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
def __init__(self):
self.onl = Online()
self.vis = Metric_Visualizer()
self.trainer = Trainer(sess_type="online")
self.serv = ExperienceServer(self.ob_callback, deserialize_obs(), 4)
self.exp_path = self.trainer.get_exp_path()
self.modelpath = self.trainer.get_model_path()
self.modelname = self.trainer.get_model_name()
m.patch()
def setUp(self):
self.trainer = Trainer(None, None)
self.dataset = DatasetwithCombinedInput(SimpleDataset(num = 2, size = self.dataset_size))
params = TrainerParams()
params.batch_size = 10
params.val_check_period = 0
params.max_epochs = 1
self.params = params
tf.reset_default_graph()
from nnet.Metric_Visualizer import Metric_Visualizer
from common.Trainer import Trainer
import os, pdb, glob
vis = Metric_Visualizer()
trainer = Trainer()
exp_path = trainer.get_exp_path()
listy = os.listdir(os.path.join(exp_path, 'data'))
for i in range(len(listy)):
pkl_name = 'batch' + str(i)
vis.vid_from_pklpath(os.path.join(exp_path, 'data', pkl_name),
0,
0,
show_steer=True,
units='rad',
live=True,
wk=0)
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!")
# Dropuoutの有無、割り合いの設定 ========================
use_dropout = True # Dropoutなしのときの場合はFalseに
dropout_ratio = 0.15
# ====================================================
network = MultiLayerNetExtend(input_size=784,
hidden_size_list=[100, 100, 100, 100, 100, 100],
output_size=10,
use_dropout=use_dropout,
dropout_ration=dropout_ratio)
trainer = Trainer(network,
x_train,
t_train,
x_test,
t_test,
epochs=601,
mini_batch_size=100,
optimizer='sgd',
optimizer_param={'lr': 0.01},
verbose=True)
trainer.train()
train_acc_list, test_acc_list = trainer.train_acc_list, trainer.test_acc_list
# グラフの描画==========
markers = {'train': 'o', 'test': 's'}
x = np.arange(len(train_acc_list))
plt.plot(x, train_acc_list, marker='o', label='train', markevery=10)
plt.plot(x, test_acc_list, marker='s', label='test', markevery=10)
plt.xlabel("epochs")
plt.ylabel("accuracy")