def compile_model(self, *args, **kwargs):
print '... building the model'
d_names = [d['name'] for d in self.discriminator_ns]
g_names = [d['name'] for d in self.generator_ns]
assert len(set(d_names).intersection(g_names)) == 0, (
'layer name cannot be the same for generator and discriminator network structure')
total_structure = []
total_structure.extend(self.generator_ns)
total_structure.extend(self.discriminator_ns)
self.name_index_dic = validate_network(total_structure)
self.x = T.matrix('x')
self.z = T.matrix('z')
self.layers = obtain_network(self.batch_size,
total_structure,
self.name_index_dic, self.init_params,
check_output_usage=False)
# this is to facilitate training
sep_index = self.name_index_dic[self.discriminator_ns[0]['name']]
self.gen_layers = self.layers[:sep_index]
self.dis_layers = self.layers[sep_index:]
self.gen_params = [l.params for l in self.gen_layers]
self.dis_params = [l.params for l in self.dis_layers]
def compile_model(self, *args, **kwargs):
print '... building the model'
self.name_index_dic = validate_network(self.network_structure)
self.layers = obtain_network(self.batch_size,
self.network_structure,
self.name_index_dic, self.init_params)
self.params = [l.params for l in self.layers]
def compile_model(self, *args, **kwargs):
print '... building the model'
data_dis_names = [d['name'] for d in self.data_discrim_ns]
prior_dis_names = [d['name'] for d in self.prior_discrim_ns]
enc_names = [d['name'] for d in self.encoder_ns]
dec_names = [d['name'] for d in self.decoder_ns]
assert len(set(prior_dis_names+data_dis_names+enc_names+dec_names)) == len(data_dis_names)+len(prior_dis_names) + len(enc_names) + len(dec_names), \
'layer name cannot be the same for encoder, decoder and discriminator network structure'
total_structure = []
total_structure.extend(self.encoder_ns)
total_structure.extend(self.decoder_ns)
total_structure.extend(self.data_discrim_ns)
total_structure.extend(self.prior_discrim_ns)
self.name_index_dic = validate_network(total_structure)
self.x = T.matrix('x')
self.z = T.matrix('z')
self.layers = obtain_network(self.batch_size,
total_structure,
self.name_index_dic, self.init_params,
check_output_usage=False)
# encoder
sep_index1 = self.name_index_dic[self.decoder_ns[0]['name']]
self.enc_layers = self.layers[:sep_index1]
# decoder
sep_index2 = self.name_index_dic[self.data_discrim_ns[0]['name']]
self.dec_layers = self.layers[sep_index1:sep_index2]
# data discriminator
sep_index3 = self.name_index_dic[self.prior_discrim_ns[0]['name']]
self.data_dis_layers = self.layers[sep_index2:sep_index3]
# prior discriminator
self.prior_dis_layers = self.layers[sep_index3:]
self.enc_params = [l.params for l in self.enc_layers]
self.dec_params = [l.params for l in self.dec_layers]
self.data_dis_params = [l.params for l in self.data_dis_layers]
self.prior_dis_params = [l.params for l in self.prior_dis_layers]