from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.model_selection import train_test_split
model = Sequential()
model.add(LSTM( 10, input_shape=(1, 1)))
model.add(Dense(1, activation="linear"))
model.compile(loss="mse", optimizer="adam")
X, y = get_data()
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=1)
X_train_2, X_val, y_train_2, y_val = train_test_split(X_train, y_train, test_size=0.25, random_state=1)
model.fit(X_train, y_train, epochs=800, validation_data=(X_val, y_val), shuffle=False)
html, body, table, thead, input, textarea, select {color: #bab5ab!important; background: #35393b;} input[type="text"], textarea, select {color: #bab5ab!important; background: #35393b;} [data-darksite-inline-background-image-gradient] {background: linear-gradient(rgba(0, 0, 0, 0.5), rgba(0, 0, 0, 0.5))!important; -webkit-background-size: cover!important; -moz-background-size: cover!important; -o-background-size: cover!important; background-size: cover!important;} [data-darksite-force-inline-background] * {background-color: rgba(0,0,0,0.7)!important;} [data-darksite-inline-background] {background-color: rgba(0,0,0,0.7)!important;} [data-darksite-inline-color] {color: #fff!important;} [data-darksite-inline-background-image] {background-image: linear-gradient(rgba(0,0,0,0.3), rgba(0,0,0,0.3))!important}