PR comments

    return out_tensor

class ToInt32(Layer):

class Cast(Layer):

  def __init__(self, in_layers=None, **kwargs):

    super(ToInt32, self).__init__(in_layers, **kwargs)

  def __init__(self, in_layers=None, dtype=None, **kwargs):

    if dtype is None:

      raise ValueError("Must cast to a dtype")

    self.dtype = dtype

    super(Cast, self).__init__(in_layers, **kwargs)

    try:

      parent_shape = self.in_layers[0].shape

      self._shape = parent_shape

  def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):

    inputs = self._get_input_tensors(in_layers)

    parent_tensor = inputs[0]

    out_tensor = tf.to_int32(parent_tensor)

    out_tensor = tf.cast(parent_tensor, self.dtype)

    if set_tensors:

      self.out_tensor = out_tensor

    return out_tensor

from deepchem.feat.graph_features import ConvMolFeaturizer

from deepchem.feat.mol_graphs import ConvMol

from deepchem.models.tensorgraph.layers import Add, MaxPool2D, MaxPool3D, GraphCNN, GraphEmbedPoolLayer, ToInt32

from deepchem.models.tensorgraph.layers import Add, MaxPool2D, MaxPool3D, GraphCNN, GraphEmbedPoolLayer, Cast

from deepchem.models.tensorgraph.layers import AlphaShareLayer

from deepchem.models.tensorgraph.layers import AttnLSTMEmbedding

from deepchem.models.tensorgraph.layers import BatchNorm

      loss = np.mean(diff**2)

      assert (loss - result) / loss < 1e-6

  def test_to_int32(self):

  def test_cast(self):

    """Test that layers can automatically reshape inconsistent inputs."""

    value1 = np.random.uniform(size=(2, 1)).astype(np.float32)

    with self.test_session() as sess:

      out_tensor = ToInt32(squeeze_dims=1)(tf.constant(value1))

      out_tensor = Cast(dtype=tf.int32)(tf.constant(value1))

      result = out_tensor.eval()

      assert result.dtype == np.int32

  SoftMaxCrossEntropy, ReduceMean, ToFloat, ReduceSquareDifference, Conv2D, MaxPool2D, ReduceSum, GraphConv, GraphPool, \

  GraphGather, BatchNorm, WeightedError, \

  Conv3D, MaxPool3D, Conv2DTranspose, Conv3DTranspose, \

  LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding, GraphEmbedPoolLayer, GraphCNN, ToInt32

  LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding, GraphEmbedPoolLayer, GraphCNN, Cast

from deepchem.models.tensorgraph.symmetry_functions import AtomicDifferentiatedDense

  tg.save()

def test_ToInt32_pickle():

def test_Cast_pickle():

  tg = TensorGraph()

  feature = Feature(shape=(tg.batch_size, 1))

  layer = ToInt32(in_layers=feature)

  layer = Cast(in_layers=feature, dtype=tf.int32)

  tg.add_output(layer)

  tg.set_loss(layer)

  tg.build()