Theano/Lasagne/Nolearn神经网络图像输入

Question

我正在从事图像分类任务，并决定使用Lasagne + Nolearn进行神经网络原型设计。 所有标准示例，如MNIST数字分类都运行良好，但是当我尝试使用自己的图片时出现了问题。

我想使用三通道图像，而不是灰度图像。 以下是我试图从图像中获取数组的代码：

 img = Image.open(item)
 img = ImageOps.fit(img, (256, 256), Image.ANTIALIAS)
 img = np.asarray(img, dtype = 'float64') / 255.
 img = img.transpose(2,0,1).reshape(3, 256, 256)   
 X.append(img)

以下是神经网络（NN）及其拟合的代码：

X, y = simple_load("new")

X = np.array(X)
y = np.array(y)


net1 = NeuralNet(
    layers=[  # three layers: one hidden layer
        ('input', layers.InputLayer),
        ('hidden', layers.DenseLayer),
        ('output', layers.DenseLayer),
        ],
    # layer parameters:
    input_shape=(None, 65536),  # 96x96 input pixels per batch
    hidden_num_units=100,  # number of units in hidden layer
    output_nonlinearity=None,  # output layer uses identity function
    output_num_units=len(y),  # 30 target values

    # optimization method:
    update=nesterov_momentum,
    update_learning_rate=0.01,
    update_momentum=0.9,

    regression=True,  # flag to indicate we're dealing with regression problem


       max_epochs=400,  # we want to train this many epochs
        verbose=1,
        )

  net1.fit(X, y)

我收到了像这样的异常:

Traceback (most recent call last):
  File "las_mnist.py", line 39, in <module>
    net1.fit(X[i], y[i])
  File "/usr/local/lib/python2.7/dist-packages/nolearn/lasagne.py", line 266, in fit
    self.train_loop(X, y)
  File "/usr/local/lib/python2.7/dist-packages/nolearn/lasagne.py", line 273, in train_loop
    X, y, self.eval_size)
  File "/usr/local/lib/python2.7/dist-packages/nolearn/lasagne.py", line 377, in train_test_split
    kf = KFold(y.shape[0], round(1. / eval_size))
IndexError: tuple index out of range

那么，您使用哪种格式向网络提供图像数据？ 感谢回答或任何提示！

- Rachnog

Answer 1

如果你正在进行分类，你需要修改几个东西：

1. In your code you have set regression = True. To do classification remove this line.
2. Ensure that your input shape matches the shape of X if want to input 3 distinct channels

3. Because you are doing classification you need the output to use a softmax nonlinearity (at the moment you have the identity which will not help you with classification)

   X, y = simple_load("new")
   
   X = np.array(X)
   y = np.array(y)
   
   net1 = NeuralNet(
       layers=[  # three layers: one hidden layer
           ('input', layers.InputLayer),
           ('hidden', layers.DenseLayer),
           ('output', layers.DenseLayer),
           ],
       # layer parameters:
       input_shape=(None, 3, 256, 256),  # TODO: change this
       hidden_num_units=100,  # number of units in hidden layer
       output_nonlinearity=lasagne.nonlinearities.softmax, # TODO: change this
       output_num_units=len(y),  # 30 target values
   
       # optimization method:
       update=nesterov_momentum,
       update_learning_rate=0.01,
       update_momentum=0.9,
   
       max_epochs=400,  # we want to train this many epochs
       verbose=1,
   

   )

- unicorn_poet

Answer 2

我也在lasagne-users论坛上提出了这个问题，Oliver Duerr通过代码示例帮助了我很多： https://groups.google.com/forum/#!topic/lasagne-users/8ZA7hr2wKfM

- Rachnog