Sequential API for Model Construction

[rbharath]

This PR adds a new API for constructing sequential models (those that are a linear stack of models) based on the Keras Sequential API. For simple networks, this new API allows users to skip explicitly specifying Feature, Label, loss inputs, outputs, and in_layers. For example, here's a simple classifier in Sequential:

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(Dense(out_channels=2))                                  
model.add(SoftMax())
model.fit(dataset, loss="binary_crossentropy", nb_epoch=1000)     
prediction = np.squeeze(model.predict_on_batch(X)) 

For comparison, here's the same model in the TensorGraph API

X = np.random.rand(20, 2)                     
y = [[0, 1] for x in range(20)]                        
dataset = NumpyDataset(X, y)                                      
features = Feature(shape=(None, 20))                      
dense = Dense(out_channels=2, in_layers=[features])               
output = SoftMax(in_layers=[dense])
label = Label(shape=(None, 2))                                    
smce = SoftMaxCrossEntropy(in_layers=[label, dense])              
loss = ReduceMean(in_layers=[smce])                               
tg = dc.models.TensorGraph(learning_rate=0.01)                    
tg.add_output(output)
tg.set_loss(loss) 
tg.fit(dataset, nb_epoch=1000)                                    
prediction = np.squeeze(tg.predict_on_batch(X))                   

Underneath the hood, Sequential inherits from TensorGraph and simply constructs the explicit TensorGraph as needed. For now, losses are explicitly specified as strings passed into Sequential.fit(). The initial implementation will support the same string arguments as the Keras Sequential API. The PR also removes an old (non-functional) version of Sequential that was not based on TensorGraph.

This PR isn't quite ready to merge (will need to add in losses beyond binary_crossentropy and mse), but I wanted to put it out for feedback on the API design.

[coveralls]

Coverage increased (+0.08%) to 80.499% when pulling 5246f2c on rbharath:sequential into 401d669 on deepchem:master.

[coveralls]

Coverage increased (+0.08%) to 80.499% when pulling 7453a90 on rbharath:sequential into 401d669 on deepchem:master.

[lilleswing]

Do you need this? When you set loss this should go through the tree and call already?

[rbharath]

Good point, will remove.

[lilleswing]

Ditto for this _add_layer

[rbharath]

Will remove.

[lilleswing]

Why is this needed? super().fit() should call build and will also install the queue for faster training/

[rbharath]

Good point. Will remove.

[peastman]

This class is kind of strange, in that the model isn't fully defined until you call fit(). But what if it never gets called? What if instead the user calls restore() to reload a previously fit model? It won't work.

[rbharath]

@peastman You raise a good point about restore(). For now, I raise a ValueError if restore() is called. In a future PR, we should figure out a more general mechanism. The issue is that Sequential allows users to avoid specifying placeholders and imputes placeholder shapes from the provided dataset in fit(). This means that the full TensorFlow graph can't be built until fit() is called.

A more general solution would probably be to write placeholder shapes in metadata and add placeholders if necessary in restore(). Will do this in a future PR.

[coveralls]

Coverage increased (+0.01%) to 80.749% when pulling c6b7c64 on rbharath:sequential into fbf8b79 on deepchem:master.

[coveralls]

Coverage increased (+0.07%) to 80.806% when pulling 046c8e8 on rbharath:sequential into f6e75cf on deepchem:master.

[rbharath]

Going to go ahead and merge in this PR. Will address any outstanding issues in a follow-on PR.

LGTM