Python DeepConvNet.save_paramsの例

コード例 #1

# coding: utf-8
import os
import sys

sys.path.append(os.pardir)  # 부모 디렉터리의 파일을 가져올 수 있도록 설정
from dataset.mnist import load_mnist
from deep_convnet import DeepConvNet
from common.trainer import Trainer

(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)

network = DeepConvNet()
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()

# 매개변수 보관
network.save_params("deep_convnet_params.pkl")
print("Saved Network Parameters!")

コード例 #2

ファイル: train_deepnet.py プロジェクト: Sai628/deep-learning-from-scratch

# coding=utf-8

import os
import sys
sys.path.append(os.pardir)  # 为了导入父目录中的文件而进行的设定

from dataset.mnist import load_mnist
from deep_convnet import DeepConvNet
from common.trainer import Trainer

(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
network = DeepConvNet()
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()

# 保存参数
network.save_params('deep_convnet_params.pkl')
print('Saved Network Parameters!')

コード例 #3

ファイル: train_deepnet.py プロジェクト: ym3k/dl_from_scratch

import numpy as np
import matplotlib.pyplot as plt
from dataset.mnist import load_mnist
from deep_convnet import DeepConvNet
from common.trainer import Trainer

opt = sys.argv[1]
print(opt)
if opt is None:
    opt = 'Adam'

(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)

network = DeepConvNet()
trainer = Trainer(network,
                  x_train,
                  t_train,
                  x_test,
                  t_test,
                  epochs=20,
                  mini_batch_size=100,
                  optimizer=opt,
                  optimizer_param={},
                  evaluate_sample_num_per_epoch=1000,
                  verbose2=True)
trainer.train()

# パラメータの保存
network.save_params(opt + "_deep_convnet_params.pkl")
print("Saved Network Parameters!")

コード例 #4

ファイル: train_deepnet.py プロジェクト: yammmt/dl-from-scratch

import matplotlib.pyplot as plt
import numpy as np
import os
import sys
sys.path.append(os.pardir)
from dataset.mnist import load_mnist
from deep_convnet import DeepConvNet
from common.trainer import Trainer

(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)

network = DeepConvNet()
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()

# save params
network.save_params('myparams.pkl')
print('Saved Network Parameters!')

コード例 #5

ファイル: train_deepnet.py プロジェクト: jyrhee/deep-learning-from-scratch

# coding: utf-8
import sys, os
sys.path.append(os.pardir)  # 부모 디렉터리의 파일을 가져올 수 있도록 설정
import numpy as np
import matplotlib.pyplot as plt
from dataset.mnist import load_mnist
from deep_convnet import DeepConvNet
from common.trainer import Trainer

(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)

network = DeepConvNet()  
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()

# 매개변수 보관
network.save_params("deep_convnet_params.pkl")
print("Saved Network Parameters!")