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()