refinement

  def __init__(self,

               max_atoms=23,

               radial_cutoff=6.,

               angular_cutoff=4.5,

               radial_cutoff=4.6,

               angular_cutoff=3.1,

               radial_length=32,

               angular_length=8,

               atom_cases=[1, 6, 7, 8, 16],

    embedding = tf.eye(np.max(self.atom_cases) + 1)

    atom_numbers_embedded = tf.nn.embedding_lookup(embedding, atom_numbers)

    d_cutoff = tf.stack([d_cutoff] * self.radial_length, axis=3)

    d = tf.stack([d] * self.radial_length, axis=3)

    Rs = np.linspace(0., self.radial_cutoff, self.radial_length)

    ita = np.ones_like(Rs) * 3 / (Rs[1] - Rs[0])**2

    Rs = tf.to_float(np.reshape(Rs, (1, 1, 1, -1)))

    ita = tf.to_float(np.reshape(ita, (1, 1, 1, -1)))

    length = ita.get_shape().as_list()[-1]

    d_cutoff = tf.stack([d_cutoff] * length, axis=3)

    d = tf.stack([d] * length, axis=3)

    out = tf.exp(-ita * tf.square(d - Rs)) * d_cutoff

    if self.atomic_number_differentiated:

    theta = tf.stack([theta] * length, axis=4)

    out_tensor = tf.pow((1. + tf.cos(theta - thetas))/2., zeta) * \

        tf.exp(-ita * tf.square((R_ij + R_ik)/2. - Rs)) * f_R_ij * f_R_ik

        tf.exp(-ita * tf.square((R_ij + R_ik)/2. - Rs)) * f_R_ij * f_R_ik * 2

    if self.atomic_number_differentiated:

      out_tensors = []

# Batch size of models

max_atoms = 23

batch_size = 16

batch_size = 128

layer_structures = [128, 128, 64]

atom_number_cases = [1, 6, 7, 8, 16]

    mode="regression")

# Fit trained model

model.fit(train_dataset, nb_epoch=3000, checkpoint_interval=100)

model.fit(train_dataset, nb_epoch=300, checkpoint_interval=100)

print("Evaluating model")

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