save_model(tensor_params_list=generator_params + generator_entropy_params+ energy_params,
save_to=save_as)
if __name__=="__main__":
model_config_dict = OrderedDict()
model_config_dict['batch_size'] = 128
model_config_dict['num_display'] = 16*16
model_config_dict['hidden_distribution'] = 1.
model_config_dict['epochs'] = 200
#################
# LOAD DATA SET #
#################
_ , data_stream = bedroom(batch_size=model_config_dict['batch_size'])
expert_size_list = [1024]
hidden_size_list = [100]
num_filters_list = [128]
lr_list = [1e-3]
lambda_eng_list = [1e-10]
lambda_gen_list = [1e-10]
for lr in lr_list:
for num_filters in num_filters_list:
for hidden_size in hidden_size_list:
for expert_size in expert_size_list:
for lambda_eng in lambda_eng_list:
for lambda_gen in lambda_gen_list:
model_config_dict['hidden_size'] = hidden_size
if batch_count%1000==0:
# sample data
save_as = samples_dir + '/' + model_name + '_SAMPLES{}.png'.format(batch_count)
sample_data = sampling_function(fixed_hidden_data)[0]
sample_data = np.asarray(sample_data)
color_grid_vis(inverse_transform(sample_data).transpose([0,2,3,1]), (16, 16), save_as)
np.save(file=samples_dir + '/' + model_name +'_MOMENT_COST',
arr=np.asarray(moment_cost_list))
if __name__=="__main__":
batch_size = 128
num_epochs = 100
_ , data_stream = bedroom(batch_size=batch_size)
num_hiddens = 100
learning_rate = 1e-3
l2_weight = 1e-10
generator_optimizer = Adagrad(lr=sharedX(learning_rate),
regularizer=Regularizer(l2=l2_weight))
model_test_name = model_name \
+ '_HIDDEN{}'.format(int(num_hiddens)) \
+ '_REG{}'.format(int(-np.log10(l2_weight))) \
+ '_LR{}'.format(int(-np.log10(learning_rate))) \
train_model(model_name=model_test_name,