準備¶
Googleドライブのマウント¶
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from google.colab import drive
drive.mount('/content/drive')
sys.pathの設定¶
以下では，Googleドライブのマイドライブ直下にDNN_codeフォルダを置くことを仮定しています．必要に応じて，パスを変更してください．
In [2]:
import sys
sys.path.append('/content/drive/My Drive/DNN_code_colab_ver200425')
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import numpy as np
from common import functions
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
np.random.seed(0)
# sin曲線
round_num = 10
div_num = 500
ts = np.linspace(0, round_num * np.pi, div_num)
f = np.sin(ts)
def d_tanh(x):
return 1/(np.cosh(x)**2 + 1e-4)
# ひとつの時系列データの長さ
maxlen = 5
# sin波予測の入力データ
test_head = [[f[k]] for k in range(0, maxlen)]
data = []
target = []
for i in range(div_num - maxlen):
data.append(f[i: i + maxlen])
target.append(f[i + maxlen])
X = np.array(data).reshape(len(data), maxlen, 1)
D = np.array(target).reshape(len(data), 1)
# データ設定
N_train = int(len(data) * 0.8)
N_validation = len(data) - N_train
x_train, x_test, d_train, d_test = train_test_split(X, D, test_size=N_validation)
input_layer_size = 1
hidden_layer_size = 5
output_layer_size = 1
weight_init_std = 0.01
learning_rate = 0.1
iters_num = 3000
# ウェイト初期化 (バイアスは簡単のため省略)
W_in = weight_init_std * np.random.randn(input_layer_size, hidden_layer_size)
W_out = weight_init_std * np.random.randn(hidden_layer_size, output_layer_size)
W = weight_init_std * np.random.randn(hidden_layer_size, hidden_layer_size)
# 勾配
W_in_grad = np.zeros_like(W_in)
W_out_grad = np.zeros_like(W_out)
W_grad = np.zeros_like(W)
us = []
zs = []
u = np.zeros(hidden_layer_size)
z = np.zeros(hidden_layer_size)
y = np.zeros(output_layer_size)
delta_out = np.zeros(output_layer_size)
delta = np.zeros(hidden_layer_size)
losses = []
# トレーニング
for i in range(iters_num):
for s in range(x_train.shape[0]):
us.clear()
zs.clear()
z *= 0
# sにおける正解データ
d = d_train[s]
xs = x_train[s]
# 時系列ループ
for t in range(maxlen):
# 入力値
x = xs[t]
u = np.dot(x, W_in) + np.dot(z, W)
us.append(u)
z = np.tanh(u)
zs.append(z)
y = np.dot(z, W_out)
#誤差
loss = functions.mean_squared_error(d, y)
delta_out = functions.d_mean_squared_error(d, y)
delta *= 0
for t in range(maxlen)[::-1]:
delta = (np.dot(delta, W.T) + np.dot(delta_out, W_out.T)) * d_tanh(us[t])
# 勾配更新
W_grad += np.dot(zs[t].reshape(-1,1), delta.reshape(1,-1))
W_in_grad += np.dot(xs[t], delta.reshape(1,-1))
W_out_grad = np.dot(z.reshape(-1,1), delta_out)
# 勾配適用
W -= learning_rate * W_grad
W_in -= learning_rate * W_in_grad
W_out -= learning_rate * W_out_grad.reshape(-1,1)
W_in_grad *= 0
W_out_grad *= 0
W_grad *= 0
# テスト
for s in range(x_test.shape[0]):
z *= 0
# sにおける正解データ
d = d_test[s]
xs = x_test[s]
# 時系列ループ
for t in range(maxlen):
# 入力値
x = xs[t]
u = np.dot(x, W_in) + np.dot(z, W)
z = np.tanh(u)
y = np.dot(z, W_out)
#誤差
loss = functions.mean_squared_error(d, y)
print('loss:', loss, ' d:', d, ' y:', y)
original = np.full(maxlen, None)
pred_num = 200
xs = test_head
# sin波予測
for s in range(0, pred_num):
z *= 0
for t in range(maxlen):
# 入力値
x = xs[t]
u = np.dot(x, W_in) + np.dot(z, W)
z = np.tanh(u)
y = np.dot(z, W_out)
original = np.append(original, y)
xs = np.delete(xs, 0)
xs = np.append(xs, y)
plt.figure()
plt.ylim([-1.5, 1.5])
plt.plot(np.sin(np.linspace(0, round_num* pred_num / div_num * np.pi, pred_num)), linestyle='dotted', color='#aaaaaa')
plt.plot(original, linestyle='dashed', color='black')
plt.show()
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