初心者のRNN(LSTM) | Kerasで試してみる
上記記事ののコードを実行してみる
In [1]:
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras.layers import LSTM
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping
import numpy as np
import matplotlib.pyplot as plt
In [2]:
def sin(x, T=100):
return np.sin(2.0 * np.pi * x / T)
# sin波にノイズを付与する
def toy_problem(T=100, ampl=0.05):
x = np.arange(0, 2 * T + 1)
noise = ampl * np.random.uniform(low=-1.0, high=1.0, size=len(x))
return sin(x) + noise
f = toy_problem()
In [3]:
def make_dataset(low_data, n_prev=100):
data, target = [], []
maxlen = 25
for i in range(len(low_data)-maxlen):
data.append(low_data[i:i + maxlen])
target.append(low_data[i + maxlen])
re_data = np.array(data).reshape(len(data), maxlen, 1)
re_target = np.array(target).reshape(len(data), 1)
return re_data, re_target
#g -> 学習データ，h -> 学習ラベル
g, h = make_dataset(f)
In [4]:
# モデル構築
# 1つの学習データのStep数(今回は25)
length_of_sequence = g.shape[1]
in_out_neurons = 1
n_hidden = 300
model = Sequential()
model.add(LSTM(n_hidden, batch_input_shape=(None, length_of_sequence, in_out_neurons), return_sequences=False))
model.add(Dense(in_out_neurons))
model.add(Activation("linear"))
optimizer = Adam(lr=0.001)
model.compile(loss="mean_squared_error", optimizer=optimizer)
In [5]:
early_stopping = EarlyStopping(monitor='val_loss', mode='auto', patience=20)
model.fit(g, h,
batch_size=300,
epochs=100,
validation_split=0.1,
callbacks=[early_stopping]
)
#【コメント】early_stoppingにより38epochで停止
Out[5]:
In [6]:
# 予測
predicted = model.predict(g)
In [7]:
plt.figure()
plt.plot(range(25,len(predicted)+25),predicted, color="r", label="predict_data")
plt.plot(range(0, len(f)), f, color="b", label="row_data")
plt.legend()
plt.show()
