# 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 ch08.deep_convnet import DeepConvNet
from dataset.mnist import load_mnist
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
network = DeepConvNet()
network.load_params("deep_convnet_params.pkl")
sampled = 10000 # 고속화를 위한 표본추출
x_test = x_test[:sampled]
t_test = t_test[:sampled]
print("caluculate accuracy (float64) ... ")
print(network.accuracy(x_test, t_test))
# float16(반정밀도)로 형변환
x_test = x_test.astype(np.float16)
for param in network.params.values():
param[...] = param.astype(np.float16)
print("caluculate accuracy (float16) ... ")
print(network.accuracy(x_test, t_test))
# coding: utf-8
import os
import sys
sys.path.append(os.pardir) # 親ディレクトリのファイルをインポートするための設定
from dataset.mnist import load_mnist
from ch08.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()
# パラメータの保存
network.save_params("deep_convnet_params.pkl")
print("Saved Network Parameters!")
# 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 ch08.deep_convnet import DeepConvNet
from trainer import Trainer
batch_size = 500
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
x_test, t_test = x_test[:batch_size], t_test[:batch_size]
# print(x_test)
# print(t_test)
network = DeepConvNet()
network.load_params("deep_convnet_params.pkl")
x = network.predict(x_test, train_flag=False)
x = np.argmax(x, axis=1)
r = np.sum(t_test == x) / batch_size * 100
print(r)
# coding: utf-8
import os
import sys
sys.path.append(os.pardir) # 親ディレクトリのファイルをインポートするための設定
import numpy as np
import matplotlib.pyplot as plt
from ch08.deep_convnet import DeepConvNet
from dataset.mnist import load_mnist
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
network = DeepConvNet()
network.load_params("deep_convnet_params.pkl")
print("calculating test accuracy ... ")
# sampled = 1000
# x_test = x_test[:sampled]
# t_test = t_test[:sampled]
classified_ids = []
acc = 0.0
batch_size = 100
for i in range(int(x_test.shape[0] / batch_size)):
tx = x_test[i * batch_size:(i + 1) * batch_size]
tt = t_test[i * batch_size:(i + 1) * batch_size]
y = network.predict(tx, train_flg=False)
y = np.argmax(y, axis=1)
classified_ids.append(y)
import sys, os
sys.path.append(os.pardir) # 부모 디렉터리의 파일을 가져올 수 있도록 설정
from dataset.mnist import load_mnist
from ch08.deep_convnet import DeepConvNet
from common.trainer import Trainer
import numpy as np
import matplotlib.pyplot as plt
(x_train, t_train), (x_test, t_test) = load_mnist(flatten=False)
# 시간이 오래 걸릴 경우 데이터를 줄인다.
x_train, t_train = x_train[:5000], t_train[:5000]
x_test, t_test = x_test[:1000], t_test[:1000]
network = DeepConvNet()
trainer = Trainer(network,
x_train,
t_train,
x_test,
t_test,
epochs=1,
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!")