好的，我想在Tensorflow中对时间序列数据进行一维卷积。根据tf.nn.conv2d these和tickets，使用the manual显然支持这一点。唯一的要求是设置strides=[1,1,1,1]。听起来很简单！
但是，即使是非常小的测试用例，我也无法弄清楚如何做到这一点。我究竟做错了什么？
我们设置一下吧。
import tensorflow as tf
import numpy as np
print(tf.__version__)
>>> 0.9.0
好的，现在在两个小阵列上生成基本卷积测试。我将通过使用1的批量大小来轻松实现，并且由于时间序列是1维的，因此我将具有1的“图像高度”。并且因为它是单变量的时间序列，所以“通道”的数量显然也是1，这样会很简单吧？
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，我不明白任何维度都会发生这种情况，因为我已经指定我在卷积OP中填充参数。
但很好，也许有限制。我必须弄乱文档，并在张量的错误轴上设置这个卷积。我会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',)
嗯。好吧，现在看起来有两个问题。首先，ValueError是关于沿错误的轴应用滤波器，我猜，虽然有两种形式。
但是我可以应用过滤器的轴也是混乱的 - 注意它实际上构造了具有输入形状（5,1,1,1）和过滤器形状（1,1,1,3）的图形。文档中的AFAICT，这应该是一个过滤器，它从批处理中查看示例，一个“像素”和一个“通道”并输出3个“通道”。那么，为什么那个人工作呢？
无论如何，有时它在构造图形时不会失败。有时它会构造图形;然后我们得到tensorflow.python.framework.errors.InvalidArgumentError。从我收集的一些confusing github tickets可能是由于
事实上我是在CPU而不是GPU上运行，反之亦然
事实上，卷积Op仅定义为32位浮点数，而不是64位浮点数。如果有人可以对我应该对齐哪些轴进行一些说明，为了将时间序列与内核进行卷积，我将非常感激。
我很遗憾地说，但你的第一个代码几乎是正确的。你刚刚在x中倒置了phi和tf.nn.conv2d：
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, 5, 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, 3, 1, 1), name="phi")
conv = tf.nn.conv2d(
x,
phi,
strides=[1, 1, 1, 1],
padding="SAME",
name="conv")
更新：TensorFlow现在支持自版本r0.11以来的1D卷积，使用tf.nn.conv1d。我以前做了一个指南，在我在这里粘贴的stackoverflow文档（现已灭绝）中使用它们：
考虑一个基本示例，其输入长度为10，维度为16。批量大小是32。因此，我们有一个输入形状为[batch_size, 10, 16]的占位符。
batch_size = 32
x = tf.placeholder(tf.float32, [batch_size, 10, 16])
然后我们创建一个宽度为3的滤波器，我们将16通道作为输入，并输出16通道。
filter = tf.zeros([3, 16, 16]) # these should be real values, not 0
最后我们应用tf.nn.conv1d的步幅和填充： - 步幅：整数s - 填充：这在2D中工作，你可以选择SAME和VALID。 SAME将输出相同的输入长度，而VALID将不会添加零填充。
对于我们的示例，我们采用2的步幅和有效的填充。
output = tf.nn.conv1d(x, filter, stride=2, padding="VALID")
输出形状应为[batch_size, 4, 16]。
使用padding="SAME"，我们的输出形状为[batch_size, 5, 16]。
在新版本的TF（从0.11开始）你有conv1d，所以没有必要使用2d卷积来进行1d卷积。以下是如何使用conv1d的简单示例：
import tensorflow as tf
i = tf.constant([1, 0, 2, 3, 0, 1, 1], dtype=tf.float32, name='i')
k = tf.constant([2, 1, 3], dtype=tf.float32, name='k')
data = tf.reshape(i, [1, int(i.shape[0]), 1], name='data')
kernel = tf.reshape(k, [int(k.shape[0]), 1, 1], name='kernel')
res = tf.squeeze(tf.nn.conv1d(data, kernel, 1, 'VALID'))
with tf.Session() as sess:
print sess.run(res)
要了解conv1d的计算方法，请查看various examples
我想我能够满足我的需求。它的工作原理的评论/细节在代码上：
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
task_name = 'task_MNIST_flat_auto_encoder'
