def build (self, learning_rate, natoms, model_dict, label_dict, suffix): coord = model_dict['coord'] energy = model_dict['energy'] atom_ener = model_dict['atom_ener'] nframes = tf.shape(atom_ener)[0] natoms = tf.shape(atom_ener)[1] # build energy dipole atom_ener0 = atom_ener - tf.reshape(tf.tile(tf.reshape(energy/global_cvt_2_ener_float(natoms), [-1, 1]), [1, natoms]), [nframes, natoms]) coord = tf.reshape(coord, [nframes, natoms, 3]) atom_ener0 = tf.reshape(atom_ener0, [nframes, 1, natoms]) ener_dipole = tf.matmul(atom_ener0, coord) ener_dipole = tf.reshape(ener_dipole, [nframes, 3]) energy_hat = label_dict['energy'] ener_dipole_hat = label_dict['energy_dipole'] find_energy = label_dict['find_energy'] find_ener_dipole = label_dict['find_energy_dipole'] l2_ener_loss = tf.reduce_mean( tf.square(energy - energy_hat), name='l2_'+suffix) ener_dipole_reshape = tf.reshape(ener_dipole, [-1]) ener_dipole_hat_reshape = tf.reshape(ener_dipole_hat, [-1]) l2_ener_dipole_loss = tf.reduce_mean( tf.square(ener_dipole_reshape - ener_dipole_hat_reshape), name='l2_'+suffix) # atom_norm_ener = 1./ global_cvt_2_ener_float(natoms[0]) atom_norm_ener = 1./ global_cvt_2_ener_float(natoms) pref_e = global_cvt_2_ener_float(find_energy * (self.limit_pref_e + (self.start_pref_e - self.limit_pref_e) * learning_rate / self.starter_learning_rate) ) pref_ed = global_cvt_2_tf_float(find_ener_dipole * (self.limit_pref_ed + (self.start_pref_ed - self.limit_pref_ed) * learning_rate / self.starter_learning_rate) ) l2_loss = 0 more_loss = {} l2_loss += atom_norm_ener * (pref_e * l2_ener_loss) l2_loss += global_cvt_2_ener_float(pref_ed * l2_ener_dipole_loss) more_loss['l2_ener_loss'] = l2_ener_loss more_loss['l2_ener_dipole_loss'] = l2_ener_dipole_loss self.l2_loss_summary = tf.summary.scalar('l2_loss', tf.sqrt(l2_loss)) self.l2_loss_ener_summary = tf.summary.scalar('l2_ener_loss', tf.sqrt(l2_ener_loss) / global_cvt_2_tf_float(natoms[0])) self.l2_ener_dipole_loss_summary = tf.summary.scalar('l2_ener_dipole_loss', tf.sqrt(l2_ener_dipole_loss)) self.l2_l = l2_loss self.l2_more = more_loss return l2_loss, more_loss
def build(self, coord_, atype_, natoms, box, mesh, input_dict, frz_model=None, suffix='', reuse=None): with tf.variable_scope('model_attr' + suffix, reuse=reuse): t_tmap = tf.constant(' '.join(self.type_map), name='tmap', dtype=tf.string) t_mt = tf.constant(self.model_type, name='model_type', dtype=tf.string) t_ver = tf.constant(MODEL_VERSION, name='model_version', dtype=tf.string) if self.srtab is not None: tab_info, tab_data = self.srtab.get() self.tab_info = tf.get_variable( 't_tab_info', tab_info.shape, dtype=tf.float64, trainable=False, initializer=tf.constant_initializer(tab_info, dtype=tf.float64)) self.tab_data = tf.get_variable( 't_tab_data', tab_data.shape, dtype=tf.float64, trainable=False, initializer=tf.constant_initializer(tab_data, dtype=tf.float64)) coord = tf.reshape(coord_, [-1, natoms[1] * 3]) atype = tf.reshape(atype_, [-1, natoms[1]]) # type embedding if any if self.typeebd is not None: type_embedding = self.typeebd.build( self.ntypes, reuse=reuse, suffix=suffix, ) input_dict['type_embedding'] = type_embedding if frz_model == None: dout \ = self.descrpt.build(coord_, atype_, natoms, box, mesh, input_dict, suffix = suffix, reuse = reuse) dout = tf.identity(dout, name='o_descriptor') else: tf.constant(self.rcut, name='descrpt_attr/rcut', dtype=GLOBAL_TF_FLOAT_PRECISION) tf.constant(self.ntypes, name='descrpt_attr/ntypes', dtype=tf.int32) feed_dict = self.descrpt.get_feed_dict(coord_, atype_, natoms, box, mesh) return_elements = [ *self.descrpt.get_tensor_names(), 'o_descriptor:0' ] imported_tensors \ = self._import_graph_def_from_frz_model(frz_model, feed_dict, return_elements) dout = imported_tensors[-1] self.descrpt.pass_tensors_from_frz_model(*imported_tensors[:-1]) if self.srtab is not None: nlist, rij, sel_a, sel_r = self.descrpt.get_nlist() nnei_a = np.cumsum(sel_a)[-1] nnei_r = np.cumsum(sel_r)[-1] atom_ener = self.fitting.build(dout, natoms, input_dict, reuse=reuse, suffix=suffix) if self.srtab is not None: sw_lambda, sw_deriv \ = op_module.soft_min_switch(atype, rij, nlist, natoms, sel_a = sel_a, sel_r = sel_r, alpha = self.smin_alpha, rmin = self.sw_rmin, rmax = self.sw_rmax) inv_sw_lambda = 1.0 - sw_lambda # NOTICE: # atom energy is not scaled, # force and virial are scaled tab_atom_ener, tab_force, tab_atom_virial \ = op_module.pair_tab(self.tab_info, self.tab_data, atype, rij, nlist, natoms, sw_lambda, sel_a = sel_a, sel_r = sel_r) energy_diff = tab_atom_ener - tf.reshape(atom_ener, [-1, natoms[0]]) tab_atom_ener = tf.reshape(sw_lambda, [-1]) * tf.reshape( tab_atom_ener, [-1]) atom_ener = tf.reshape(inv_sw_lambda, [-1]) * atom_ener energy_raw = tab_atom_ener + atom_ener else: energy_raw = atom_ener energy_raw = tf.reshape(energy_raw, [-1, natoms[0]], name='o_atom_energy' + suffix) energy = tf.reduce_sum(global_cvt_2_ener_float(energy_raw), axis=1, name='o_energy' + suffix) force, virial, atom_virial \ = self.descrpt.prod_force_virial (atom_ener, natoms) if self.srtab is not None: sw_force \ = op_module.soft_min_force(energy_diff, sw_deriv, nlist, natoms, n_a_sel = nnei_a, n_r_sel = nnei_r) force = force + sw_force + tab_force force = tf.reshape(force, [-1, 3 * natoms[1]], name="o_force" + suffix) if self.srtab is not None: sw_virial, sw_atom_virial \ = op_module.soft_min_virial (energy_diff, sw_deriv, rij, nlist, natoms, n_a_sel = nnei_a, n_r_sel = nnei_r) atom_virial = atom_virial + sw_atom_virial + tab_atom_virial virial = virial + sw_virial \ + tf.reduce_sum(tf.reshape(tab_atom_virial, [-1, natoms[1], 9]), axis = 1) virial = tf.reshape(virial, [-1, 9], name="o_virial" + suffix) atom_virial = tf.reshape(atom_virial, [-1, 9 * natoms[1]], name="o_atom_virial" + suffix) model_dict = {} model_dict['energy'] = energy model_dict['force'] = force model_dict['virial'] = virial model_dict['atom_ener'] = energy_raw model_dict['atom_virial'] = atom_virial model_dict['coord'] = coord model_dict['atype'] = atype return model_dict
def build (self, learning_rate, natoms, model_dict, label_dict, suffix): energy = model_dict['energy'] force = model_dict['force'] virial = model_dict['virial'] atom_ener = model_dict['atom_ener'] energy_hat = label_dict['energy'] force_hat = label_dict['force'] virial_hat = label_dict['virial'] atom_ener_hat = label_dict['atom_ener'] atom_pref = label_dict['atom_pref'] find_energy = label_dict['find_energy'] find_force = label_dict['find_force'] find_virial = label_dict['find_virial'] find_atom_ener = label_dict['find_atom_ener'] find_atom_pref = label_dict['find_atom_pref'] l2_ener_loss = tf.reduce_mean( tf.square(energy - energy_hat), name='l2_'+suffix) force_reshape = tf.reshape (force, [-1]) force_hat_reshape = tf.reshape (force_hat, [-1]) atom_pref_reshape = tf.reshape (atom_pref, [-1]) diff_f = force_hat_reshape - force_reshape if self.relative_f is not None: force_hat_3 = tf.reshape(force_hat, [-1, 3]) norm_f = tf.reshape(tf.norm(force_hat_3, axis = 1), [-1, 1]) + self.relative_f diff_f_3 = tf.reshape(diff_f, [-1, 3]) diff_f_3 = diff_f_3 / norm_f diff_f = tf.reshape(diff_f_3, [-1]) l2_force_loss = tf.reduce_mean(tf.square(diff_f), name = "l2_force_" + suffix) l2_pref_force_loss = tf.reduce_mean(tf.multiply(tf.square(diff_f), atom_pref_reshape), name = "l2_pref_force_" + suffix) virial_reshape = tf.reshape (virial, [-1]) virial_hat_reshape = tf.reshape (virial_hat, [-1]) l2_virial_loss = tf.reduce_mean (tf.square(virial_hat_reshape - virial_reshape), name = "l2_virial_" + suffix) atom_ener_reshape = tf.reshape (atom_ener, [-1]) atom_ener_hat_reshape = tf.reshape (atom_ener_hat, [-1]) l2_atom_ener_loss = tf.reduce_mean (tf.square(atom_ener_hat_reshape - atom_ener_reshape), name = "l2_atom_ener_" + suffix) atom_norm = 1./ global_cvt_2_tf_float(natoms[0]) atom_norm_ener = 1./ global_cvt_2_ener_float(natoms[0]) pref_e = global_cvt_2_ener_float(find_energy * (self.limit_pref_e + (self.start_pref_e - self.limit_pref_e) * learning_rate / self.starter_learning_rate) ) pref_f = global_cvt_2_tf_float(find_force * (self.limit_pref_f + (self.start_pref_f - self.limit_pref_f) * learning_rate / self.starter_learning_rate) ) pref_v = global_cvt_2_tf_float(find_virial * (self.limit_pref_v + (self.start_pref_v - self.limit_pref_v) * learning_rate / self.starter_learning_rate) ) pref_ae= global_cvt_2_tf_float(find_atom_ener * (self.limit_pref_ae+ (self.start_pref_ae-self.limit_pref_ae) * learning_rate / self.starter_learning_rate) ) pref_pf= global_cvt_2_tf_float(find_atom_pref * (self.limit_pref_pf+ (self.start_pref_pf-self.limit_pref_pf) * learning_rate / self.starter_learning_rate) ) l2_loss = 0 more_loss = {} if self.has_e : l2_loss += atom_norm_ener * (pref_e * l2_ener_loss) more_loss['l2_ener_loss'] = l2_ener_loss if self.has_f : l2_loss += global_cvt_2_ener_float(pref_f * l2_force_loss) more_loss['l2_force_loss'] = l2_force_loss if self.has_v : l2_loss += global_cvt_2_ener_float(atom_norm * (pref_v * l2_virial_loss)) more_loss['l2_virial_loss'] = l2_virial_loss if self.has_ae : l2_loss += global_cvt_2_ener_float(pref_ae * l2_atom_ener_loss) more_loss['l2_atom_ener_loss'] = l2_atom_ener_loss if self.has_pf : l2_loss += global_cvt_2_ener_float(pref_pf * l2_pref_force_loss) more_loss['l2_pref_force_loss'] = l2_pref_force_loss # only used when tensorboard was set as true self.l2_loss_summary = tf.summary.scalar('l2_loss', tf.sqrt(l2_loss)) self.l2_loss_ener_summary = tf.summary.scalar('l2_ener_loss', global_cvt_2_tf_float(tf.sqrt(l2_ener_loss)) / global_cvt_2_tf_float(natoms[0])) self.l2_loss_force_summary = tf.summary.scalar('l2_force_loss', tf.sqrt(l2_force_loss)) self.l2_loss_virial_summary = tf.summary.scalar('l2_virial_loss', tf.sqrt(l2_virial_loss) / global_cvt_2_tf_float(natoms[0])) self.l2_l = l2_loss self.l2_more = more_loss return l2_loss, more_loss