Merge conflict handling

- '2.7'

sudo: required

install:

- sudo apt-get update

- wget http://repo.continuum.io/archive/Anaconda2-2.4.1-Linux-x86_64.sh -O anaconda.sh;

- bash anaconda.sh -b -p $HOME/anaconda

- export PATH="$HOME/anaconda/bin:$PATH"

- conda install joblib

- conda install -c omnia theano

- conda install -c omnia keras

- conda install seaborn

- conda install six

- python setup.py install

script:

- nosetests -v deepchem

import numpy as np

from keras.models import Graph

from keras.models import model_from_json

from keras.layers.core import Dense, Dropout

from keras.layers.core import Dense, Dropout, Activation

from keras.layers.normalization import BatchNormalization 

from keras.optimizers import SGD

from deepchem.models import Model

      (n_inputs,) = model_params["data_shape"]

      model = Graph()

      model.add_input(name="input", input_shape=(n_inputs,))

      prev_layer = "input"

      for ind, layer in enumerate(range(model_params["nb_layers"])):

        dense_layer_name = "dense%d" % ind

        activation_layer_name = "activation%d" % ind

        batchnorm_layer_name = "batchnorm%d" % ind

        dropout_layer_name = "dropout%d" % ind

        model.add_node(

          Dense(model_params["nb_hidden"], init=model_params["init"],

                activation=model_params["activation"]),

          name="dense", input="input")

            Dense(model_params["nb_hidden"], init=model_params["init"]),

            name=dense_layer_name, input=prev_layer)

        prev_layer = dense_layer_name 

        if model_params["batchnorm"]:

          model.add_node(

            BatchNormalization(), input=prev_layer, name=batchnorm_layer_name)

          prev_layer = batchnorm_layer_name

        model.add_node(Activation(model_params["activation"]),

                       name=activation_layer_name, input=prev_layer)

        prev_layer = activation_layer_name

        if model_params["dropout"] > 0:

          model.add_node(Dropout(model_params["dropout"]),

                     name="dropout",

                     input="dense")

      top_layer = "dropout"

                         name=dropout_layer_name,

                         input=prev_layer)

          prev_layer = dropout_layer_name

      for ind, task in enumerate(sorted_tasks):

        task_type = task_types[task]

        if task_type == "classification":

          model.add_node(

              Dense(2, init=model_params["init"], activation="softmax"),

              name="dense_head%d" % ind, input=top_layer)

              name="dense_head%d" % ind, input=prev_layer)

        elif task_type == "regression":

          model.add_node(

              Dense(1, init=model_params["init"]),

              name="dense_head%d" % ind, input=top_layer)

              name="dense_head%d" % ind, input=prev_layer)

        model.add_output(name="task%d" % ind, input="dense_head%d" % ind)

      loss_dict = {}