神经网络为两个类返回相同的类标签

Question

我正在使用来自this站点的神经网络对我的臂带手势进行动态分类（神经网络是在我自己的数据集上训练的）。现在，由于这个问题，我删除了所有使用Myo的东西：这个程序为我用于测试的数据集中的每一行返回相同的类标签。怎么能修好呢？另外，有没有一种方法可以实际进行动态分类（Myo发送一个8个数字的向量）（我有疑问，因为minmax要求数据集是二维的）？你知道吗

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代码如下：

from math import exp


# Calculate neuron activation for an input
def activate(weights, inputs):
    activation = weights[-1]
    for i in range(len(weights)-1):
        activation += weights[i] * inputs[i]
    return activation


# Transfer neuron activation
def transfer(activation):
    return 1.0 / (1.0 + exp(-activation))


# Forward propagate input to a network output
def forward_propagate(network, row):
    inputs = row
    for layer in network:
        new_inputs = []
        for neuron in layer:
            activation = activate(neuron['weights'], inputs)
            neuron['output'] = transfer(activation)
            new_inputs.append(neuron['output'])
        inputs = new_inputs
    return inputs


# Make a prediction with a network
def predict(network, row):
    outputs = forward_propagate(network, row)
    return outputs.index(max(outputs))


def dataset_minmax(dataset):
    stats = [[min(column), max(column)] for column in zip(*dataset)]
    return stats


# Rescale dataset columns to the range 0-1
def normalize_dataset(dataset, minmax):
    for row in dataset:
        for i in range(len(row) - 1):
            row[i] = (row[i] - minmax[i][0]) / (minmax[i][1] - minmax[i][0])


def make_predictions():
    dataset = [[29,46,107,324,56,44,121,35,1],
            [29,46,109,327,51,37,123,38,1],
            [28,42,107,309,55,32,124,38,1],
            [40,112,287,59,35,121,36,1],
            [27,43,129,306,75,41,107,38,1],
            [28,38,127,289,79,40,109,37,1],
            [29,37,126,292,77,35,100,34,1],
            [30,40,87,48,77,51,272,80,2],
            [26,37,88,47,84,44,250,80,2],
            [29,39,91,47,84,46,247,79,2],
            [28,38,85,45,80,47,249,78,2],
            [28,36,81,43,76,50,337,83,2],
            [28,34,75,41,83,52,344,81,2],
            [30,38,80,46,71,53,347,92,2],
            [28,35,72,45,64,47,360,101,2]]
    network = [[{'weights': [0.09640510259345969, 0.37923370996257266, 0.5476265202749506, 0.9144446394025773, 0.837692750149296, 0.5343300438262426, 0.7679511829130964, 0.5325204151469501, 0.06532276962299033]}],
            [{'weights': [0.040400453542770665, 0.13301701225112483]}, {'weights': [0.1665525504275246, 0.5382087395561351]}, {'weights': [0.26800994395551214, 0.3322334781304659]}]]
    minmax = dataset_minmax(dataset)
    normalize_dataset(dataset, minmax)
    for row in dataset:
        prediction = predict(network, row)
        print('Expected=%d, Got=%d' % (row[-1], prediction))


if __name__ == '__main__':
    make_predictions()

Answer 1

解决了这个问题，实际上是使用了错误的训练模型。你知道吗