76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
import sys
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import os
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from keras.optimizers import SGD
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from keras.optimizers import Adam
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from tensorflow.keras.callbacks import TensorBoard
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import keras
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from keras.src.legacy.preprocessing.image import ImageDataGenerator
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from keras.callbacks import ModelCheckpoint
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from tensorflow.keras.callbacks import EarlyStopping
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import numpy as np
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import tensorflow
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from resnet50 import *
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import math
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from time import time
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# This is the new 2024 version
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# CM10-31-2015_OI_10_1_MPBW_65_USEOVTRUE_V3.xlsm
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# Create a TensorBoard instance with the path to the logs directory. Before training, in Terminal, run tensorboard --logdir=logs/
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tensorboard = TensorBoard(log_dir='logs/{}'.format(time()))
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# RESNET50 ARCHITECTURE
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image_size = (128, 128)
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model_name='resnet50_20241024_270.h5.keras'
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train_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
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'C:\\boneyard\\DeepLearning\\data',
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label_mode="binary",
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subset="training",
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validation_split=.20,
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image_size=image_size,
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color_mode='grayscale',
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batch_size=32,
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seed=50
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)
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val_ds = tensorflow.keras.preprocessing.image_dataset_from_directory(
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'C:\\boneyard\\DeepLearning\\data',
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label_mode="binary",
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subset="validation",
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validation_split=.20,
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image_size=image_size,
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color_mode='grayscale',
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batch_size=32,
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seed=50
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)
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input_shape=(128,128,1)
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epochs=200
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initial_lrate=0.01
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batch_size=128
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patience_on_early_stop=epochs*.25
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metric='accuracy'
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#ReduceLROnPlateau
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reduce_lr=tensorflow.keras.callbacks.ReduceLROnPlateau(monitor='val_loss',factor=.1,patience=5,min_lr=0.5e-6)
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#Optimizer
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optimizer = keras.optimizers.Adam(learning_rate=initial_lrate, beta_1=0.9, beta_2=0.999,epsilon=1e-8)
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# Checkpointer
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checkpointer=ModelCheckpoint(filepath=model_name,monitor=metric,verbose=1,save_best_only=True)
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#Early Stopping
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early_stopping=EarlyStopping(monitor='val_loss',mode='min',verbose=1,patience=patience_on_early_stop)
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model=resnet50(input_shape,1)
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model.summary()
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model.compile(loss='binary_crossentropy',optimizer=optimizer,metrics=['accuracy'])
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history = model.fit(train_ds, validation_data=val_ds, epochs=epochs, batch_size=batch_size, callbacks=[tensorboard,reduce_lr,early_stopping,checkpointer])
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# leave the following line commented out because the early stopping is in place
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#model.save(model_name)
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