Question

我试图训练/使用带有neupy库的卷积神经网络进行项目，但我在训练阶段遇到错误。

我有很多图像（rgb，shape = 66,160,3），我将它们分成训练和测试集。然后，我试图训练一个卷积神经网络（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数字[-1,1]，我解决了回归问题，但我之前遇到过问题。

我现在得到的错误是： ValueError：无法随机播放矩阵。所有矩阵应具有相同的行数

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？

由于

Answer 1

您需要修改一些内容：

您已经提到过您正试图解决回归问题。您的网络将Softmax图层作为输出，这意味着您的网络只能为您提供[0,1]范围的输出，而不是[-1,1]。您可以将其更改为Tanh图层。它将产生[-1,1]范围的输出。
仅适用于分类问题的交叉熵误差
```
error='categorical_crossentropy'
```
对于回归，您可以使用MSE或RMSE（您可以找到更多错误函数here）
```
error='mse'
```
我假设在（66,160,3）中，形状编号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，那么您需要转换输入如下
```
# 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)
```
最终图层的输出应为1而不是10.这意味着每个样本只预测一个值，而不是预测10个值的向量（使用layers.Tanh(1)而不是layers.Softmax(10) ）

以下代码可以正常运行（无法正确训练，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)

Neupy神经网络问题

1 个答案: