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class Sequential(TensorGraph):

  """Sequential models are linear stacks of layers.

  Analogous to the Sequential model from Keras.

  Analogous to the Sequential model from Keras and allows for less

  verbose construction of simple deep learning model.

  Example

  -------

  >>> import deepchem as dc

  >>> import numpy as np

  >>> from deepchem.models.tensorgraph import layers

  >>> # Define Data

  >>> X = np.random.rand(20, 2)                     

  >>> y = [[0, 1] for x in range(20)]

  >>> dataset = dc.data.NumpyDataset(X, y)                              

  >>> model = dc.models.Sequential(learning_rate=0.01)                  

  >>> model.add(layers.Dense(out_channels=2))                                  

  >>> model.add(layers.SoftMax())

  >>> model.fit(dataset, loss="binary_crossentropy", nb_epoch=1000)     

  >>> prediction = np.squeeze(model.predict_on_batch(X)) 

  """

  def __init__(self, **kwargs):

  def fit(self, dataset, loss, **kwargs):

    """Fits on the specified dataset.

    Adds the necessary feature and placeholders.

    If called for the first time, constructs the TensorFlow graph for this

    model. Fits this graph on the specified dataset according to the specified

    loss.

    Parameters

    ----------