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!")