Merge pull request #629 from lilleswing/set2set

from deepchem.metrics import to_one_hot

from deepchem.feat.mol_graphs import ConvMol

from deepchem.models.tensorgraph.layers import Input, GraphConvLayer, BatchNormLayer, GraphPoolLayer, Dense, GraphGather, \

from deepchem.models.tensorgraph.layers import Input, GraphConv, BatchNorm, GraphPool, Dense, GraphGather, \

  SoftMax, SoftMaxCrossEntropy, Concat, WeightedError, Label, Weights, Feature

np.random.seed(123)

  for i in range(0, 10 + 1):

    deg_adj = Feature(shape=(None, i + 1), dtype=tf.int32)

    deg_adjs.append(deg_adj)

  gc1 = GraphConvLayer(

  gc1 = GraphConv(

      64,

      activation_fn=tf.nn.relu,

      in_layers=[atom_features, degree_slice, membership] + deg_adjs)

  batch_norm1 = BatchNormLayer(in_layers=[gc1])

  gp1 = GraphPoolLayer(

      in_layers=[batch_norm1, degree_slice, membership] + deg_adjs)

  gc2 = GraphConvLayer(

  batch_norm1 = BatchNorm(in_layers=[gc1])

  gp1 = GraphPool(in_layers=[batch_norm1, degree_slice, membership] + deg_adjs)

  gc2 = GraphConv(

      64,

      activation_fn=tf.nn.relu,

      in_layers=[gp1, degree_slice, membership] + deg_adjs)

  batch_norm2 = BatchNormLayer(in_layers=[gc2])

  gp2 = GraphPoolLayer(

      in_layers=[batch_norm2, degree_slice, membership] + deg_adjs)

  batch_norm2 = BatchNorm(in_layers=[gc2])

  gp2 = GraphPool(in_layers=[batch_norm2, degree_slice, membership] + deg_adjs)

  dense = Dense(out_channels=128, activation_fn=None, in_layers=[gp2])

  batch_norm3 = BatchNormLayer(in_layers=[dense])

  batch_norm3 = BatchNorm(in_layers=[dense])

  gg1 = GraphGather(

      batch_size=batch_size,

      activation_fn=tf.nn.tanh,