我正在进行图像分类任务，并决定使用 Lasagne + Nolearn 来构建神经网络原型。所有标准的例子，如 MNIST 数字分类，都运行得很好，但当我尝试处理自己的图像时就出现了问题。

我想使用三通道图像，而不是灰度图像。以下是我尝试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)

这是神经网络及其拟合的代码：

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)

我收到了如下异常：

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，您是如何将图像数据“输入”到网络中的呢？感谢您的回答或任何建议！

回答：

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