Question

我试图创建一个基于theano / lasagne的神经网络，它将（基本上）尝试进行多变量回归。

代码的内容是：

    train_value = train_df.values[:, 0]
    train_data = train_df.values[:, 1:]
    #print "train:", train_data.shape, train_label.shape


    #test_data = test_df.values
    #print "test:", test_data.shape


    train_data = train_data.astype(np.float)
    train_value = train_value.astype(np.int32)



    fc_1hidden = NeuralNet(
        layers = [  # three layers: one hidden layer
            ('input', layers.InputLayer),
            ('hidden', layers.DenseLayer),
            ('dropout', layers.DropoutLayer),
            ('output', layers.DenseLayer),
            ],
        # layer parameters:
        input_shape = (None, 36),  # 36 rows of data 
        hidden_num_units = 100,  # number of units in hidden layer
        dropout_p = 0.25, # dropout probability
        output_nonlinearity = softmax,  # output layer uses softmax function
        output_num_units = 10,  # 10 labels

        # optimization method:
        #update = nesterov_momentum,
        update = sgd,
        update_learning_rate = 0.001,
        #update_momentum = 0.9,

        eval_size = 0.1,

        # batch_iterator_train = BatchIterator(batch_size = 20),
        # batch_iterator_test = BatchIterator(batch_size = 20),

        max_epochs = 100,  # we want to train this many epochs
        verbose = 1,
        )

fc_1hidden.fit(train_data, train_value)
plot_loss(fc_1hidden)

这里，train_value只是我希望训练我的NN预测的（数字）数据的一列，以下57列（train_data）是所有应该适当加权以预测的参数/值（所有数字）第一列中的值。

但是，当我运行此脚本时，我收到以下错误：

  Epoch  |  Train loss  |  Valid loss  |  Train / Val  |  Valid acc  |  Dur
--------|--------------|--------------|---------------|-------------|-------
Traceback (most recent call last):
  File "neuralnetwork.py", line 77, in <module>
    fc_1hidden.fit(train_data, train_value)
  File "/Users/spadavec/anaconda/lib/python2.7/site-packages/nolearn/lasagne.py", line 150, in fit
    self.train_loop(X, y)
  File "/Users/spadavec/anaconda/lib/python2.7/site-packages/nolearn/lasagne.py", line 188, in train_loop
    batch_train_loss = self.train_iter_(Xb, yb)
  File "/Users/spadavec/anaconda/lib/python2.7/site-packages/theano/compile/function_module.py", line 606, in __call__
    storage_map=self.fn.storage_map)
  File "/Users/spadavec/anaconda/lib/python2.7/site-packages/theano/compile/function_module.py", line 595, in __call__
    outputs = self.fn()
ValueError: Shape mismatch: x has 83 cols (and 29 rows) but y has 36 rows (and 100 cols)
Apply node that caused the error: Dot22(x_batch, W)
Inputs types: [TensorType(float64, matrix), TensorType(float64, matrix)]
Inputs shapes: [(29, 83), (36, 100)]
Inputs strides: [(664, 8), (800, 8)]
Inputs values: ['not shown', 'not shown']

我不确定这个形状的位置 - 我的数据都没有83列或行。（注意：我已经尝试改编这个剧本，这个剧本最初用于查看面部图片并猜测不同部位（眼睛，鼻子，嘴巴等））。

我在pybrain中编写了一个更简单的版本（sans-dropout方法），但我正在尝试迁移到sklearn / lasagne / theano，因为它打开了更多的门。

Answer 1

由于您要进行回归，请确保正确设置输出类型：

output_nonlinearity = linear

您确定还有10个输出单位吗？我在烤宽面条中经历了一些奇怪的行为。我认为API随着时间的推移而发生了变化，并且包含一些错误。我成功地使用了最新的API demo并根据我的需要进行调整。

＆＃34; ValueError：形状不匹配＆＃34;神话网络的烤宽面条错误

1 个答案: