Removed obsolete tests

from deepchem.models.tensorgraph.layers import WeightDecay

from deepchem.models.tensorgraph.layers import WeightedError

from deepchem.models.tensorgraph.layers import WeightedLinearCombo

from deepchem.models.tensorgraph.IRV import IRVLayer

from deepchem.models.tensorgraph.IRV import IRVRegularize

from deepchem.models.tensorgraph.IRV import Slice

from deepchem.models.tensorgraph.graph_layers import DTNNEmbedding

from deepchem.models.tensorgraph.graph_layers import DTNNExtract

from deepchem.models.tensorgraph.graph_layers import WeaveGather

      assert vertex_props.shape == (10, 6, 50)

      assert adjs.shape == (10, 6, 5, 6)

  def test_slice(self):

    """Test that Slice can be invoked."""

    batch_size = 10

    n_features = 5

    test_tensor_input = np.random.rand(batch_size, n_features)

    with self.session() as sess:

      test_tensor = tf.convert_to_tensor(test_tensor_input, dtype=tf.float32)

      out_tensor = Slice(1)(test_tensor)

      out_tensor = out_tensor.eval()

      assert np.allclose(out_tensor, test_tensor_input[:, 1:2])

  def test_IRV(self):

    """Test that IRVLayer and IRVRegularize can be invoked."""

    batch_size = 10

    n_tasks = 5

    K = 10

    n_features = 2 * K * n_tasks

    test_tensor_input = np.random.rand(batch_size, n_features)

    with self.session() as sess:

      test_tensor = tf.convert_to_tensor(test_tensor_input, dtype=tf.float32)

      irv_layer = IRVLayer(n_tasks, K)

      irv_layer.create_tensor(in_layers=[test_tensor])

      out_tensor = irv_layer.out_tensor

      sess.run(tf.global_variables_initializer())

      out_tensor = out_tensor.eval()

      assert out_tensor.shape == (batch_size, n_tasks)

      irv_reg = IRVRegularize(irv_layer, 1.)()

      assert irv_reg.eval() >= 0

  def test_hingeloss(self):

    separation = 0.25

    labels = [1, 1, 0, 0]

  Conv3D, MaxPool3D, Conv2DTranspose, Conv3DTranspose, \

  LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding, GraphEmbedPoolLayer, GraphCNN, Cast,HingeLoss,SparseSoftMaxCrossEntropy

from deepchem.models.tensorgraph.symmetry_functions import AtomicDifferentiatedDense

from deepchem.models.tensorgraph.IRV import IRVLayer, IRVRegularize, Slice

def test_Conv1D_pickle():

  tg.save()

def test_IRVLayer_pickle():

  n_tasks = 10

  K = 10

  V = Feature(shape=(None, 200))

  irv_layer = IRVLayer(n_tasks, K, in_layers=[V])

  irv_reg = IRVRegularize(irv_layer, in_layers=[irv_layer])

  tg = TensorGraph()

  tg.add_output(irv_layer)

  tg.add_output(irv_reg)

  tg.set_loss(irv_reg)

  tg.build()

  tg.save()

def test_Slice_pickle():

  V = Feature(shape=(None, 10))

  out = Slice(5, 1, in_layers=[V])

  tg = TensorGraph()

  tg.add_output(out)

  tg.set_loss(out)

  tg.build()

  tg.save()

def test_hingeloss_pickle():

  tg = TensorGraph()

  feature = Feature(shape=(1, None))