recover change

# Fit models

metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)

max_atoms_train = max([mol.get_num_atoms() for mol in train_dataset.X])

max_atoms_valid = max([mol.get_num_atoms() for mol in valid_dataset.X])

max_atoms_test = max([mol.get_num_atoms() for mol in test_dataset.X])

max_atoms = max([max_atoms_train, max_atoms_valid, max_atoms_test])

n_atom_feat = 75

n_pair_feat = 14

# Batch size of models

batch_size = 64

n_feat = 128

graph = dc.nn.SequentialWeaveGraph(batch_size, 

    n_atom_feat=n_atom_feat, n_pair_feat=n_pair_feat)

graph = dc.nn.SequentialWeaveGraph(

    max_atoms=max_atoms, n_atom_feat=n_atom_feat, n_pair_feat=n_pair_feat)

graph.add(dc.nn.WeaveLayer(75, 14))

#graph.add(dc.nn.WeaveLayer(50, 50))

graph.add(dc.nn.Dense(n_feat, 50, activation='tanh'))

graph.add(dc.nn.WeaveLayer(max_atoms, 75, 14))

#graph.add(dc.nn.WeaveLayer(max_atoms, 50, 50))

graph.add(dc.nn.WeaveConcat(batch_size, n_output=n_feat))

graph.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))

graph.add(dc.nn.WeaveGather(batch_size, n_input=n_feat, gaussian_expand=True))

    beta2=.999)

# Fit trained model

model.fit(train_dataset, nb_epoch=50, log_every_N_batches=5)

model.fit(train_dataset, nb_epoch=50, log_every_N_batches=50)

print("Evaluating model")

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