Merged changes from master branch

# TODO: Get rid of this when there is a stable release of deepchem.

RUN git clone https://github.com/deepchem/deepchem.git && \

    cd deepchem && \

    git checkout 415aebadff54175b7ba108964723c8f69438af94 && \

    git checkout tags/1.0.0 && \

    bash scripts/install_deepchem_conda.sh root && \

    pip install tensorflow-gpu==0.12.1 && \

    pip install tensorflow-gpu==1.0.1 && \

    python setup.py develop

# Clean up

```bash

bash scripts/install_deepchem_conda.sh deepchem

pip install tensorflow-gpu==0.12.1                      # If you want GPU support

pip install tensorflow-gpu==1.0.1                      # If you want GPU support

git clone https://github.com/deepchem/deepchem.git      # Clone deepchem source code from GitHub

cd deepchem

python setup.py install                                 # Manual install

    contact your local sysadmin to work out a custom installation. If your

    version of Linux is recent, then the following command will work:

    ```

    pip install tensorflow-gpu==0.12.1

    pip install tensorflow-gpu==1.0.1

    ```

9. `deepchem`: Clone the `deepchem` github repo:

## About Us

DeepChem is a package by the [Pande group](https://pande.stanford.edu/) at Stanford. DeepChem was originally created by [Bharath Ramsundar](http://rbharath.github.io/), and has grown through the contributions of a number of undergraduate, graduate, and postdoctoral researchers working with the Pande lab.

## Version

1.0.1

from deepchem.models.sklearn_models import SklearnModel

from deepchem.models.tf_new_models.multitask_classifier import MultitaskGraphClassifier

from deepchem.models.tf_new_models.multitask_regressor import MultitaskGraphRegressor

from deepchem.models.tf_new_models.DTNN_regressor import DTNNGraphRegressor

from deepchem.models.tf_new_models.support_classifier import SupportGraphClassifier

from deepchem.models.multitask import SingletaskToMultitask

from deepchem.models.sequential import Sequential

      A tensor with shape batch_size containing the weighted cost for each

      example.

    """

    return tf.mul(

        tf.nn.softmax_cross_entropy_with_logits(logits, labels), weights)

    return tf.multiply(

        tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels),

        weights)

  def add_label_placeholders(self, graph, name_scopes):

    """Add Placeholders for labels for each task.

      A tensor with shape batch_size containing the weighted cost for each

      example.

    """

    return tf.mul(0.5 * tf.square(output - labels), weights)

    return tf.multiply(0.5 * tf.square(output - labels), weights)

  def add_label_placeholders(self, graph, name_scopes):

    """Add Placeholders for labels for each task.

    else:

      raise NotImplementedError('Unsupported penalty_type %s' % penalty_type)

    cost *= penalty

    tf.scalar_summary('Weight Decay Cost', cost)

    tf.summary.scalar('Weight Decay Cost', cost)

  return cost

      return loss

  def cost(self, logits, labels, weights):

    return tf.mul(

        tf.nn.sigmoid_cross_entropy_with_logits(logits, labels), weights)

    return tf.multiply(

        tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels),

        weights)

  def add_output_ops(self, graph, output):

    # adding output nodes of sigmoid function