max_iter=20)
net = BrainNetCNN(
input_shape=(90, 90),
in_channels=1,
num_classes=N_CLUSTERS,
nb_e2e=32,
nb_e2n=64,
nb_n2g=30,
dropout=0,
leaky_alpha=0.1,
twice_e2e=False,
dense_sml=False)
net_params = pynet.NetParameters(
network=net,
clustering=kmeans,
data_loader=train_loader,
n_batchs=10,
pca_dim=6,
assignment_logfile=None,
use_cuda=False)
model = DeepClusterClassifier(
net_params,
optimizer_name="SGD",
learning_rate=0.001,
momentum=0.9,
weight_decay=10**-5,
# loss=my_loss)
loss_name="CrossEntropyLoss")
model.board = Board(port=8097, host="http://localhost", env="deepcluster")
model.add_observer("before_epoch", update_pseudo_labels)
model.add_observer("after_epoch", update_board)
plt.title(y_train[idx])
manager = DataManager.from_numpy(train_inputs=x_train,
train_labels=y_train,
validation_inputs=x_valid,
validation_labels=y_valid,
test_inputs=x_test,
test_labels=y_test,
batch_size=128,
continuous_labels=True)
interfaces = pynet.get_interfaces()["graph"]
net_params = pynet.NetParameters(input_shape=(90, 90),
in_channels=1,
num_classes=2,
nb_e2e=32,
nb_e2n=64,
nb_n2g=30,
dropout=0.5,
leaky_alpha=0.1,
twice_e2e=False,
dense_sml=True)
my_loss = pynet.get_tools()["losses"]["MSELoss"]()
model = interfaces["BrainNetCNNGraph"](net_params,
optimizer_name="Adam",
learning_rate=0.01,
weight_decay=0.0005,
loss_name="MSELoss")
model.board = Board(port=8097, host="http://localhost", env="main")
model.add_observer("after_epoch", update_board)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer=model.optimizer,
mode="min",
factor=0.1,
size=len(k_indices))
labels = np.ones((N_SAMPLES, 1)) * labels
print("dataset: x {0} - y {1}".format(data.shape, labels.shape))
# Create data manager
manager = DataManager.from_numpy(train_inputs=data,
train_labels=labels,
test_inputs=data,
test_labels=labels,
batch_size=BATCH_SIZE)
# Create model
net_params = pynet.NetParameters(in_order=ICO_ORDER,
in_channels=2,
out_channels=N_CLASSES,
depth=3,
start_filts=32,
conv_mode="1ring",
up_mode="transpose",
cachedir=os.path.join(OUTDIR, "cache"))
model = SphericalUNetEncoder(net_params,
optimizer_name="SGD",
learning_rate=0.1,
momentum=0.99,
weight_decay=10**-4,
loss_name="CrossEntropyLoss",
use_cuda=True)
model.board = Board(port=8097, host="http://localhost", env="spherical_unet")
model.add_observer("after_epoch", update_board)
# Train model
test_history, train_history = model.training(manager=manager,