Fixes

	       layer_sizes=layer_sizes, weight_init_stddevs=weight_init_stddevs, 

	       bias_init_consts=bias_init_consts, penalty=penalty, 

	       penalty_type=penalty_type, dropouts=dropouts, 

	       learning_rate=self.learning_rate, momentum=momentum, optimizer=optimizer, 

	       learning_rate=learning_rate, momentum=momentum, optimizer=optimizer, 

	       batch_size=batch_size, n_classes=n_classes, pad_batches=False, verbose=verbose, seed=seed, 

	       **kwargs)

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.TensorflowCoulombMatrixRegressor(

model = dc.models.TensorflowMultiTaskFitTransformRegressor(

    n_tasks=1, n_features=23,

    learning_rate={0: 0.001, 500: 0.0025, 2500: 0.005, 12500: 0.01} , momentum=.8, batch_size=25,

    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=50)

model.fit(train_dataset, nb_epoch=5)

model.save()

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