Formatting

      prev_layer_size = num_features

      for i in range(num_layers):

        # layer has shape [None, layer_sizes[i]]

        print("Adding weights of shape %s" % str(

            [prev_layer_size, layer_sizes[i]]))

        print("Adding weights of shape %s" %

              str([prev_layer_size, layer_sizes[i]]))

        layer = tf.nn.relu(

            model_ops.fully_connected_layer(

                tensor=prev_layer,

      prev_layer_size = num_features

      for i in range(num_layers):

        # layer has shape [None, layer_sizes[i]]

        print("Adding weights of shape %s" % str(

            [prev_layer_size, layer_sizes[i]]))

        print("Adding weights of shape %s" %

              str([prev_layer_size, layer_sizes[i]]))

        layer = tf.nn.relu(

            model_ops.fully_connected_layer(

                tensor=prev_layer,

conda install -y -c omnia mdtraj

conda install -y scikit-learn

conda install -y setuptools

conda install -y keras=1.1.1

conda install -y keras=1.2.2

conda install -y -c conda-forge protobuf=3.1.0

conda install -y -c anaconda networkx=1.11

yes | pip install $tensorflow==1.0.1

        "tf.sparse_split": ["axis", "num_or_size_splits", "value", "name"],

        "tf.concat": ["concat_dim", "values", "name"],

        "tf.svd": ["tensor", "compute_uv", "full_matrices", "name"],

        "tf.nn.softmax_cross_entropy_with_logits": [

            "logits", "labels", "dim", "name"],

        "tf.nn.sparse_softmax_cross_entropy_with_logits": [

            "logits", "labels", "name"],

        "tf.nn.sigmoid_cross_entropy_with_logits": [

            "logits", "labels", "name"],

        "tf.nn.softmax_cross_entropy_with_logits":

        ["logits", "labels", "dim", "name"],

        "tf.nn.sparse_softmax_cross_entropy_with_logits":

        ["logits", "labels", "name"],

        "tf.nn.sigmoid_cross_entropy_with_logits": ["logits", "labels", "name"],

        "tf.op_scope": ["values", "name", "default_name"],

    }

    comment = ("ERROR: tf.reverse has had its argument semantics changed\n"

               "significantly the converter cannot detect this reliably, so you"

               "need to inspect this usage manually.\n")

    file_edit_recorder.add(comment,

    file_edit_recorder.add(

        comment,

        node.lineno,

        node.col_offset,

        "tf.reverse",

        error="tf.reverse requires manual check.")

class FileEditTuple(collections.namedtuple(

    "FileEditTuple", ["comment", "line", "start", "old", "new"])):

class FileEditTuple(

    collections.namedtuple("FileEditTuple",

                           ["comment", "line", "start", "old", "new"])):

  """Each edit that is recorded by a FileEditRecorder.

  Fields:

    function_renames = self._api_change_spec.function_renames

    try:

      new_name = function_renames[full_name]

      self._file_edit.add("Renamed function %r to %r" % (full_name,

                                                         new_name),

      self._file_edit.add("Renamed function %r to %r" % (full_name, new_name),

                          node.lineno, node.col_offset, full_name, new_name)

    except KeyError:

      pass

    # it is not possible to use that in an argument.

    return node.lineno, node.col_offset

  def visit_Call(self, node):  # pylint: disable=invalid-name

    """Handle visiting a call node in the AST.

      node: Current Node

    """

    # Find a simple attribute name path e.g. "tf.foo.bar"

    full_name = self._get_attribute_full_path(node.func)

          lineno, col_offset = self._find_true_position(arg)

          if lineno is None or col_offset is None:

            self._file_edit.add(

                "Failed to add keyword %r to reordered function %r"

                % (reordered[idx], full_name), arg.lineno, arg.col_offset,

                "", "",

                "Failed to add keyword %r to reordered function %r" %

                (reordered[idx], full_name),

                arg.lineno,

                arg.col_offset,

                "",

                "",

                error="A necessary keyword argument failed to be inserted.")

          else:

            keyword_arg = reordered[idx]

            if (full_name in function_keyword_renames and

                keyword_arg in function_keyword_renames[full_name]):

              keyword_arg = function_keyword_renames[full_name][keyword_arg]

            self._file_edit.add("Added keyword %r to reordered function %r"

                                % (reordered[idx], full_name), lineno,

                                col_offset, "", keyword_arg + "=")

            self._file_edit.add("Added keyword %r to reordered function %r" %

                                (reordered[idx], full_name), lineno, col_offset,

                                "", keyword_arg + "=")

      # Examine each keyword argument and convert it to the final renamed form

      renamed_keywords = ({} if full_name not in function_keyword_renames else

            # value.

            key_start = argval_col_offset - len(argkey) - 1

            key_end = key_start + len(argkey) + 1

            if self._lines[argval_lineno - 1][key_start:key_end] == argkey + "=":

            if self._lines[argval_lineno -

                           1][key_start:key_end] == argkey + "=":

              self._file_edit.add("Renamed keyword argument from %r to %r" %

                                  (argkey, renamed_keywords[argkey]),

                                  argval_lineno,

              (argkey, renamed_keywords[argkey]),

              argval.lineno,

              argval.col_offset - len(argkey) - 1,

              "", "",

              "",

              "",

              error="Failed to find keyword lexographically. Fix manually.")

    ast.NodeVisitor.generic_visit(self, node)

    # Write to a temporary file, just in case we are doing an implace modify.

    with open(in_filename, "r") as in_file, \

        tempfile.NamedTemporaryFile("w", delete=False) as temp_file:

      ret = self.process_opened_file(

          in_filename, in_file, out_filename, temp_file)

      ret = self.process_opened_file(in_filename, in_file, out_filename,

                                     temp_file)

    shutil.move(temp_file.name, out_filename)

    return ret

        out_file.write(out_text)

    text += "\n"

    return 1, text, process_errors

  # pylint: enable=broad-except

  def process_tree(self, root_directory, output_root_directory, copy_other_files):

  def process_tree(self, root_directory, output_root_directory,

                   copy_other_files):

    """Processes upgrades on an entire tree of python files in place.

    Note that only Python files. If you have custom code in other languages,

    # make sure output directory doesn't exist

    if output_root_directory and os.path.exists(output_root_directory):

      print("Output directory %r must not already exist." % (

          output_root_directory))

      print("Output directory %r must not already exist." %

            (output_root_directory))

      sys.exit(1)

    # make sure output directory does not overlap with root_directory

    norm_root = os.path.split(os.path.normpath(root_directory))

    norm_output = os.path.split(os.path.normpath(output_root_directory))

    if norm_root == norm_output:

      print("Output directory %r same as input directory %r" % (

          root_directory, output_root_directory))

      print("Output directory %r same as input directory %r" %

            (root_directory, output_root_directory))

      sys.exit(1)

    # Collect list of files to process (we do this to correctly handle if the

      shutil.copy(input_path, output_path)

    return file_count, report, tree_errors

def old_main():

  parser = argparse.ArgumentParser(

      formatter_class=argparse.RawDescriptionHelpFormatter,

    print("\n".join(errors))

    print("\nMake sure to read the detailed log %r\n" % report_filename)

def deepchem_main():

  paths = []

  for root, dirs, files in os.walk("deepchem"):

if __name__ == "__main__":

  deepchem_main()