What does tf.nn.conv2d do in tensorflow? Ask Question

I was looking at the docs of tensorflow about tf.nn.conv2d here. But I can't understand what it does or what it is trying to achieve. It says on the docs,

#1 : Flattens the filter to a 2-D matrix with shape

[filter_height * filter_width * in_channels, output_channels].

Now what does that do? Is that element-wise multiplication or just plain matrix multiplication? I also could not understand the other two points mentioned in the docs. I have written them below :

# 2: Extracts image patches from the the input tensor to form a virtual tensor of shape

[batch, out_height, out_width, filter_height * filter_width * in_channels].

# 3: For each patch, right-multiplies the filter matrix and the image patch vector.

It would be really helpful if anyone could give an example, a piece of code (extremely helpful) maybe and explain what is going on there and why the operation is like this.

I've tried coding a small portion and printing out the shape of the operation. Still, I can't understand.

I tried something like this:

op = tf.shape(tf.nn.conv2d(tf.random_normal([1,10,10,10]), 
              tf.random_normal([2,10,10,10]), 
              strides=[1, 2, 2, 1], padding='SAME'))

with tf.Session() as sess:
    result = sess.run(op)
    print(result)

input = tf.Variable(tf.random_normal([1,2,2,1]))
filter = tf.Variable(tf.random_normal([1,1,1,1]))

op = tf.nn.conv2d(input, filter, strides=[1, 1, 1, 1], padding='SAME')
init = tf.initialize_all_variables()
with tf.Session() as sess:
    sess.run(init)

    print("input")
    print(input.eval())
    print("filter")
    print(filter.eval())
    print("result")
    result = sess.run(op)
    print(result)

input
[[[[ 1.60314465]
   [-0.55022103]]

  [[ 0.00595062]
   [-0.69889867]]]]
filter
[[[[-0.59594476]]]]
result
[[[[-0.95538563]
   [ 0.32790133]]

  [[-0.00354624]
   [ 0.41650501]]]]