张量流中的基本一维卷积

好的，我想在Tensorflow中对时间序列数据进行一维卷积。tf.nn.conv2d根据这些 票证和手册，显然可以支持此操作。唯一的要求是设置strides=[1,1,1,1]。听起来很简单！

但是，即使在非常小的测试用例中，我也无法弄清楚如何做到这一点。我究竟做错了什么？

让我们进行设置。

import tensorflow as tf
import numpy as np
print(tf.__version__)
>>> 0.9.0

好的，现在在两个小的数组上生成一个基本的卷积测试。我将通过使用1的批处理大小来简化该操作，并且由于时间序列是一维的，所以我的“图像高度”将为1。并且由于它是单变量时间序列，因此显然“通道”的数量也是1，所以这很简单，对吧？

g = tf.Graph()
with g.as_default():
    # data shape is "[batch, in_height, in_width, in_channels]",
    x = tf.Variable(np.array([0.0, 0.0, 0.0, 0.0, 1.0]).reshape(1,1,-1,1), name="x")
    # filter shape is "[filter_height, filter_width, in_channels, out_channels]"
    phi = tf.Variable(np.array([0.0, 0.5, 1.0]).reshape(1,-1,1,1), name="phi")
    conv = tf.nn.conv2d(
        phi,
        x,
        strides=[1, 1, 1, 1],
        padding="SAME",
        name="conv")

繁荣。错误。

ValueError: Dimensions 1 and 5 are not compatible

好的，首先，我不知道在任何维度上该如何发生，因为我已经指定要在卷积OP中填充参数。

但是很好，也许对此有限制。我一定对文档感到困惑，并在张量的错误轴上设置了这种卷积。我将尝试所有可能的排列：

for i in range(4):
    for j in range(4):
        shape1 = [1,1,1,1]
        shape1[i] = -1
        shape2 = [1,1,1,1]
        shape2[j] = -1
        x_array = np.array([0.0, 0.0, 0.0, 0.0, 1.0]).reshape(*shape1)
        phi_array = np.array([0.0, 0.5, 1.0]).reshape(*shape2)
        try:
            g = tf.Graph()
            with g.as_default():
                x = tf.Variable(x_array, name="x")
                phi = tf.Variable(phi_array, name="phi")
                conv = tf.nn.conv2d(
                    x,
                    phi,
                    strides=[1, 1, 1, 1],
                    padding="SAME",
                    name="conv")
                init_op = tf.initialize_all_variables()
            sess = tf.Session(graph=g)
            sess.run(init_op)
            print("SUCCEEDED!", x_array.shape, phi_array.shape, conv.eval(session=sess))
            sess.close()
        except Exception as e:
            print("FAILED!", x_array.shape, phi_array.shape, type(e), e.args or e._message)

结果：

FAILED! (5, 1, 1, 1) (3, 1, 1, 1) <class 'ValueError'> ('Filter must not be larger than the input: Filter: (3, 1) Input: (1, 1)',)
FAILED! (5, 1, 1, 1) (1, 3, 1, 1) <class 'ValueError'> ('Filter must not be larger than the input: Filter: (1, 3) Input: (1, 1)',)
FAILED! (5, 1, 1, 1) (1, 1, 3, 1) <class 'ValueError'> ('Dimensions 1 and 3 are not compatible',)
FAILED! (5, 1, 1, 1) (1, 1, 1, 3) <class 'tensorflow.python.framework.errors.InvalidArgumentError'> No OpKernel was registered to support Op 'Conv2D' with these attrs
     [[Node: conv = Conv2D[T=DT_DOUBLE, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](x/read, phi/read)]]
FAILED! (1, 5, 1, 1) (3, 1, 1, 1) <class 'tensorflow.python.framework.errors.InvalidArgumentError'> No OpKernel was registered to support Op 'Conv2D' with these attrs
     [[Node: conv = Conv2D[T=DT_DOUBLE, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](x/read, phi/read)]]
FAILED! (1, 5, 1, 1) (1, 3, 1, 1) <class 'ValueError'> ('Filter must not be larger than the input: Filter: (1, 3) Input: (5, 1)',)
FAILED! (1, 5, 1, 1) (1, 1, 3, 1) <class 'ValueError'> ('Dimensions 1 and 3 are not compatible',)
FAILED! (1, 5, 1, 1) (1, 1, 1, 3) <class 'tensorflow.python.framework.errors.InvalidArgumentError'> No OpKernel was registered to support Op 'Conv2D' with these attrs
     [[Node: conv = Conv2D[T=DT_DOUBLE, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](x/read, phi/read)]]
FAILED! (1, 1, 5, 1) (3, 1, 1, 1) <class 'ValueError'> ('Filter must not be larger than the input: Filter: (3, 1) Input: (1, 5)',)
FAILED! (1, 1, 5, 1) (1, 3, 1, 1) <class 'tensorflow.python.framework.errors.InvalidArgumentError'> No OpKernel was registered to support Op 'Conv2D' with these attrs
     [[Node: conv = Conv2D[T=DT_DOUBLE, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](x/read, phi/read)]]
FAILED! (1, 1, 5, 1) (1, 1, 3, 1) <class 'ValueError'> ('Dimensions 1 and 3 are not compatible',)
FAILED! (1, 1, 5, 1) (1, 1, 1, 3) <class 'tensorflow.python.framework.errors.InvalidArgumentError'> No OpKernel was registered to support Op 'Conv2D' with these attrs
     [[Node: conv = Conv2D[T=DT_DOUBLE, data_format="NHWC", padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](x/read, phi/read)]]
FAILED! (1, 1, 1, 5) (3, 1, 1, 1) <class 'ValueError'> ('Dimensions 5 and 1 are not compatible',)
FAILED! (1, 1, 1, 5) (1, 3, 1, 1) <class 'ValueError'> ('Dimensions 5 and 1 are not compatible',)
FAILED! (1, 1, 1, 5) (1, 1, 3, 1) <class 'ValueError'> ('Dimensions 5 and 3 are not compatible',)
FAILED! (1, 1, 1, 5) (1, 1, 1, 3) <class 'ValueError'> ('Dimensions 5 and 1 are not compatible',)

唔。好的，看来现在有两个问题。首先，ValueError我猜想是关于沿错误的轴应用滤镜，尽管有两种形式。

但是，随后我可以沿其应用过滤器的轴也令人困惑-请注意，它实际上构造了具有输入形状（5、1、1、1、1）和过滤器形状（1、1、1、1、3）的图形。根据文档中的AFAICT，这应该是一个过滤器，该过滤器查看批次中的示例，一个“像素”和一个“通道”，并输出3个“通道”。那么，当其他人不起作用时，为什么一个人起作用呢？

无论如何，有时在构建图形时它不会失败。有时它会构造图；然后我们得到了tensorflow.python.framework.errors.InvalidArgumentError。从一些令人困惑的github票证中，我收集到这可能是由于我在CPU而不是GPU上运行的事实，反之亦然卷积运算仅针对32位浮点数而不是64位浮点数进行定义的事实。如果任何人都可能会引发一些光其轴我应该对准什么上，为了与卷积内核时间序列，我会非常感激。