diff --git a/exps-tf/one-shot-nas.py b/exps-tf/one-shot-nas.py
new file mode 100644
index 0000000..181d9d8
--- /dev/null
+++ b/exps-tf/one-shot-nas.py
@@ -0,0 +1,206 @@
+# [D-X-Y]
+# Run DARTS
+# CUDA_VISIBLE_DEVICES=0 python exps-tf/one-shot-nas.py --epochs 50
+#
+import os, sys, math, time, random, argparse
+import tensorflow as tf
+from pathlib import Path
+
+lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
+if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
+
+# self-lib
+from tf_models import get_cell_based_tiny_net
+from tf_optimizers import SGDW, AdamW
+from config_utils import dict2config
+from log_utils import time_string
+from models import CellStructure
+
+
+def pre_process(image_a, label_a, image_b, label_b):
+  def standard_func(image):
+    x = tf.pad(image, [[4, 4], [4, 4], [0, 0]])
+    x = tf.image.random_crop(x, [32, 32, 3])
+    x = tf.image.random_flip_left_right(x)
+    return x
+  return standard_func(image_a), label_a, standard_func(image_b), label_b
+
+
+class CosineAnnealingLR(object):
+  def __init__(self, warmup_epochs, epochs, initial_lr, min_lr):
+    self.warmup_epochs = warmup_epochs
+    self.epochs = epochs
+    self.initial_lr = initial_lr
+    self.min_lr = min_lr
+
+  def get_lr(self, epoch):
+    if epoch < self.warmup_epochs:
+      lr = self.min_lr + (epoch/self.warmup_epochs) * (self.initial_lr-self.min_lr)
+    elif epoch >= self.epochs:
+      lr = self.min_lr
+    else:
+      lr = self.min_lr + (self.initial_lr-self.min_lr) * 0.5 * (1 + math.cos(math.pi * epoch / self.epochs))
+    return lr
+      
+
+
+def main(xargs):
+  cifar10 = tf.keras.datasets.cifar10
+
+  (x_train, y_train), (x_test, y_test) = cifar10.load_data()
+  x_train, x_test = x_train / 255.0, x_test / 255.0
+  x_train, x_test = x_train.astype('float32'), x_test.astype('float32')
+  y_train, y_test = y_train.reshape(-1), y_test.reshape(-1)
+
+  # Add a channels dimension
+  all_indexes = list(range(x_train.shape[0]))
+  random.shuffle(all_indexes)
+  s_train_idxs, s_valid_idxs = all_indexes[::2], all_indexes[1::2]
+  search_train_x, search_train_y = x_train[s_train_idxs], y_train[s_train_idxs]
+  search_valid_x, search_valid_y = x_train[s_valid_idxs], y_train[s_valid_idxs]
+  #x_train, x_test = x_train[..., tf.newaxis], x_test[..., tf.newaxis]
+  
+  # Use tf.data
+  #train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(64)
+  search_ds = tf.data.Dataset.from_tensor_slices((search_train_x, search_train_y, search_valid_x, search_valid_y))
+  search_ds = search_ds.map(pre_process).shuffle(1000).batch(64)
+
+  test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
+
+  # Create an instance of the model
+  config = dict2config({'name': 'DARTS',
+                        'C'   : xargs.channel, 'N': xargs.num_cells, 'max_nodes': xargs.max_nodes,
+                        'num_classes': 10, 'space': 'nas-bench-201', 'affine': True}, None)
+  model = get_cell_based_tiny_net(config)
+  num_iters_per_epoch = int(tf.data.experimental.cardinality(search_ds).numpy())
+  #lr_schedular = tf.keras.experimental.CosineDecay(xargs.w_lr_max, num_iters_per_epoch*xargs.epochs, xargs.w_lr_min / xargs.w_lr_max)
+  lr_schedular = CosineAnnealingLR(0, xargs.epochs, xargs.w_lr_max, xargs.w_lr_min)
+  # Choose optimizer
+  loss_object = tf.keras.losses.CategoricalCrossentropy()
+  w_optimizer = SGDW(learning_rate=xargs.w_lr_max, weight_decay=xargs.w_weight_decay, momentum=xargs.w_momentum, nesterov=True)
+  a_optimizer = AdamW(learning_rate=xargs.arch_learning_rate, weight_decay=xargs.arch_weight_decay, beta_1=0.5, beta_2=0.999, epsilon=1e-07)
