Corrected Softmax to SoftMax

from deepchem.models.tensorgraph.layers import SigmoidCrossEntropy

from deepchem.models.tensorgraph.layers import SoftMax

from deepchem.models.tensorgraph.layers import SoftMaxCrossEntropy

from deepchem.models.tensorgraph.layers import SparseSoftmaxCrossEntropy

from deepchem.models.tensorgraph.layers import SparseSoftMaxCrossEntropy

from deepchem.models.tensorgraph.layers import StopGradient

from deepchem.models.tensorgraph.layers import TensorWrapper

from deepchem.models.tensorgraph.layers import TimeSeriesDense

    with self.test_session() as sess:

      logit_tensor = tf.convert_to_tensor(logit_tensor, dtype=tf.float32)

      label_tensor = tf.convert_to_tensor(label_tensor, dtype=tf.float32)

      out_tensor = SparseSoftmaxCrossEntropy()(logit_tensor, label_tensor)

      out_tensor = SparseSoftMaxCrossEntropy()(logit_tensor, label_tensor)

      out_tensor = out_tensor.eval()

      assert out_tensor.shape == (batch_size,)

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

  GraphGather, BatchNorm, WeightedError, ReLU, \

  Conv3D, MaxPool3D, Conv2DTranspose, Conv3DTranspose, \

  LSTMStep, AttnLSTMEmbedding, IterRefLSTMEmbedding, GraphEmbedPoolLayer, GraphCNN, Cast,Hingeloss,SparseSoftmaxCrossEntropy

  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

  tg = TensorGraph()

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

  labels = Feature(shape=(tg.batch_size))

  layer = SparseSoftmaxCrossEntropy(in_layers=[logits, labels])

  layer = SparseSoftMaxCrossEntropy(in_layers=[logits, labels])

  tg.add_output(layer)

  tg.set_loss(layer)

  tg.build()