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I am learning tensorflow, I picked up the following code from the tensorflow website. According to my understanding, axis=0 is for rows and axis=1 is for columns.
How are they getting output mentioned in comments? I have mentioned output according to my thinking against ##.
import tensorflow as tf x = tf.constant([[1, 1, 1], [1, 1, 1]]) tf.reduce_sum(x, 0) # [2, 2, 2] ## [3, 3] tf.reduce_sum(x, 1) # [3, 3] ##[2, 2, 2] tf.reduce_sum(x, [0, 1]) # 6 ## Didn't understand at all. 
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The tf. sum() function is used to calculate sum of the elements of a specified Tensor across its dimension. It reduces the given input elements along the dimensions of axes. If the parameter “keepDims” is true, the reduced dimensions are retained with length 1 else the rank of Tensor is reduced by 1.
transpose(x, perm=[1, 0]) . As above, simply calling tf. transpose will default to perm=[2,1,0] . To take the transpose of the matrices in dimension-0 (such as when you are transposing matrices where 0 is the batch dimension), you would set perm=[0,2,1] .
expand_dims() is used to insert an addition dimension in input Tensor. Parameters: input: It is the input Tensor. axis: It defines the index at which dimension should be inserted.
tf.stack( values, axis=0, name='stack' ) Defined in tensorflow/python/ops/array_ops.py. Stacks a list of rank- R tensors into one rank- (R+1) Packs the list of tensors in values into a tensor with rank one higher than each tensor in values , by packing them along the dimension.
x has a shape of (2, 3) (two rows and three columns):
1 1 1 1 1 1 By doing tf.reduce_sum(x, 0) the tensor is reduced along the first dimension (rows), so the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2].
By doing tf.reduce_sum(x, 1) the tensor is reduced along the second dimension (columns), so the result is [1, 1] + [1, 1] + [1, 1] = [3, 3].
By doing tf.reduce_sum(x, [0, 1]) the tensor is reduced along BOTH dimensions (rows and columns), so the result is 1 + 1 + 1 + 1 + 1 + 1 = 6 or, equivalently, [1, 1, 1] + [1, 1, 1] = [2, 2, 2], and then 2 + 2 + 2 = 6 (reduce along rows, then reduce the resulted array).
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In order to understand better what is going on I will change the values, and the results are self explanatory
import tensorflow as tf x = tf.constant([[1, 2, 4], [8, 16, 32]]) a = tf.reduce_sum(x, 0) # [ 9 18 36] b = tf.reduce_sum(x, 1) # [ 7 56] c = tf.reduce_sum(x, [0, 1]) # 63 with tf.Session() as sess: output_a = sess.run(a) print(output_a) output_b = sess.run(b) print(output_b) output_c = sess.run(c) print(output_c) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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