如何我可以用一个数字序列来预测一个数字在Tensorflow吗?

Question

我正在尝试建立一个机器学习模型，它可以从一系列数字中预测单个数字。我正在使用Tensorflow的keras API中的顺序模型。

你可以想象我的数据集是这样的:

<表类> 指数 x数据 y数据 tbody><<tr>0np.ndarray(shape (1209278,) )numpy.float321np.ndarray(shape (1211140,) )numpy.float322np.ndarray(shape (1418411,) )numpy.float323np.ndarray(shape (1077132,) )numpy.float32………

试试这样:

import numpy as np
import tensorflow as tf
# add additional dimension for lstm layer
x_train = np.asarray(train_set["x data"].values))[..., None] 
y_train = np.asarray(train_set["y data"]).astype(np.float32)
model = tf.keras.Sequential()
model.add(tf.keras.layers.LSTM(units=32))
model.add(tf.keras.layers.Dense(units=1))
model.compile(loss="mean_squared_error", optimizer="adam", metrics="mse")
model.fit(x=x_train,y=y_train,epochs=10)

或使用不同序列长度的不规则输入:

x_train = tf.ragged.constant(train_set["x data"].values[..., None]) # add additional dimension for lstm layer
y_train = np.asarray(train_set["y data"]).astype(np.float32)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=[None, x_train.bounding_shape()[-1]], batch_size=2, dtype=tf.float32, ragged=True))
model.add(tf.keras.layers.LSTM(units=32))
model.add(tf.keras.layers.Dense(units=1))
model.compile(loss="mean_squared_error", optimizer="adam", metrics="mse")
model.fit(x=x_train,y=y_train,epochs=10)

或:

x_train = tf.ragged.constant([np.array(list(v))[..., None] for v in train_set["x data"].values]) # add additional dimension for lstm layer