optim = Adam(1e-4)
# optim = SGD(1e-4, momentum=0.99, nesterov=True)
loss = PGCE
# model._weights('../models/deform_cnn.h5')
model.compile(optim,
loss=[loss, lambda y_true, y_pred: K.sum(y_pred)],
loss_weights=[1., .001],
metrics={'output': 'accuracy'})
checkpoint = ModelCheckpoint("deform_center_best.h5",
monitor='val_output_acc',
save_best_only=True)
checkpoint_tl = ModelCheckpoint("deform_center_trainbest.h5",
monitor='output_loss',
save_best_only=True)
spreadsheet = SpreadSheet("1nu6AFqzeYc2rNFAjtUtem-CFYKiRI4HCmXkxWsGglRg",
"DeformFaceAgeML3")
if args.img_dir is None:
try:
model.fit_generator(
train_scaled_gen,
steps_per_epoch=steps_per_epoch,
epochs=1000,
verbose=1,
validation_data=test_scaled_gen,
validation_steps=validation_steps,
callbacks=[checkpoint, checkpoint_tl],
)
val_loss, val_acc = model.evaluate_generator(
test_scaled_gen, steps=validation_steps)
model.load_weights(args.weight)
# sys.exit(0)
# input("Press enter to start training...")
optim = Adam(1e-4)
loss = categorical_crossentropy
model.compile(optim, [loss], metrics=['accuracy'])
checkpoint = ModelCheckpoint("deform_cnn_pg_best.h5",
monitor='val_acc',
save_best_only=True)
checkpoint_tl = ModelCheckpoint("deform_cnn_pg_trainbest.h5",
monitor='loss',
save_best_only=True)
spreadsheet = SpreadSheet(
"1nu6AFqzeYc2rNFAjtUtem-CFYKiRI4HCmXkxWsGglRg",
"DeformPersonGenderML3")
if args.img_dir is None:
(x_train,
y_train), (x_test, y_test) = cifar100.load_data(label_mode='fine')
# x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# x_train /= 255
x_test /= 255
datagen = ImageDataGenerator(featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True)