+  #w_optimizer = tf.keras.optimizers.SGD(learning_rate=0.025, momentum=0.9, nesterov=True)
+  #a_optimizer = tf.keras.optimizers.AdamW(learning_rate=xargs.arch_learning_rate, beta_1=0.5, beta_2=0.999, epsilon=1e-07)
+  ####
+  # metrics
+  train_loss = tf.keras.metrics.Mean(name='train_loss')
+  train_accuracy = tf.keras.metrics.CategoricalAccuracy(name='train_accuracy')
+  valid_loss = tf.keras.metrics.Mean(name='valid_loss')
+  valid_accuracy = tf.keras.metrics.CategoricalAccuracy(name='valid_accuracy')
+  test_loss = tf.keras.metrics.Mean(name='test_loss')
+  test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
+  
+  @tf.function
+  def search_step(train_images, train_labels, valid_images, valid_labels):
+    # optimize weights
+    with tf.GradientTape() as tape:
+      predictions = model(train_images, True)
+      w_loss = loss_object(train_labels, predictions)
+    net_w_param = model.get_weights()
+    gradients = tape.gradient(w_loss, net_w_param)
+    w_optimizer.apply_gradients(zip(gradients, net_w_param))
+    train_loss(w_loss)
+    train_accuracy(train_labels, predictions)
+    # optimize alphas
+    with tf.GradientTape() as tape:
+      predictions = model(valid_images, True)
+      a_loss = loss_object(valid_labels, predictions)
+    net_a_param = model.get_alphas()
+    gradients = tape.gradient(a_loss, net_a_param)
+    a_optimizer.apply_gradients(zip(gradients, net_a_param))
+    valid_loss(a_loss)
+    valid_accuracy(valid_labels, predictions)
+
+  # IFT with Neumann approximation
+  @tf.function
+  def search_step_IFTNA(train_images, train_labels, valid_images, valid_labels, max_step):
+    # optimize weights
+    with tf.GradientTape() as tape:
+      predictions = model(train_images, True)
+      w_loss = loss_object(train_labels, predictions)
+    # get the weights
+    net_w_param = model.get_weights()
+    net_a_param = model.get_alphas()
+    gradients = tape.gradient(w_loss, net_w_param)
+    w_optimizer.apply_gradients(zip(gradients, net_w_param))
+    train_loss(w_loss)
+    train_accuracy(train_labels, predictions)
+    # optimize alphas
+    with tf.GradientTape(persistent=True) as tape:
+      predictions = model(valid_images, True)
+      val_loss = loss_object(valid_labels, predictions)
+      predictions = model(train_images, True)
+      trn_loss = loss_object(train_labels, predictions)
+      # ----
+      dV_dW = tape.gradient(val_loss, net_w_param)
+      # approxInverseHVP to calculate v2
+      sum_p = v1 = dV_dW
+      dT_dW = tape.gradient(trn_loss, net_w_param)
+      for j in range(1, max_step):
+        temp_dot = tape.gradient(dT_dW, net_w_param, output_gradients=v1)
+        v1 = [tf.subtract(A, B) for A, B in zip(v1, temp_dot)]
+        sum_p = [tf.add(A, B) for A, B in zip(sum_p, v1)]
+      # calculate v3
+      dT_dl = tape.gradient(trn_loss, net_a_param)
+      import pdb; pdb.set_trace()
+      v3 = tape.gradient(dT_dl, net_w_param, output_gradients=sum_p)
+    dV_dl = tape.gradient(val_loss, net_a_param)
+    a_gradients = [tf.subtract(A, B) for A, B in zip(dV_dl, v3)]
+    import pdb; pdb.set_trace()
+    print('--')
+
+  # TEST
+  @tf.function
+  def test_step(images, labels):
+    predictions = model(images)
+    t_loss = loss_object(labels, predictions)
+
+    test_loss(t_loss)
+    test_accuracy(labels, predictions)
+
+  print('{:} start searching with {:} epochs ({:} batches per epoch).'.format(time_string(), xargs.epochs, num_iters_per_epoch))
+
+  for epoch in range(xargs.epochs):
+    # Reset the metrics at the start of the next epoch
+    train_loss.reset_states() ; train_accuracy.reset_states()
+    test_loss.reset_states()  ; test_accuracy.reset_states()
