Neupy 神经网络问题

Question

我正在尝试为项目训练/使用带有 neupy 库的卷积神经网络，但我在训练阶段遇到错误。

我有很多图像 (rgb, shape=66, 160, 3)，我将它们分成训练集和测试集。然后我尝试训练一个卷积神经网络（稍后我将尝试使用不同的算法、层数和大小进行优化）。我的项目的目标输出是一个数字 [-1, 1]，我正在解决一个回归问题，但我之前遇到过问题。

我现在遇到的错误是： ValueError：无法打乱矩阵。所有矩阵都应具有相同的行数

相关代码：

print numpy.array(y_train).shape
# outputs (84, 66, 160, 3)
print numpy.array(y_test).shape
# outputs (15, 66, 160, 3)

cgnet = algorithms.Adadelta(
    [
        layers.Input((6, 66, 160*3)),

        layers.Convolution((8, 3, 3)),
        layers.Relu(),
        layers.Convolution((8, 3, 3)),
        layers.Relu(),
        layers.MaxPooling((2, 2)),
        layers.Reshape(),
        layers.Linear(1024),
        layers.Softmax(10),
    ],

    error='categorical_crossentropy',
    step=1.0,
    verbose=True,
    shuffle_data=True,
    #shuffle_data=False,

    reduction_freq=8,
    addons=[algorithms.StepDecay],
)

print cgnet.architecture()
cgnet.train(x_train, y_train, x_test, y_test, epochs=100)

输出：

Main information

[ALGORITHM] Adadelta

[OPTION] batch_size = 128
[OPTION] verbose = True
[OPTION] epoch_end_signal = None
[OPTION] show_epoch = 1
[OPTION] shuffle_data = True
[OPTION] step = 1.0
[OPTION] train_end_signal = None
[OPTION] error = categorical_crossentropy
[OPTION] addons = ['StepDecay']
[OPTION] decay = 0.95
[OPTION] epsilon = 1e-05
[OPTION] reduction_freq = 8

[THEANO] Initializing Theano variables and functions.
[THEANO] Initialization finished successfully. It took 7.01 seconds

Network's architecture

-------------------------------------------------
| # | Input shape  | Layer Type  | Output shape |
-------------------------------------------------
| 1 | (6, 66, 480) | Input       | (6, 66, 480) |
| 2 | (6, 66, 480) | Convolution | (8, 64, 478) |
| 3 | (8, 64, 478) | Relu        | (8, 64, 478) |
| 4 | (8, 64, 478) | Convolution | (8, 62, 476) |
| 5 | (8, 62, 476) | Relu        | (8, 62, 476) |
| 6 | (8, 62, 476) | MaxPooling  | (8, 31, 238) |
| 7 | (8, 31, 238) | Reshape     | 59024        |
| 8 | 59024        | Linear      | 1024         |
| 9 | 1024         | Softmax     | 10           |
-------------------------------------------------

None

Start training

[TRAIN DATA] 84 samples, feature shape: (66, 160, 3)
[TEST DATA] 15 samples, feature shape: (66, 160, 3)
[TRAINING] Total epochs: 100

------------------------------------------------
| Epoch # | Train err | Valid err | Time       |
------------------------------------------------
Traceback (most recent call last):
  File "./ml_neupy.py", line 68, in <module>
    cgnet.train(x_train, y_train, x_test, y_test, epochs=100)
  File "/usr/local/lib/python2.7/dist-packages/neupy/algorithms/constructor.py", line 539, in train
    *args, **kwargs
  File "/usr/local/lib/python2.7/dist-packages/neupy/algorithms/learning.py", line 49, in train
    summary=summary
  File "/usr/local/lib/python2.7/dist-packages/neupy/algorithms/base.py", line 409, in train
    target_train)
  File "/usr/local/lib/python2.7/dist-packages/neupy/algorithms/utils.py", line 146, in shuffle
    raise ValueError("Cannot shuffle matrices. All matrices should "
ValueError: Cannot shuffle matrices. All matrices should have the same number of rows

输入数据或网络有什么问题？

谢谢

Answer 1

有几点需要修改：

您提到您正在尝试解决回归问题。您的网络有一个 Softmax 层作为输出，这意味着您的网络只能为您提供 [0, 1] 范围内的输出，而不是 [-1, 1]。您可以将其更改为 Tanh 层。它将产生 [-1, 1] 范围内的输出。
交叉熵误差只适用于分类问题
```
error='categorical_crossentropy'
```
对于回归，您可以使用 MSE 或 RMSE（您可以找到更多误差函数 here）
```
error='mse'
```
我假设在 (66, 160, 3) 形状中，数字 3 定义了每个 RGB 通道。 NeuPy 与 Theano 库一起使用，这意味着您需要在图像的宽度和高度之前定义通道形状。正确的顺序是：(n_samples, n_channels, height, width)。在您的情况下，我假设您有 84 个样本、66 像素的高度、160 像素的宽度和 3 个通道 (RGB)。如果是这样，那么您需要按如下方式转换输入
```
# convert this shape (n_samples, height, width, n_channels)
# to (n_samples, n_channels, height, width)
x_train = x_train.transpose((0, 3, 1, 2))
print(x_train.shape)  # (84, 3, 66, 160)
```
最后一层的输出应该是 1 而不是 10。这意味着您每个样本只预测一个值，而不是具有 10 个值的向量（使用 layers.Tanh(1) 而不是 layers.Softmax(10))

以下代码没有错误（没有正确训练，因为值是随机的）：

import numpy
from neupy import algorithms, layers


x_train = numpy.random.random((84, 3, 66, 160))
x_test = numpy.random.random((15, 3, 66, 160))
y_train = numpy.random.random(84)
y_test = numpy.random.random(15)

cgnet = algorithms.Adadelta(
    [
        layers.Input((3, 66, 160)),

        layers.Convolution((8, 3, 3)),
        layers.Relu(),
        layers.Convolution((8, 3, 3)),
        layers.Relu(),
        layers.MaxPooling((2, 2)),

        layers.Reshape(),
        layers.Linear(1024),
        layers.Tanh(1),
    ],

    error='mse',
    step=1.0,
    verbose=True,
    shuffle_data=True,

    reduction_freq=8,
    addons=[algorithms.StepDecay],
)

cgnet.architecture()
cgnet.train(x_train, y_train, x_test, y_test, epochs=100)

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