1 Star 1 Fork 0

郭少强/deeplearning-note

Create your Gitee Account
Explore and code with more than 13.5 million developers,Free private repositories !:)
Sign up
文件
This repository doesn't specify license. Please pay attention to the specific project description and its upstream code dependency when using it.
Clone or Download
sin.py 5.13 KB
Copy Edit Raw Blame History
Mario Cho authored 5 years ago . Create sin.py
import numpy as np # 配列
import time # 時間
from matplotlib import pyplot as plt # グラフ
import os # フォルダ作成のため
# pytorch
import torch as T
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
# y=sin(x)のデータセットをN個分作成
def get_data(N, Nte):
x = np.linspace(0, 2 * np.pi, N+Nte)
# 学習データとテストデータに分ける
ram = np.random.permutation(N+Nte)
x_train = np.sort(x[ram[:N]])
x_test = np.sort(x[ram[N:]])
t_train = np.sin(x_train)
t_test = np.sin(x_test)
return x_train, t_train, x_test, t_test
# Neural Network
class SIN_NN(nn.Module):
def __init__(self, h_units, act):
super(SIN_NN, self).__init__()
self.l1=nn.Linear(1, h_units[0])
self.l2=nn.Linear(h_units[0], h_units[1])
self.l3=nn.Linear(h_units[1], 1)
if act == "relu":
self.act = F.relu
elif act == "sig":
self.act = F.sigmoid
def __call__(self, x, t):
x = T.from_numpy(x.astype(np.float32).reshape(x.shape[0],1))
t = T.from_numpy(t.astype(np.float32).reshape(t.shape[0],1))
y = self.forward(x)
return y, t
def forward(self, x):
h = self.act(self.l1(x))
h = self.act(self.l2(h))
h = self.l3(h)
return h
def predict(self, x):
x = T.from_numpy(x.astype(np.float32).reshape(x.shape[0],1))
y = self.forward(x)
return y.data
def training(N, Nte, bs, n_epoch, h_units, act):
# データセットの取得
x_train, t_train, x_test, t_test = get_data(N, Nte)
x_test_torch = T.from_numpy(x_test.astype(np.float32).reshape(x_test.shape[0],1))
t_test_torch = T.from_numpy(t_test.astype(np.float32).reshape(t_test.shape[0],1))
# モデルセットアップ
model = SIN_NN(h_units, act)
optimizer = optim.Adam(model.parameters())
MSE = nn.MSELoss()
# loss格納配列
tr_loss = []
te_loss = []
# ディレクトリを作成
if os.path.exists("Results/{}/Pred".format(act)) == False:
os.makedirs("Results/{}/Pred".format(act))
# 時間を測定
start_time = time.time()
print("START")
# 学習回数分のループ
for epoch in range(1, n_epoch + 1):
model.train()
perm = np.random.permutation(N)
sum_loss = 0
for i in range(0, N, bs):
x_batch = x_train[perm[i:i + bs]]
t_batch = t_train[perm[i:i + bs]]
optimizer.zero_grad()
y_batch, t_batch = model(x_batch, t_batch)
loss = MSE(y_batch, t_batch)
loss.backward()
optimizer.step()
sum_loss += loss.data * bs
# 学習誤差の平均を計算
ave_loss = sum_loss / N
tr_loss.append(ave_loss)
# テスト誤差
model.eval()
y_test_torch = model.forward(x_test_torch)
loss = MSE(y_test_torch, t_test_torch)
te_loss.append(loss.data)
# 学習過程を出力
if epoch % 100 == 1:
print("Ep/MaxEp tr_loss te_loss")
if epoch % 10 == 0:
print("{:4}/{} {:10.5} {:10.5}".format(epoch, n_epoch, ave_loss, float(loss.data)))
# 誤差をリアルタイムにグラフ表示
plt.plot(tr_loss, label = "training")
plt.plot(te_loss, label = "test")
plt.yscale('log')
plt.legend()
plt.grid(True)
plt.xlabel("epoch")
plt.ylabel("loss (MSE)")
plt.pause(0.1) # このコードによりリアルタイムにグラフが表示されたように見える
plt.clf()
if epoch % 20 == 0:
# epoch20ごとのテスト予測結果
plt.figure(figsize=(5, 4))
y_test = model.predict(x_test)
plt.plot(x_test, t_test, label = "target")
plt.plot(x_test, y_test, label = "predict")
plt.legend()
plt.grid(True)
plt.xlim(0, 2 * np.pi)
plt.ylim(-1.2, 1.2)
plt.xlabel("x")
plt.ylabel("y")
plt.savefig("Results/{}/Pred/ep{}.png".format(act,epoch))
plt.clf()
plt.close()
print("END")
# 経過時間
total_time = int(time.time() - start_time)
print("Time : {} [s]".format(total_time))
# 誤差のグラフ作成
plt.figure(figsize=(5, 4))
plt.plot(tr_loss, label = "training")
plt.plot(te_loss, label = "test")
plt.yscale('log')
plt.legend()
plt.grid(True)
plt.xlabel("epoch")
plt.ylabel("loss (MSE)")
plt.savefig("Results/{}/loss_history.png".format(act))
plt.clf()
plt.close()
# 学習済みモデルの保存
T.save(model, "Results/model.pt")
if __name__ == "__main__":
# 設定
N = 1000 # 学習データ
Nte = 200 # テストデータ数
bs = 10 # バッチサイズ
n_epoch = 200 # 学習回数
h_units = [10, 10] # ユニット数 [中間層1 中間層2]
act = "relu" # 活性化関数(ReLU関数にしたい場合は、"relu")
training(N, Nte, bs, n_epoch, h_units, act)
Loading...
马建仓 AI 助手
尝试更多
代码解读
代码找茬
代码优化
1
https://gitee.com/guo_shaoqiang/deeplearning-note.git
git@gitee.com:guo_shaoqiang/deeplearning-note.git
guo_shaoqiang
deeplearning-note
deeplearning-note
master

Search