Fix typos in documentation

class ChemCeption(KerasModel):

  """

  Implements the ChemCeption model that leverages the representational capacties

  Implements the ChemCeption model that leverages the representational capacities

  of convolutional neural networks (CNNs) to predict molecular properties.

  The model is based on the description in Goh et al., "Chemception: A Deep

  encode different atomic and bond properties. More details on the image repres-

  entations can be found at https://arxiv.org/abs/1710.02238

  The model consists of a Stem Layer, that reduces the image resolution for the

  The model consists of a Stem Layer that reduces the image resolution for the

  layers to follow. The output of the Stem Layer is followed by a series of

  Inception-Resnet blocks & a Reduction layer. Layers in the Inception-Resnet

  blocks process image tensors as multiple resolutions, and use a ResNet style

  skip-connections, combining features from different resolutions. The Reduction

  blocks process image tensors at multiple resolutions and use a ResNet style

  skip-connection, combining features from different resolutions. The Reduction

  layers reduce the spatial extent of the image by max-pooling and 2-strided

  convolutions. More details of these layers can be found in the ChemCeption

  convolutions. More details on these layers can be found in the ChemCeption

  paper referenced above. The output of the final Reduction layer is subject to

  a Global Average Pooling, and a fully-connected layer maps the features to

  downstream outputs.