+    cur_lr  = lr_schedular.get_lr(epoch)
+    tf.keras.backend.set_value(w_optimizer.lr, cur_lr)
+
+    for trn_imgs, trn_labels, val_imgs, val_labels in search_ds:
+      #search_step(trn_imgs, trn_labels, val_imgs, val_labels)
+      trn_labels, val_labels = tf.one_hot(trn_labels, 10), tf.one_hot(val_labels, 10)
+      search_step_IFTNA(trn_imgs, trn_labels, val_imgs, val_labels, 5)
+    genotype = model.genotype()
+    genotype = CellStructure(genotype)
+
+    #for test_images, test_labels in test_ds:
+    #  test_step(test_images, test_labels)
+
+    cur_lr = float(tf.keras.backend.get_value(w_optimizer.lr))
+    template = '{:} Epoch {:03d}/{:03d}, Train-Loss: {:.3f}, Train-Accuracy: {:.2f}%, Valid-Loss: {:.3f}, Valid-Accuracy: {:.2f}% | lr={:.6f}'
+    print(template.format(time_string(), epoch+1, xargs.epochs,
+                          train_loss.result(),
+                          train_accuracy.result()*100,
+                          valid_loss.result(),
+                          valid_accuracy.result()*100,
+                          cur_lr))
+    print('{:} genotype : {:}\n{:}\n'.format(time_string(), genotype, model.get_np_alphas()))
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser(description='NAS-Bench-201', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  # training details
+  parser.add_argument('--epochs'            , type=int  ,   default= 250  ,   help='')
+  parser.add_argument('--w_lr_max'          , type=float,   default= 0.025,   help='')
+  parser.add_argument('--w_lr_min'          , type=float,   default= 0.001,   help='')
+  parser.add_argument('--w_weight_decay'    , type=float,   default=0.0005,   help='')
+  parser.add_argument('--w_momentum'        , type=float,   default= 0.9  ,   help='')
+  parser.add_argument('--arch_learning_rate', type=float,   default=0.0003,   help='')
+  parser.add_argument('--arch_weight_decay' , type=float,   default=0.001,    help='')
+  # marco structure
+  parser.add_argument('--channel'           , type=int  ,   default=16,       help='')
+  parser.add_argument('--num_cells'         , type=int  ,   default= 5,       help='')
+  parser.add_argument('--max_nodes'         , type=int  ,   default= 4,       help='')
+  args = parser.parse_args()
+  main( args )
diff --git a/exps-tf/test-invH.py b/exps-tf/test-invH.py
new file mode 100644
index 0000000..b455506
--- /dev/null
+++ b/exps-tf/test-invH.py
@@ -0,0 +1,46 @@
+import os, sys, math, time, random, argparse
+import tensorflow as tf
+from pathlib import Path
+
+
+def test_a():
+  x = tf.Variable([[1.], [2.], [4.0]])
+  with tf.GradientTape(persistent=True) as g:
+    trn = tf.math.exp(tf.math.reduce_sum(x))
+    val = tf.math.cos(tf.math.reduce_sum(x))
+    dT_dx = g.gradient(trn, x)
+    dV_dx = g.gradient(val, x)
+    hess_vector = g.gradient(dT_dx, x, output_gradients=dV_dx)
+  print ('calculate ok : {:}'.format(hess_vector))
+
+def test_b():
+  cce = tf.keras.losses.SparseCategoricalCrossentropy()
+  L1 = tf.convert_to_tensor([0, 1, 2])
+  L2 = tf.convert_to_tensor([2, 0, 1])
+  B = tf.Variable([[.9, .05, .05], [.5, .89, .6], [.05, .01, .94]])
+  with tf.GradientTape(persistent=True) as g:
+    trn = cce(L1, B)
+    val = cce(L2, B)
+    dT_dx = g.gradient(trn, B)
+    dV_dx = g.gradient(val, B)
+    hess_vector = g.gradient(dT_dx, B, output_gradients=dV_dx)
+  print ('calculate ok : {:}'.format(hess_vector))
+
+def test_c():
+  cce = tf.keras.losses.CategoricalCrossentropy()
+  L1 = tf.convert_to_tensor([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])
+  L2 = tf.convert_to_tensor([[0., 0., 1.], [0., 1., 0.], [1., 0., 0.]])