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
X_train, Y_train = mnist.train.images, mnist.train.labels # N x D
X_cv, Y_cv = mnist.validation.images, mnist.validation.labels
X_test, Y_test = mnist.test.images, mnist.test.labels
# data shape is "[batch, in_height, in_width, in_channels]",
# X_train = N x D
N, D = X_train.shape
# think of it as N images with height 1 and width D.
X_train = X_train.reshape(N,1,D,1)
x = tf.placeholder(tf.float32, shape=[None,1,D,1], name='x-input')
#x = tf.Variable( X_train , name='x-input')
# filter shape is "[filter_height, filter_width, in_channels, out_channels]"
filter_size, nb_filters = 10, 12 # filter_size , number of hidden units/units
# think of it as having nb_filters number of filters, each of size filter_size
W = tf.Variable( tf.truncated_normal(shape=[1, filter_size, 1,nb_filters], stddev=0.1) )
stride_convd1 = 2 # controls the stride for 1D convolution
conv = tf.nn.conv2d(input=x, filter=W, strides=[1, 1, stride_convd1, 1], padding="SAME", name="conv")
with tf.Session() as sess:
sess.run( tf.initialize_all_variables() )
sess.run(fetches=conv, feed_dict={x:X_train})
感谢Olivier的帮助（请参阅他的评论中的讨论以获得进一步的澄清）。
手动检查：
X_train_org = np.array([[0,1,2,3]])
N, D = X_train_org.shape
X_train_1d = X_train_org.reshape(N,1,D,1)
#X_train = tf.constant( X_train_org )
# think of it as N images with height 1 and width D.
xx = tf.placeholder(tf.float32, shape=[None,1,D,1], name='xx-input')
#x = tf.Variable( X_train , name='x-input')
# filter shape is "[filter_height, filter_width, in_channels, out_channels]"
filter_size, nb_filters = 2, 2 # filter_size , number of hidden units/units
# think of it as having nb_filters number of filters, each of size filter_size
filter_w = np.array([[1,3],[2,4]]).reshape(1,filter_size,1,nb_filters)
#W = tf.Variable( tf.truncated_normal(shape=[1,filter_size,1,nb_filters], stddev=0.1) )
W = tf.Variable( tf.constant(filter_w, dtype=tf.float32) )
stride_convd1 = 2 # controls the stride for 1D convolution
conv = tf.nn.conv2d(input=xx, filter=W, strides=[1, 1, stride_convd1, 1], padding="SAME", name="conv")
#C = tf.constant( (np.array([[4,3,2,1]]).T).reshape(1,1,1,4) , dtype=tf.float32 ) #
#tf.reshape( conv , [])
#y_tf = tf.matmul(conv, C)
##
x = tf.placeholder(tf.float32, shape=[None,D], name='x-input') # N x 4
W1 = tf.Variable( tf.constant( np.array([[1,2,0,0],[3,4,0,0]]).T, dtype=tf.float32 ) ) # 2 x 4
y1 = tf.matmul(x,W1) # N x 2 = N x 4 x 4 x 2
W2 = tf.Variable( tf.constant( np.array([[0,0,1,2],[0,0,3,4]]).T, dtype=tf.float32 ))
y2 = tf.matmul(x,W2) # N x 2 = N x 4 x 4 x 2
C1 = tf.constant( np.array([[4,3]]).T, dtype=tf.float32 ) # 1 x 2
C2 = tf.constant( np.array([[2,1]]).T, dtype=tf.float32 )
p1 = tf.matmul(y1,C1)
p2 = tf.matmul(y2,C2)
y = p1 + p2
with tf.Session() as sess:
sess.run( tf.initialize_all_variables() )
print 'manual conv'
print sess.run(fetches=y1, feed_dict={x:X_train_org})
print sess.run(fetches=y2, feed_dict={x:X_train_org})
#print sess.run(fetches=y, feed_dict={x:X_train_org})
print 'tf conv'
print sess.run(fetches=conv, feed_dict={xx:X_train_1d})
#print sess.run(fetches=y_tf, feed_dict={xx:X_train_1d})
输出：
manual conv
[[ 2. 4.]]
[[ 8. 18.]]
tf conv
[[[[ 2. 4.]
[ 8. 18.]]]]