Update GDB7 example to use CoulombFitTransformer

gdb7_tasks, datasets, transformers = load_gdb7(featurizer=dc.feat.CoulombMatrix(23))

train_dataset, valid_dataset, test_dataset = datasets

fit_transformers = [dc.trans.CoulombRandomizationFitTransformer(), dc.trans.NormalizationFitTransformer()]

#fit_transformers = [dc.trans.CoulombRandomizationFitTransformer(), dc.trans.NormalizationFitTransformer()]

X = train_dataset.X

fit_transformers = [dc.trans.CoulombFitTransformer(X, num_atoms=23)]

regression_metric = dc.metrics.Metric(dc.metrics.mean_absolute_error, 

                                      mode="regression")

model = dc.models.TensorflowMultiTaskFitTransformRegressor(

    n_tasks=len(gdb7_tasks), n_features=23,

    learning_rate=.0002, momentum=.8, batch_size=512,

    learning_rate=.001, momentum=.8, batch_size=512,

    weight_init_stddevs=[1/np.sqrt(2000),1/np.sqrt(800),1/np.sqrt(800),1/np.sqrt(1000)],

    bias_init_consts=[0.,0.,0.,0.], layer_sizes=[2000,800,800,1000], 

    dropouts=[0.1,0.1,0.1,0.1], fit_transformers=fit_transformers, n_random_samples=10, seed=123)

# Fit trained model

model.fit(train_dataset, nb_epoch=2)

model.fit(train_dataset, nb_epoch=50)

model.save()

train_scores = model.evaluate(train_dataset, [regression_metric], transformers)