问题标签 [python-nolearn]

我正在按照本教程中的示例进行操作。

而不是按如下方式加载数据集：

如何从“本地”文件夹加载数据集？为了在示例中使用数据，是否有特定的数据结构？

谢谢。

我正在尝试使用卷积神经网络对酒店图像数据进行分类..

以下是一些亮点：

1. 图像预处理：

   • 转换为灰度
   • 将所有图像调整为相同的分辨率
   • 标准化图像数据
   • 寻找 pca 组件

2. 卷积神经网络：

   • 输入- 32*32
   • 卷积 - 16 个过滤器，3*3 过滤器大小
   • pooling - 2*2 过滤器大小
   • dropout——以 0.5 的概率下降
   • 全连接 - 256 台
   • dropout——以 0.5 的概率下降
   • 输出 - 8 类

3. 使用的库：

   • 千层面
   • 不学习

但是，我对测试数据的准确度降低了，只有 28% 左右。

如此低的准确性有什么可能的原因吗？有什么改进建议吗？

提前致谢。

I am trying to work with nolearn and use the ConcatLayer to combine multiple inputs. It works great as long as every input has the same type and shape. I have three different types of inputs that will eventually produce a single scalar output value.

• The first input is an image of dimensions (288,1001)

• The second input is a vector of length 87

• The third is a single scalar value

I am using Conv2DLayer(s) on the first input. The second input utilizes Conv1DLayer or DenseLayer (not sure which would be better since I can't get it far enough to see what happens) I'm not even sure how the third input should be set up since it is only a single value I want to feed into the network.

The code blows up at the ConcatLayer with: 'Mismatch: input shapes must be the same except in the concatenation axis'

It would be forever grateful if someone could write out a super simple network structure that can take these types of inputs and output a single scalar value. I have been googling all day and simply cannot figure this one out.

The fit function looks like this if it is helpful to know, as you can see I am inputting a dictionary with an item for each type of input:

我正在研究openface。Openface有未知分类python代码e。

我正在测试lfw-classification-unknown.py's火车部分。它有训练使用

我安装了nolearn version 0.5. DBN classifier有一个函数调用/usr/local/lib/python2.7/dist-packages/gnumpy.py，我有错误

如何修复错误？

整个错误是

编辑：如果不在调试模式下，错误如下。