conv_param={
'filter_num': (32, 32, 64),
'filter_size': 3,
'pad': 1,
'stride': 1
},
hidden_size=512,
output_size=10,
weight_init_std=0.01)
# パラメータの復帰
network.load_params("params.pkl")
print("Loaded Network Parameters!")
start = time.time()
test_acc = network.accuracy(x_test, t_test)
elapsed_time = time.time() - start
print("=== " + "test acc:" + str(test_acc) + " ===")
print("elapsed_time:{0}".format(elapsed_time) + "[sec]")
#network.save_params("params.pkl")
#print("Saved Network Parameters!")
np.savetxt('W1.h',
network.params['W1'].reshape(32, -1),
delimiter=',',
newline=',\n',
header='float W1[32][27]={',
footer='};',
comments='')
np.savetxt('mean1.h',
x_test, t_test = x_test[:1000], t_test[:1000]
# ハイパーパラメータの設定
max_epochs = 20
batch_size = 50
model = SimpleConvNet(input_dim=(1, 28, 28),
conv_param={
'filter_num': 30,
'filter_size': 5,
'pad': 0,
'stride': 1
},
hidden_size=100,
output_size=10,
weight_init_std=0.01)
optimizer = AdaGrad(lr=0.001)
trainer = Trainer(model, optimizer)
trainer.fit(x_train,
t_train,
x_test,
t_test,
max_epochs,
batch_size,
eval_interval=10)
# グラフの描画
trainer.plot()
print('test accuracy : ' + str(model.accuracy(x_test, t_test)))
