|
|
|
|
# Notes: 10/23/2024 Achived 53.85%
|
|
|
|
# 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
|
|
|
|
# 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_weights_file='model_resnet50.hdf5'
|
|
model_name='resnet50_20241022.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=10
|
|
metric='accuracy'
|
|
|
|
#ReduceLROnPlateau
|
|
reduce_lr=tensorflow.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',factor=np.sqrt(0.1),patience=5,min_lr=0.5e-6)
|
|
|
|
#Optimizer
|
|
optimizer=SGD(learning_rate=initial_lrate, momentum=0.9, nesterov=False)
|
|
|
|
# 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)
|
|
|
|
# plot the learning curves
|
|
# pyplot.plot(history.history['accuracy'],label='train')
|
|
# pyplot.plot(history.history['val_accuracy'],label='validation')
|
|
# pyplot.legend()
|
|
# pyplot.show()
|