帮我写一个php版的卷积神经网络

好的，以下是一个简单的 PHP 版本的卷积神经网络的实现代码：

class ConvolutionalNeuralNetwork
{
    private $filters;
    private $bias;
    private $input_shape;
    private $output_shape;
    private $stride;
    private $padding;
    private $activation;

    function __construct($filters, $bias, $input_shape, $output_shape, $stride, $padding, $activation)
    {
        $this->filters = $filters;
        $this->bias = $bias;
        $this->input_shape = $input_shape;
        $this->output_shape = $output_shape;
        $this->stride = $stride;
        $this->padding = $padding;
        $this->activation = $activation;
    }

    function forward($input)
    {
        $output = array();
        $input_rows = $this->input_shape[0];
        $input_cols = $this->input_shape[1];
        $filter_size = count($this->filters[0]);
        $output_rows = $this->output_shape[0];
        $output_cols = $this->output_shape[1];

        for ($i = 0; $i < $output_rows; $i++) {
            for ($j = 0; $j < $output_cols; $j++) {
                $sum = 0;
                for ($k = 0; $k < $filter_size; $k++) {
                    for ($l = 0; $l < $filter_size; $l++) {
                        $row_index = $i * $this->stride + $k - $this->padding;
                        $col_index = $j * $this->stride + $l - $this->padding;
                        if ($row_index >= 0 && $row_index < $input_rows && $col_index >= 0 && $col_index < $input_cols) {
                            $sum += $input[$row_index][$col_index] * $this->filters[$k][$l];
                        }
                    }
                }
                $sum += $this->bias;
                $output[$i][$j] = $this->activation($sum);
            }
        }
        return $output;
    }
}

这段代码实现了一个卷积神经网络的前向传播，包括了卷积核、偏置、输入形状、输出形状、步长、填充和激活函数等参数。通过调用 `forward` 方法，可以将输入数据传递给网络，并得到输出结果。