adding loss

import deepchem as dc

from deepchem.models import Sequential

from deepchem.models.tensorgraph.layers import Conv2D, MaxPool2D, Conv2DTranspose, Concat, Feature

from deepchem.models.tensorgraph.layers import SoftMaxCrossEntropy, ReduceMean, SoftMax

from deepchem.models import TensorGraph

    self.model = dc.models.TensorGraph()

    input = Feature(shape=(None, self.img_rows, self.img_cols, 3))

    labels = Feature(shape=(None, self.img_rows, self.img_cols))

    conv1 = Conv2D(

        num_outputs=self.filters[0],

    conv10 = Conv2D(

        num_outputs=1, kernel_size=1, activation='sigmoid', in_layers=[conv9])

    loss = SoftMaxCrossEntropy(in_layers=[labels, conv10])

    loss = ReduceMean(in_layers=[loss])

    model.set_loss(loss)

    model.add_output(conv10)