DESIRED_SIZE,
nclasses=NUM_CLASSES,
filters=UNET_FILTERS)
print(unet.output_shape)
unet.summary()
# Setting BCE to False to use focal loss
BCE = False
print(xtrain.shape, train_images.shape)
if BCE:
unet.compile(optimizer='adam',
loss="sparse_categorical_crossentropy",
metrics=['accuracy'])
history = unet.fit(train_images,
train_labels,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=(valid_images, valid_labels))
else:
unet.compile(optimizer='adam',
loss=SparseCategoricalFocalLoss(gamma=2),
metrics=['accuracy'])
history = unet.fit(xtrain,
ytrain,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=(xval, yval))
unet.save(f"unet-SAMPLESIZE{SAMPLE_SIZE}-EPOCHS{EPOCHS}.h5")
loss = history.history["loss"]
)
earlyStopping = callbacks.EarlyStopping(
monitor='val_loss', # Monitored data
min_delta=0.001,
patience=
4, # Can accept the number of rounds with a promotion less than min_delta
)
# Continuous patience rounds of monotor without improvement will change the learning rate
reduceLROnPlateau = callbacks.ReduceLROnPlateau(
factor=0.2, # new_lr = lr * factor
patience=10, # Can accept rounds without lifting
min_lr=0.0000000001) # lr lower limit
tensorboard = callbacks.TensorBoard(
log_dir=args.logs,
write_graph=True, # Visualize images in TensorBoard
update_freq=
'epoch' # Write loss and metrics to TensorBoard after each epoch
)
model.fit(
x=X_train,
y=Y_train,
epochs=args.epochs,
validation_data=(X_test, Y_test),
validation_freq=1, # Test every few rounds
callbacks=[
modelCheckpoint, reduceLROnPlateau, tensorboard, earlyStopping
],
batch_size=args.batch_size,
workers=args.workers,
)
os.mkdir(logdir)
output_model_file = os.path.join(logdir, 'slate_models2.h5')
callbacks = [
tf.keras.callbacks.TensorBoard(logdir),
tf.keras.callbacks.ModelCheckpoint(output_model_file,
monitor='val_acc',
save_best_only=True),
# tf.keras.callbacks.EarlyStopping(monitor='val_accuracy',patience =10,min_delta = 0.1)
]
def focal(y_ture, y_pre):
loss = -tf.reduce_mean(
y_ture * tf.multiply(1 - y_pre, 1 - y_pre) *
tf.math.log(tf.clip_by_value(y_pre, 1e-10, 1.0))) * 0.25
return loss
# def meanIOU(y_ture,y_pre):
#
#
#
# #'categorical_crossentropy'
model.compile(loss=focal, optimizer=optimizers.Adam(lr=1e-4), metrics=['acc'])
model.fit(train_db,
epochs=100,
validation_data=test_db,
callbacks=callbacks,
steps_per_epoch=3)
