是否可以将数组传递到神经网络感知器？

Question

我正在尝试建立一个用于识别Elliott Waves的神经网络，我想知道是否有可能将一组数组传递给感知器？我的计划是将大小为4（[打开，关闭，高，低]）的数组传递到每个感知器中。如果是这样，加权平均计算将如何工作？如何使用Python Keras库进行此处理？谢谢！

Answer 1

这是一个非常标准的全连接神经网络。我认为您有一个分类问题：

from keras.layers import Input, Dense
from keras.models import Model

# I assume that x is the array containing the training data
# the shape of x should be (num_samples, 4)
# The array containing the test data is named y and is 
# one-hot encoded with a shape of (num_samples, num_classes)
# num_samples is the number of samples in your training set
# num_classes is the number of classes you have
# e.g. is a binary classification problem num_classes=2

# First, we'll define the architecture of the network
inp = Input(shape=(4,)) # you have 4 features
hidden = Dense(10, activation='sigmoid')(inp)  # 10 neurons in your hidden layer
out = Dense(num_classes, activation='softmax')(hidden)  

# Create the model
model = Model(inputs=[inp], outputs=[out])

# Compile the model and define the loss function and optimizer
model.compile(loss='categorical_crossentropy', optimizer='adam', 
              metrics=['accuracy'])
# feel free to change these to suit your needs

# Train the model
model.fit(x, y, epochs=10, batch_size=512)
# train the model for 10 epochs with a batch size of 512