Question

我试图通过将列数增加到21来扩展the tflearn example for linear regression。

from trafficdata import X,Y

import tflearn

print(X.shape) #(1054, 21)
print(Y.shape) #(1054,)

# Linear Regression graph
input_ = tflearn.input_data(shape=[None,21])
linear = tflearn.single_unit(input_)
regression = tflearn.regression(linear, optimizer='sgd', loss='mean_square',
                                metric='R2', learning_rate=0.01)
m = tflearn.DNN(regression)
m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)

print("\nRegression result:")
print("Y = " + str(m.get_weights(linear.W)) +
      "*X + " + str(m.get_weights(linear.b)))

然而，tflearn抱怨：

Traceback (most recent call last):
  File "linearregression.py", line 16, in <module>
    m.fit(X, Y, n_epoch=1000, show_metric=True, snapshot_epoch=False)
  File "/usr/local/lib/python3.5/dist-packages/tflearn/models/dnn.py", line 216, in fit
    callbacks=callbacks)
  File "/usr/local/lib/python3.5/dist-packages/tflearn/helpers/trainer.py", line 339, in fit
    show_metric)
  File "/usr/local/lib/python3.5/dist-packages/tflearn/helpers/trainer.py", line 818, in _train
    feed_batch)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 789, in run
    run_metadata_ptr)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 975, in _run
    % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
ValueError: Cannot feed value of shape (64,) for Tensor 'TargetsData/Y:0', which has shape '(21,)'

我发现形状（64，）来自tflearn.regression（）的默认批量大小。

我需要转换标签（Y）吗？以什么方式？

谢谢！

Answer 1

我也尝试过这样做。我进行了这些更改以使其正常工作

# linear = tflearn.single_unit(input_)
linear = tflearn.fully_connected(input_, 1, activation='linear')

我的猜测是，使用功能＆gt; 1，您无法使用tflearn.single_unit()。你可以添加额外的fully_connected图层，但最后一个图层必须只有1个神经元，因为Y.shape =（？，1）

Answer 2

你有21个功能。因此，您无法使用linear = tflearn.single_unit(input_)

请尝试这样做：linear = tflearn.fully_connected(input_, 21, activation='linear')

您得到的错误是因为您的标签，即Y的形状为（1054，）。你必须先对它进行预处理。

尝试在# linear regression graph之前使用下面给出的代码：

Y = np.expand_dims(Y,-1)

在regression = tflearn.regression(linear, optimizer='sgd', loss='mean_square',metric='R2', learning_rate=0.01)之前，输入以下代码：

linear = tflearn.fully_connected(linear, 1, activation='linear')

使用TFlearn为线性回归整形数据

2 个答案: