相关疑难解决方法(0)

我正在制作一个 MLP 模型，它需要两个输入并产生一个输出。

I have two input arrays (one for each input) and 1 output array. The neural network has 1 hidden layer with 2 neurons. Each array has 336 elements.

model0 = keras.Sequential([
keras.layers.Dense(2, input_dim=2, activation=keras.activations.sigmoid, use_bias=True),
keras.layers.Dense(1, activation=keras.activations.relu, use_bias=True),
])

# Compile the neural network #
model0.compile(
    optimizer = keras.optimizers.RMSprop(lr=0.02,rho=0.9,epsilon=None,decay=0),
    loss = 'mean_squared_error',
    metrics=['accuracy']
)

I tried two ways, both of them are giving errors.

model0.fit(numpy.array([array_1, array_2]),output, batch_size=16, epochs=100)

ValueError: Error when checking input: expected dense_input to have …

我有将近5年的时间序列数据。使用此数据，我想预测未来2年。这该怎么做？

我提到了许多与此有关的网站。我注意到，大多数预测仅使用用于训练的相同数据集完成，而对于未来30天则不会进行预测。如果有可能通过TensorFlow实现。我可以知道如何实现吗？

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import tensorflow as tf
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dropout

dataset_train = pd.read_csv(r'C:\Users\Kavin\source\repos\SampleTensorFlow\SampleTensorFlow\data\traindataset.csv')
training_set = dataset_train.iloc[:, 1:2].values

sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)

X_train = []
y_train = []
for i in range(60, 2035):
    X_train.append(training_set_scaled[i-60:i, 0])
    y_train.append(training_set_scaled[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) …