https://www.tensorflow.org/versions/r1.6/api_docs/python/tf/gradients
In the documentation for tf.gradients(ys, xs) it states that
Constructs symbolic derivatives of sum of ys w.r.t. x in xs
I am confused about the summing part, I have read elsewhere that this sums the derivatives dy/dx across the batch for every x in the batch. However, whenever I use this I fail to see this happening. Take the following simple example:
x_dims = 3
batch_size = 4
x = tf.placeholder(tf.float32, (None, x_dims))
y = 2*(x**2)
grads = tf.gradients(y,x)
sess = tf.Session()
x_val = np.random.randint(0, 10, (batch_size, x_dims))
y_val, grads_val = sess.run([y, grads], {x:x_val})
print('x = \n', x_val)
print('y = \n', y_val)
print('dy/dx = \n', grads_val[0])
This gives the following output:
x =
[[5 3 7]
[2 2 5]
[7 5 0]
[3 7 6]]
y =
[[50. 18. 98.]
[ 8. 8. 50.]
[98. 50. 0.]
[18. 98. 72.]]
dy/dx =
[[20. 12. 28.]
[ 8. 8. 20.]
[28. 20. 0.]
[12. 28. 24.]]
This is the output I would expect, simply the derivative dy/dx for every element in the batch. I don't see any summing happening. I have seen in other examples that this operation is followed by dividing by the batch size to account for tf.gradients() summing the gradients over the batch (see here: https://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html). Why is this necessary?
I am using Tensorflow 1.6 and Python 3.
If y and x have the same shape then the sum over the dy/dx is the sum over exactly one value. However, if you have more than one y for each x, then the gradients are summed.
import numpy as np
import tensorflow as tf
x_dims = 3
batch_size = 4
x = tf.placeholder(tf.float32, (None, x_dims))
y = 2*(x**2)
z = tf.stack([y, y]) # There are twice as many z's as x's
dy_dx = tf.gradients(y,x)
dz_dx = tf.gradients(z,x)
sess = tf.Session()
x_val = np.random.randint(0, 10, (batch_size, x_dims))
y_val, z_val, dy_dx_val, dz_dx_val = sess.run([y, z, dy_dx, dz_dx], {x:x_val})
print('x.shape =', x_val.shape)
print('x = \n', x_val)
print('y.shape = ', y_val.shape)
print('y = \n', y_val)
print('z.shape = ', z_val.shape)
print('z = \n', z_val)
print('dy/dx = \n', dy_dx_val[0])
print('dz/dx = \n', dz_dx_val[0])
Produces the following output:
x.shape = (4, 3)
x =
[[1 4 8]
[0 2 8]
[2 8 1]
[4 5 2]]
y.shape = (4, 3)
y =
[[ 2. 32. 128.]
[ 0. 8. 128.]
[ 8. 128. 2.]
[ 32. 50. 8.]]
z.shape = (2, 4, 3)
z =
[[[ 2. 32. 128.]
[ 0. 8. 128.]
[ 8. 128. 2.]
[ 32. 50. 8.]]
[[ 2. 32. 128.]
[ 0. 8. 128.]
[ 8. 128. 2.]
[ 32. 50. 8.]]]
dy/dx =
[[ 4. 16. 32.]
[ 0. 8. 32.]
[ 8. 32. 4.]
[16. 20. 8.]]
dz/dx =
[[ 8. 32. 64.]
[ 0. 16. 64.]
[16. 64. 8.]
[32. 40. 16.]]
In particular, notice that the values of dz/dx are twice those of dy/dz since they are summed over the inputs to the stack.
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