Merge pull request #487 from lilleswing/tensorflow-1.0.1

```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

      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

      prev_layers.append(all_layers[(i - 1, prev_task)])

    # prev_layers is a list with elements of size

    # (batch_size, layer_sizes[i-1])

    prev_layer = tf.concat(1, prev_layers)

    prev_layer = tf.concat(axis=1, values=prev_layers)

    alpha = tf.Variable(tf.truncated_normal([

        1,

    ], stddev=alpha_init_stddev))

    prev_layer = tf.mul(alpha, prev_layer)

    prev_layer = tf.multiply(alpha, prev_layer)

    prev_layer_size = task * layer_sizes[i - 1]

    print("Creating V_layer_%d_task%d of shape %s" %

          (i, task, str([prev_layer_size, layer_sizes[i - 1]])))

      prev_layers.append(all_layers[(i - 1, prev_task)])

    # prev_layers is a list with elements of size

    # (batch_size, layer_sizes[i-1])

    prev_layer = tf.concat(1, prev_layers)

    prev_layer = tf.concat(axis=1, values=prev_layers)

    alpha = tf.Variable(

        tf.truncated_normal([

            1,

        ], stddev=alpha_init_stddev),

        name="alpha_layer_%d_task%d" % (i, task))

    prev_layer = tf.mul(alpha, prev_layer)

    prev_layer = tf.multiply(alpha, prev_layer)

    prev_layer_size = task * layer_sizes[i - 1]

    print("Creating V_layer_%d_task%d of shape %s" %

          (i, task, str([prev_layer_size, layer_sizes[i - 1]])))

      sess = self._get_shared_session(train=True)

      #with self._get_shared_session(train=True) as sess:

      sess.run(tf.initialize_all_variables())

      sess.run(tf.global_variables_initializer())

      # Save an initial checkpoint.

      saver = tf.train.Saver(max_to_keep=max_checkpoints_to_keep)

      saver.save(sess, self._save_path, global_step=0)