[英]Tensorflow 2.2, tf.nn.conv1d in Lambda layer
I'd like to perform a convolution in a Lambda layer, but I can't get it to work any way.我想在 Lambda 层中执行卷积,但我无法让它以任何方式工作。
kernel = [1.0,2.0,1.0] # weighted moving average
x = [ # history_size=5, num_features=10
[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0],
[2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0],
[3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0],
[4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0],
[5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0],
]
k = tf.constant(kernel, dtype=tf.float32)
y = tf.nn.conv1d(x, k, stride=1, padding='SAME')
I realize dimensions are not correct in the above example, but that's my data's actual format.我意识到在上面的例子中尺寸不正确,但这是我数据的实际格式。 The training samples have a shape of
(history_size, num_features)
and the kernel has to convolve along history_size, each feature separately.训练样本的形状为
(history_size, num_features)
并且内核必须沿着 history_size 分别对每个特征进行卷积。 Any help would be appreciated.任何帮助,将不胜感激。 I cannot find an example on how to perform tf.nn.conv1d manually.
我找不到有关如何手动执行 tf.nn.conv1d 的示例。
You could use numpy.convolve()
for this.您可以
numpy.convolve()
使用numpy.convolve()
。
import numpy as np
kernel = [1.0,2.0,1.0] # weighted moving average
x = [ # history_size=5, num_features=10
[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0],
[2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0,2.0],
[3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0,3.0],
[4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0,4.0],
[5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0,5.0],
]
output = []
for i in range(len(x)):
output.append(list(np.convolve(x[i], kernel, mode = 'same')))
output
'''
[[3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 3.0],
[6.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 6.0],
[9.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 12.0, 9.0],
[12.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 16.0, 12.0],
[15.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 20.0, 15.0]]
'''
You could try changing the mode
whichever fits best to you according to the documentation .您可以尝试根据文档更改最适合您的
mode
。
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