def __train(lr, weight_decay, epocs=50): network = MultiLayerNet(input_size=784, hidden_size_list=[100, 100, 100, 100, 100, 100], output_size=10, weight_decay_lambda=weight_decay) trainer = Trainer(network, x_train, t_train, x_val, t_val, epochs=epocs, mini_batch_size=100, optimizer='sgd', optimizer_param={'lr': lr}, verbose=False) trainer.train() return trainer.test_acc_list, trainer.train_acc_list
element_dict = { 'encoder': ConvEncoder(config.convs, DenseElement), #CapsEncoder(),#ConvEncoder(), 'predictor': EmptyElementConfig( ), #DensePredict(),#CapsPredict(),#EmptyElementConfig(), } super().__init__(modes_dict, 'classification', element_dict, config=config) dataset = SimpleDataset(num=2) save_folder = 'simple-test' params = TrainerParams(0.001) params.batch_size = 1 params.val_check_period = 0 params.max_epochs = 100 network_base = SimpleTestNetwork() network = Network(network_base, *network_base.get_functions_for_trainer()) tf.reset_default_graph() trainer = Trainer(network, dataset, params) saver = CustomSaver(folders=[ save_folder + '/classification', save_folder + '/classification/epoch' ]) trainer.train(saver, restore_from_epochend=True)
# coding: utf-8 import sys sys.path.append('../../') import numpy as np import matplotlib.pyplot as plt from data.mnist import load_mnist from DeepCNN import DeepCNN from common.Trainer import Trainer (x_train, t_train), (x_test, t_test) = load_mnist(flatten=False) network = DeepCNN() trainer = Trainer(network, x_train, t_train, x_test, t_test, epochs=20, mini_batch_size=100, optimizer='Adam', optimizer_param={'lr': 0.001}, evaluate_sample_num_per_epoch=1000) trainer.train() # パラメータの保存 network.save_params("deep_convnet_params.pkl") print("Saved Network Parameters!")