optimizer = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
lr_sched = rate_scheduler(lr=0.01, decay=0.99)
file_name = os.path.join(direc_data, dataset + ".npz")
training_data = np.load(file_name)
class_weights = training_data["class_weights"]
for iterate in range(1):
model = the_model(batch_shape=(1, 512, 512, 1),
n_features=3,
reg=1e-5,
softmax=True,
permute=True)
train_model(model=model,
dataset=dataset,
optimizer=optimizer,
expt=expt,
it=iterate,
batch_size=batch_size,
n_epoch=n_epoch,
direc_save=direc_save,
direc_data=direc_data,
lr_sched=lr_sched,
class_weight=class_weights,
rotation_range=180,
flip=True,
shear=False)
optimizer = SGD(lr=1e-2, decay=1e-6, momentum=0.9, nesterov=True)
lr_sched = rate_scheduler(lr=1e-2, decay=0.99)
file_name = os.path.join(direc_data, dataset + ".npz")
training_data = np.load(file_name)
for iterate in range(1):
model = the_model(batch_shape=(1, 1, 5, 256, 256),
n_features=3,
reg=1e-5,
location=False,
permute=True,
softmax=False)
trained_model = train_model(model=model,
dataset=dataset,
optimizer=optimizer,
expt=expt,
it=iterate,
batch_size=batch_size,
n_epoch=n_epoch,
direc_save=direc_save,
direc_data=direc_data,
number_of_frames=5,
lr_sched=lr_sched,
rotation_range=180,
flip=True,
shear=False)