Fixed test cases

from deepchem.models.tensorgraph.layers import Layer, Feature, Label, Weights, \

    WeightedError, Dense, Dropout, WeightDecay, Reshape, SparseSoftMaxCrossEntropy, \

    L2Loss, ReduceSum, Concat, Stack, TensorWrapper, ReLU, Squeeze, SoftMax, Cast

from deepchem.models.tensorgraph.IRV import Slice

from deepchem.models.tensorgraph.layers import convert_to_layers

class Slice(Layer):

  """ Choose a slice of input on the last axis given order,

  Suppose input x has two dimensions,

  output f(x) = x[:, slice_num:slice_num+1]

  """

  def __init__(self, slice_num, axis=1, **kwargs):

    """

    Parameters

    ----------

    slice_num: int

      index of slice number

    axis: int

      axis id

    """

    self.slice_num = slice_num

    self.axis = axis

    super(Slice, self).__init__(**kwargs)

  def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):

    if in_layers is None:

      in_layers = self.in_layers

    in_layers = convert_to_layers(in_layers)

    slice_num = self.slice_num

    axis = self.axis

    inputs = in_layers[0].out_tensor

    out_tensor = tf.slice(inputs, [0] * axis + [slice_num], [-1] * axis + [1])

    if set_tensors:

      self.out_tensor = out_tensor

    return out_tensor

class ProgressiveMultitaskRegressor(TensorGraph):