+  B = tf.Variable([[.9, .05, .05], [.5, .89, .6], [.05, .01, .94]])
+  with tf.GradientTape(persistent=True) as g:
+    trn = cce(L1, B)
+    val = cce(L2, B)
+    dT_dx = g.gradient(trn, B)
+    dV_dx = g.gradient(val, B)
+    hess_vector = g.gradient(dT_dx, B, output_gradients=dV_dx)
+  print ('calculate ok : {:}'.format(hess_vector))
+
+if __name__ == '__main__':
+  print(tf.__version__)
+  test_c()
+  #test_b()
+  #test_a()
diff --git a/lib/tf_models/__init__.py b/lib/tf_models/__init__.py
index ac05da5..d402913 100644
--- a/lib/tf_models/__init__.py
+++ b/lib/tf_models/__init__.py
@@ -9,7 +9,7 @@ __all__ = ['get_cell_based_tiny_net', 'get_search_spaces']
 
 # the cell-based NAS models
 def get_cell_based_tiny_net(config):
-  group_names = ['GDAS']
+  group_names = ['GDAS', 'DARTS']
   if config.name in group_names:
     from .cell_searchs import nas_super_nets
     from .cell_operations import SearchSpaceNames
diff --git a/lib/tf_models/cell_searchs/__init__.py b/lib/tf_models/cell_searchs/__init__.py
index 479cb03..717fbe4 100644
--- a/lib/tf_models/cell_searchs/__init__.py
+++ b/lib/tf_models/cell_searchs/__init__.py
@@ -2,5 +2,7 @@
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
 ##################################################
 from .search_model_gdas     import TinyNetworkGDAS
+from .search_model_darts    import TinyNetworkDARTS
 
-nas_super_nets = {'GDAS': TinyNetworkGDAS}
+nas_super_nets = {'GDAS' : TinyNetworkGDAS,
+                  'DARTS': TinyNetworkDARTS}
diff --git a/lib/tf_models/cell_searchs/search_model_darts.py b/lib/tf_models/cell_searchs/search_model_darts.py
new file mode 100644
index 0000000..ad05b8b
--- /dev/null
+++ b/lib/tf_models/cell_searchs/search_model_darts.py
@@ -0,0 +1,83 @@
+import tensorflow as tf
+import numpy as np
+from copy import deepcopy
+from ..cell_operations import ResNetBasicblock
+from .search_cells     import NAS201SearchCell as SearchCell
+
+
+class TinyNetworkDARTS(tf.keras.Model):
+
+  def __init__(self, C, N, max_nodes, num_classes, search_space, affine):
+    super(TinyNetworkDARTS, self).__init__()
+    self._C        = C
+    self._layerN   = N
+    self.max_nodes = max_nodes
+    self.stem = tf.keras.Sequential([
+                    tf.keras.layers.Conv2D(16, 3, 1, padding='same', use_bias=False),
+                    tf.keras.layers.BatchNormalization()], name='stem')
+  
+    layer_channels   = [C    ] * N + [C*2 ] + [C*2  ] * N + [C*4 ] + [C*4  ] * N    
+    layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
+
+    C_prev, num_edge, edge2index = C, None, None
+    for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
+      cell_prefix = 'cell-{:03d}'.format(index)
+      #with tf.name_scope(cell_prefix) as scope:
+      if reduction:
+        cell = ResNetBasicblock(C_prev, C_curr, 2)
+      else:
+        cell = SearchCell(C_prev, C_curr, 1, max_nodes, search_space, affine)
+        if num_edge is None: num_edge, edge2index = cell.num_edges, cell.edge2index
+        else: assert num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(num_edge, cell.num_edges)
+      C_prev = cell.out_dim
+      setattr(self, cell_prefix, cell)
+    self.num_layers = len(layer_reductions)
+    self.op_names   = deepcopy( search_space )
+    self.edge2index = edge2index
+    self.num_edge   = num_edge
+    self.lastact    = tf.keras.Sequential([
+                        tf.keras.layers.BatchNormalization(),
+                        tf.keras.layers.ReLU(),
+                        tf.keras.layers.GlobalAvgPool2D(),
+                        tf.keras.layers.Flatten(),
+                        tf.keras.layers.Dense(num_classes, activation='softmax')], name='lastact')
+    #self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
+    arch_init = tf.random_normal_initializer(mean=0, stddev=0.001)
+    self.arch_parameters = tf.Variable(initial_value=arch_init(shape=(num_edge, len(search_space)), dtype='float32'), trainable=True, name='arch-encoding')
+
+  def get_alphas(self):
+    xlist = self.trainable_variables
+    return [x for x in xlist if 'arch-encoding' in x.name]
+
+  def get_weights(self):
+    xlist = self.trainable_variables
+    return [x for x in xlist if 'arch-encoding' not in x.name]
+
+  def get_np_alphas(self):
+    arch_nps = self.arch_parameters.numpy()
+    arch_ops = np.exp(arch_nps) / np.sum(np.exp(arch_nps), axis=-1, keepdims=True)
+    return arch_ops
+
+  def genotype(self):
+    genotypes, arch_nps = [], self.arch_parameters.numpy()
+    for i in range(1, self.max_nodes):
+      xlist = []
+      for j in range(i):
+        node_str = '{:}<-{:}'.format(i, j)
+        weights = arch_nps[ self.edge2index[node_str] ]
+        op_name = self.op_names[ weights.argmax().item() ]
+        xlist.append((op_name, j))
+      genotypes.append( tuple(xlist) )
+    return genotypes
+
+  def call(self, inputs, training):
+    weightss = tf.nn.softmax(self.arch_parameters, axis=1)
+    feature = self.stem(inputs, training)
+    for idx in range(self.num_layers):
+      cell = getattr(self, 'cell-{:03d}'.format(idx))
+      if isinstance(cell, SearchCell):
+        feature = cell.call(feature, weightss, training)
+      else:
+        feature = cell(feature, training)
+    logits = self.lastact(feature, training)
+    return logits