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add one-sided embedding net #237

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Merged
amcadmus merged 1 commit into from
Jul 20, 2020
Merged

add one-sided embedding net #237

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30 changes: 22 additions & 8 deletions source/train/DescrptSeA.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@ def __init__ (self, jdata):
.add('resnet_dt',bool, default = False) \
.add('trainable',bool, default = True) \
.add('seed', int) \
.add('type_one_side', bool, default = False) \
.add('exclude_types', list, default = []) \
.add('set_davg_zero', bool, default = False) \
.add('activation_function', str, default = 'tanh') \
Expand All @@ -39,6 +40,9 @@ def __init__ (self, jdata):
self.exclude_types.add((tt[0], tt[1]))
self.exclude_types.add((tt[1], tt[0]))
self.set_davg_zero = class_data['set_davg_zero']
self.type_one_side = class_data['type_one_side']
if self.type_one_side and len(exclude_types) != 0:
raise RuntimeError('"type_one_side" is not compatible with "exclude_types"')

# descrpt config
self.sel_r = [ 0 for ii in range(len(self.sel_a)) ]
Expand Down Expand Up @@ -244,17 +248,27 @@ def _pass_filter(self,
inputs = tf.reshape(inputs, [-1, self.ndescrpt * natoms[0]])
output = []
output_qmat = []
for type_i in range(self.ntypes):
inputs_i = tf.slice (inputs,
[ 0, start_index* self.ndescrpt],
[-1, natoms[2+type_i]* self.ndescrpt] )
if not self.type_one_side:
for type_i in range(self.ntypes):
inputs_i = tf.slice (inputs,
[ 0, start_index* self.ndescrpt],
[-1, natoms[2+type_i]* self.ndescrpt] )
inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
layer, qmat = self._filter(tf.cast(inputs_i, self.filter_precision), type_i, name='filter_type_'+str(type_i)+suffix, natoms=natoms, reuse=reuse, seed = self.seed, trainable = trainable, activation_fn = self.filter_activation_fn)
layer = tf.reshape(layer, [tf.shape(inputs)[0], natoms[2+type_i] * self.get_dim_out()])
qmat = tf.reshape(qmat, [tf.shape(inputs)[0], natoms[2+type_i] * self.get_dim_rot_mat_1() * 3])
output.append(layer)
output_qmat.append(qmat)
start_index += natoms[2+type_i]
else :
inputs_i = inputs
inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
layer, qmat = self._filter(tf.cast(inputs_i, self.filter_precision), type_i, name='filter_type_'+str(type_i)+suffix, natoms=natoms, reuse=reuse, seed = self.seed, trainable = trainable, activation_fn = self.filter_activation_fn)
layer = tf.reshape(layer, [tf.shape(inputs)[0], natoms[2+type_i] * self.get_dim_out()])
qmat = tf.reshape(qmat, [tf.shape(inputs)[0], natoms[2+type_i] * self.get_dim_rot_mat_1() * 3])
type_i = -1
layer, qmat = self._filter(tf.cast(inputs_i, self.filter_precision), type_i, name='filter_type_all'+suffix, natoms=natoms, reuse=reuse, seed = self.seed, trainable = trainable, activation_fn = self.filter_activation_fn)
layer = tf.reshape(layer, [tf.shape(inputs)[0], natoms[0] * self.get_dim_out()])
qmat = tf.reshape(qmat, [tf.shape(inputs)[0], natoms[0] * self.get_dim_rot_mat_1() * 3])
output.append(layer)
output_qmat.append(qmat)
start_index += natoms[2+type_i]
output = tf.concat(output, axis = 1)
output_qmat = tf.concat(output_qmat, axis = 1)
return output, output_qmat
Expand Down