rid.nn package

Submodules

rid.nn.data module

rid.nn.data.check_new_data(iter_index, train_path, base_path)

rid.nn.data.collect_data(iter_index, base_dir)

rid.nn.data.make_train(iter_index, json_file, base_dir='./')

rid.nn.freeze module

rid.nn.freeze.freeze_model(model_folder, output, output_node_names='o_energy,o_forces')

rid.nn.model module

class rid.nn.model.Model(config, sess)

Bases: object

build_force(inputs, suffix, shift=None, scale=None, reuse=None, graph=None)

compute_statistic(reader)

load_graph(frozen_graph_filename, prefix='load')

test_error(inputs_train)

test_error_mix(reader)

train(reader)

class rid.nn.model.Reader(config)

Bases: object

get_batch_size()

get_data()

get_train_size()

prepare()

sample_train(cat=True)

rid.nn.train_net module

class rid.nn.train_net.Config(cv_dim)

Bases: object

rid.nn.train_net.get_parm()

rid.nn.train_net.print_conf(config, nthreads)

rid.nn.train_net.reset_batch_size(config)

rid.nn.train_net.set_conf(cv_dim, angular_mask, neurons=[240, 120, 60, 30], numb_threads=8, resnet=True, use_mix=False, restart=False, batch_size=128, epoches=12000, lr=0.0008, decay_steps=120, decay_rate=0.96, old_ratio=7.0, decay_steps_inner=0, drop_out_rate=0.5, data_path='./')

rid.nn.train_net.train(cv_dim, angular_mask, neurons=[240, 120, 60, 30], numb_threads=8, resnet=True, use_mix=False, restart=False, batch_size=128, epoches=12000, lr=0.0008, decay_steps=120, decay_rate=0.96, old_ratio=7.0, decay_steps_inner=0, init_model=None, drop_out_rate=0.5, data_path='./')