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

# 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.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()