Update GDB7 example

gdb7_tasks, datasets, transformers = load_gdb7_from_mat(split)

train_dataset, test_dataset = datasets

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

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

                                      mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score,

				      mode="regression")]

fit_transformers = [dc.trans.CoulombFitTransformer(train_dataset)]

regression_metric = [dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), 

              dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")]

model = dc.models.TensorflowMultiTaskFitTransformRegressor(

    n_tasks=1, n_features=23,

    learning_rate=0.001 , momentum=.8, batch_size=25,

    n_tasks=1, n_features=[23, 23], learning_rate=0.001 , momentum=.8, batch_size=25,

    weight_init_stddevs=[1/np.sqrt(400),1/np.sqrt(100),1/np.sqrt(100)],

    bias_init_consts=[0.,0.,0.], layer_sizes=[400,100,100], 

    dropouts=[0.01,0.01,0.01], fit_transformers=fit_transformers, n_evals=10, seed=123)

# Fit trained model

model.fit(train_dataset, nb_epoch=5)

model.fit(train_dataset, nb_epoch=50)

model.save()

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