Demonedge - 1 month ago 4
Python Question

# How to reshape a vector to TensorFlow's filters?

I want to transfer some weights trained by another network to TensorFlow, the weights are stored in a single vector like this:

`[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]`

By using numpy, I can reshape it to two 3 by 3 filters like this:

``````1 2 3     9  10 11
3 4 5     12 13 14
6 7 8     15 16 17
``````

Thus, the shape of my filters are
`(1,2,3,3)`
. However, in TensorFlow, the shape of filters are
`(3,3,2,1)`
:

``````tf_weights = tf.Variable(tf.random_normal([3,3,2,1]))
``````

After reshaping the tf_weights to the expected shape, the weight becomes a mess and I can't get the expected convolution result.

To be specific, when the shape of an image or filter is [number,channel,size,size], I wrote a convolution function and it gives the correct answer,but it's too slow:

``````def convol(images,weights,biases,stride):
"""
Args:
images:input images or features, 4-D tensor
weights:weights, 4-D tensor
biases:biases, 1-D tensor
stride:stride, a float number
Returns:
conv_feature: convolved feature map
"""
image_num = images.shape[0] #the number of input images or feature maps
channel = images.shape[1] #channels of an image,images's shape should be like [n,c,h,w]
weight_num = weights.shape[0] #number of weights, weights' shape should be like [n,c,size,size]
ksize = weights.shape[2]
h = images.shape[2]
w = images.shape[3]
out_h = (h+np.floor(ksize/2)*2-ksize)/2+1
out_w = out_h

conv_features = np.zeros([image_num,weight_num,out_h,out_w])
for i in range(image_num):
image = images[i,...,...,...]
for j in range(weight_num):
sum_convol_feature = np.zeros([out_h,out_w])
for c in range(channel):
#extract a single channel image
channel_image = image[c,...,...]
#transform this image to a vector

weight = weights[j,c,...,...]
weight_col = np.reshape(weight,[-1])
mul = np.dot(im_col,weight_col)
convol_feature = np.reshape(mul,[out_h,out_w])
sum_convol_feature = sum_convol_feature + convol_feature
conv_features[i,j,...,...] = sum_convol_feature + biases[j]
return conv_features
``````

Instead, by using tensorflow's conv2d like this:

``````img = np.zeros([1,3,224,224])
img = img - 1
img = np.rollaxis(img, 1, 4)

weight_array = np.reshape(weight_array,[64,3,7,7])

tf_weight = tf.Variable(weight_array)

tf_img = tf.Variable(img)
tf_img = tf.cast(tf_img,tf.float32)

tf_biases = tf.Variable(biases_array)

sess = tf.Session()
sess.run(tf.initialize_all_variables())
feautre = sess.run(conv_feature)
``````

The feature map I got is wrong.

Don't use `np.reshape`. It might mess up the order of your values.

Use `np.rollaxis` instead:

``````>>> a = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18])
>>> a = a.reshape((1,2,3,3))
>>> a
array([[[[ 1,  2,  3],
[ 4,  5,  6],
[ 7,  8,  9]],

[[10, 11, 12],
[13, 14, 15],
[16, 17, 18]]]])
>>> b = np.rollaxis(a, 1, 4)
>>> b.shape
(1, 3, 3, 2)
>>> b = np.rollaxis(b, 0, 4)
>>> b.shape
(3, 3, 2, 1)
``````

Note that the order of the two axes with size 3 haven't changed. If I were to label them, the two `rollaxis` operations caused the shapes to change as (1, 2, 31, 32) -> (1, 31, 32, 2) -> (31, 32, 2, 1). Your final array looks like:

``````>>> b
array([[[[ 1],
[10]],

[[ 2],
[11]],

[[ 3],
[12]]],

[[[ 4],
[13]],

[[ 5],
[14]],

[[ 6],
[15]]],

[[[ 7],
[16]],

[[ 8],
[17]],

[[ 9],
[18]]]])
``````