【機械学習】LSTMを使った未来予測

from tensorflow.python.keras.models import Sequential

from tensorflow.python.keras.optimizers import adam_v2

optimizer = adam_v2.Adam(lr=0.001)

from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Activation
from tensorflow.python.keras.layers import LSTM
from tensorflow.python.keras.optimizers import adam_v2
from tensorflow.python.keras.callbacks import EarlyStopping
import numpy as np
import matplotlib.pyplot as plt


def myfunc(t, Fm, f, phi):
    return Fm * np.sin(2 * np.pi * f * t + phi) + np.sin(5 * np.pi * f * t + phi)


# sin波にノイズを付与する
def toy_problem(T=100, ampl=0.05):
    t = np.arange(0, 2 * T + 1)
    noise = ampl * np.random.uniform(low=-1.0, high=1.0, size=len(t))
    Fm = 1
    f = 0.1
    phi = 0
    return myfunc(t, Fm, f, phi) + noise


f = toy_problem()


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)

# 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_v2.Adam(lr=0.001)
model.compile(loss="mean_squared_error", optimizer=optimizer)

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]
          )

# 予測
predicted = model.predict(g)

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()