simple_conv_net_learning_execute.py

import sys,os
sys.path.append(os.pardir)
import numpy as np

from dataset.mnist import load_mnist
from simple_conv_net import SimpleConvNet

# データの読み込み
(x_train,t_train),(x_test,t_test) = load_mnist(normalize=True,one_hot_label=True)

network = TwoLayerNet(input_size=784,hidden_size=50,output_size=10)
network = SimpleConvNet()

iters_num = 10000
train_size = x_train.shape[0]
batch_size = 100
learning_rate = 0.01

train_loss_list = []
train_acc_list = []
test_acc_list = []


iter_per_epoch = max(train_size / batch_size, 1)

for i in range(iters_num):
    batch_mask = np.random.choice(train_size,batch_size)
    x_batch = x_train[batch_mask]
    t_batch = t_train[batch_mask]

    # 誤差逆伝播法によって勾配を求める
    grad = network.backprop_gradient(x_batch,t_batch)
    # 数値微分によって勾配を求める
    # grad = network.numerical_gradient(x_batch,t_batch)

    # パラメータの更新
    for key in ('W1','b1','W2','b2'):
        network.params[key] -= learning_rate * grad[key]

    loss = network.loss(x_batch,t_batch)
    train_loss_list.append(loss)

    if i % iter_per_epoch == 0:
        train_acc = network.accuracy(x_train,t_train)
        test_acc = network.accuracy(x_test,t_test)
        train_acc_list.append(train_acc)
        test_acc_list.append(test_acc)
        print(train_acc,test_acc)