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fix bug of getting ntypes from data system, trainable control for fitting layers #206

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Merged
amcadmus merged 3 commits into from
Mar 30, 2020
Merged

fix bug of getting ntypes from data system, trainable control for fitting layers #206

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d928618
fix bug of getting ntypes from data
Mar 24, 2020
7918287
fix bug of getting ntypes from data
Mar 25, 2020
ba3ed02
set trainable of fitting layers
Mar 30, 2020
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6 changes: 6 additions & 0 deletions source/train/Data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -144,6 +144,12 @@ def get_test (self, ntests = -1) :
self.modifier.modify_data(ret)
return ret

def get_ntypes(self) :
if self.type_map is not None:
return len(self.type_map)
else:
return max(self.get_atom_type()) + 1

def get_type_map(self) :
return self.type_map

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2 changes: 1 addition & 1 deletion source/train/DataSystem.py
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Expand Up @@ -52,7 +52,7 @@ def __init__ (self,
# natoms, nbatches
ntypes = []
for ii in self.data_systems :
ntypes.append(np.max(ii.get_atom_type()) + 1)
ntypes.append(ii.get_ntypes())
self.sys_ntypes = max(ntypes)
self.natoms = []
self.natoms_vec = []
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21 changes: 13 additions & 8 deletions source/train/Fitting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,8 @@ def __init__ (self, jdata, descrpt):
.add('seed', int) \
.add('atom_ener', list, default = [])\
.add("activation_function", str, default = "tanh")\
.add("precision", str, default = "default")
.add("precision", str, default = "default")\
.add("trainable", [list, bool], default = True)
class_data = args.parse(jdata)
self.numb_fparam = class_data['numb_fparam']
self.numb_aparam = class_data['numb_aparam']
Expand All @@ -32,7 +33,11 @@ def __init__ (self, jdata, descrpt):
self.rcond = class_data['rcond']
self.seed = class_data['seed']
self.fitting_activation_fn = get_activation_func(class_data["activation_function"])
self.fitting_precision = get_precision(class_data['precision'])
self.fitting_precision = get_precision(class_data['precision'])
self.trainable = class_data['trainable']
if type(self.trainable) is bool:
self.trainable = [self.trainable] * (len(self.n_neuron)+1)
assert(len(self.trainable) == len(self.n_neuron) + 1), 'length of trainable should be that of n_neuron + 1'
self.atom_ener = []
for at, ae in enumerate(class_data['atom_ener']):
if ae is not None:
Expand Down Expand Up @@ -205,10 +210,10 @@ def build (self,

for ii in range(0,len(self.n_neuron)) :
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii-1] :
layer+= one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, use_timestep = self.resnet_dt, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision)
layer+= one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, use_timestep = self.resnet_dt, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision, trainable = self.trainable[ii])
else :
layer = one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, precision = self.fitting_precision)
final_layer = one_layer(layer, 1, activation_fn = None, bavg = type_bias_ae, name='final_layer_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, precision = self.fitting_precision)
layer = one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, precision = self.fitting_precision, trainable = self.trainable[ii])
final_layer = one_layer(layer, 1, activation_fn = None, bavg = type_bias_ae, name='final_layer_type_'+str(type_i)+suffix, reuse=reuse, seed = self.seed, precision = self.fitting_precision, trainable = self.trainable[-1])

if type_i < len(self.atom_ener) and self.atom_ener[type_i] is not None:
inputs_zero = tf.zeros_like(inputs_i, dtype=global_tf_float_precision)
Expand All @@ -219,10 +224,10 @@ def build (self,
layer = tf.concat([layer, ext_aparam], axis = 1)
for ii in range(0,len(self.n_neuron)) :
if ii >= 1 and self.n_neuron[ii] == self.n_neuron[ii-1] :
layer+= one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=True, seed = self.seed, use_timestep = self.resnet_dt, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision)
layer+= one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=True, seed = self.seed, use_timestep = self.resnet_dt, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision, trainable = self.trainable[ii])
else :
layer = one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=True, seed = self.seed, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision)
zero_layer = one_layer(layer, 1, activation_fn = None, bavg = type_bias_ae, name='final_layer_type_'+str(type_i)+suffix, reuse=True, seed = self.seed, precision = self.fitting_precision)
layer = one_layer(layer, self.n_neuron[ii], name='layer_'+str(ii)+'_type_'+str(type_i)+suffix, reuse=True, seed = self.seed, activation_fn = self.fitting_activation_fn, precision = self.fitting_precision, trainable = self.trainable[ii])
zero_layer = one_layer(layer, 1, activation_fn = None, bavg = type_bias_ae, name='final_layer_type_'+str(type_i)+suffix, reuse=True, seed = self.seed, precision = self.fitting_precision, trainable = self.trainable[-1])
final_layer += self.atom_ener[type_i] - zero_layer

final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0], natoms[2+type_i]])
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10 changes: 7 additions & 3 deletions source/train/Network.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,23 +13,27 @@ def one_layer(inputs,
reuse=None,
seed=None,
use_timestep = False,
trainable = True,
useBN = False):
with tf.variable_scope(name, reuse=reuse):
shape = inputs.get_shape().as_list()
w = tf.get_variable('matrix',
[shape[1], outputs_size],
precision,
tf.random_normal_initializer(stddev=stddev/np.sqrt(shape[1]+outputs_size), seed = seed))
tf.random_normal_initializer(stddev=stddev/np.sqrt(shape[1]+outputs_size), seed = seed),
trainable = trainable)
b = tf.get_variable('bias',
[outputs_size],
precision,
tf.random_normal_initializer(stddev=stddev, mean = bavg, seed = seed))
tf.random_normal_initializer(stddev=stddev, mean = bavg, seed = seed),
trainable = trainable)
hidden = tf.matmul(inputs, w) + b
if activation_fn != None and use_timestep :
idt = tf.get_variable('idt',
[outputs_size],
precision,
tf.random_normal_initializer(stddev=0.001, mean = 0.1, seed = seed))
tf.random_normal_initializer(stddev=0.001, mean = 0.1, seed = seed),
trainable = trainable)
if activation_fn != None:
if useBN:
None
Expand Down