Question

我对LSTM输入大小的工作原理感到困惑。

我有一个场景，我试图根据重量和身高来预测一个人的体重，两者都是时间序列。

我似乎无法弄清楚我在尺寸方面出了什么问题：

from datetime import datetime
import numpy as np
import pandas as pd
import math
from keras.layers import Dense, Activation, LSTM, Input, concatenate
from keras.models import Model

def create_dataset(dataset, window_length=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-window_length-1):
        # print("dataX from {} to {}".format(i, i+window_length))
        # print("dataY from {}".format(i+window_length))
        dataX.append(dataset[i:(i+window_length)])
        dataY.append(dataset[(i + window_length):])


    return np.array(dataX), np.array(dataY)

def buildModel(dataLength, labelLength):

    weight = Input(shape=(dataLength, 1), name="weight")
    height = Input(shape=(dataLength, 1), name="height")

    weightLayers = LSTM(64, return_sequences=False)(weight)
    heightLayers = LSTM(64, return_sequences=False)(height)

    output = concatenate([ weightLayers, heightLayers ])

    output = Dense(labelLength, activation="linear", name="weightedAverage_output")(output)

    model = Model(
        inputs=[weight, height],
        outputs=[output]
        )

    model.compile(optimizer="rmsprop", loss="mse")

    return model

bogus = {
    "weight": range(100,200),
    "height": range(150,250)
}

dataset = pd.DataFrame(bogus)



train_size      = int(len(dataset) * 0.90)
test_size       = len(dataset) - train_size
train, test     = dataset[:train_size], dataset[-test_size:]

# print("*" * 30)
# print(train.head())
# print(train.tail())
# print("==> {}".format(len(train)))
# print("*" * 30)
# print(test.head())
# print(test.tail())
# print("==> {}".format(len(test)))
# input(">")

height_train = np.array(train["height"].values.tolist()).reshape((-1, 1)).astype('float32')
weight_train = np.array(train["weight"].values.tolist()).reshape((-1, 1)).astype('float32')

height_test = np.array(test["height"].values.tolist()).reshape((-1, 1)).astype('float32')
weight_test = np.array(test["weight"].values.tolist()).reshape((-1, 1)).astype('float32')

x_train_height, y_train_height  = create_dataset(height_train, 60)
x_train_weight, y_train_weight  = create_dataset(weight_train, 60)

x_test_height, y_test_height  = create_dataset(height_test, 60)
x_test_weight, y_test_weight  = create_dataset(weight_test, 60)



model = buildModel(60,4)
model.fit(
    [ 
        x_train_weight,
        x_train_height,
    ],

    [ 
        y_train_weight
    ],

    validation_data=(
        [
            x_test_weight,
            x_test_height,
        ],
        [
            y_test_weight
        ],
    ),

    epochs=1,
    batch_size=3000,
    callbacks=[
        # board.createTensorboardConfig("log/graph"),
    ]
)

我收到此错误：

ValueError：检查目标时出错：预期的weightedAverage_output具有形状（无，4）但是具有形状的数组（29,1）

很确定我在输入和输出维度的某个地方出错了。

有什么想法吗？

Answer 1

首先，您需要更改最终输出图层的尺寸：

output = Dense(1, activation="linear", name="weightedAverage_output")(output)

其次，您需要更改输入尺寸以包含时间步长：

timesteps = 1
weight = Input(shape=(dataLength,timesteps,1), name="weight")
height = Input(shape=(dataLength,timesteps,1), name="height")

时间步长可以介于（0，inf）之间。

您可以参考此帖子以获取有关时间步长的更多信息：https://machinelearningmastery.com/use-timesteps-lstm-networks-time-series-forecasting/

什么是RNN LSTM输入形状应该是什么？

1 个答案: