Created
April 18, 2018 14:35
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Demo on using control dependencies version 2.
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import tensorflow as tf | |
def main1(): | |
sess = tf.Session() | |
x = tf.Variable(1.) | |
sess.run(tf.initialize_variables([x])) | |
y = x + 3 | |
assign_op = tf.assign(x, x+1) | |
w = tf.identity(x) | |
u = x + 0 | |
with tf.control_dependencies([assign_op]): | |
z = tf.identity(x) | |
q = x + 0 | |
print(sess.run([y,z,q,w, u])) | |
print(sess.run([y,z,q,w, u])) | |
print(sess.run([y,z,q,w, u])) | |
print(sess.run([y,z,q,w, u])) | |
def main2(): | |
sess = tf.Session() | |
x = tf.Variable(1.) | |
sess.run(tf.initialize_variables([x])) | |
y = x + 3 | |
with tf.control_dependencies([y]): | |
assign_op = tf.assign(x, x+1) | |
w = tf.identity(x) | |
u = x + 0 | |
with tf.control_dependencies([assign_op]): | |
z = tf.identity(x) | |
q = x + 0 | |
print(sess.run([y,z,q,w, u])) | |
print(sess.run([y,z,q,w, u])) | |
print(sess.run([y,z,q,w, u])) | |
print(sess.run([y,z,q,w, u])) | |
if __name__ == '__main__': | |
main1() | |
tf.reset_default_graph() | |
print('=') | |
main2() | |
# TF 1.4 | |
# prints (although main1 appears to be stochastic!) | |
# [4.0, 2.0, 2.0, 2.0, 1.0] | |
# [6.0, 3.0, 3.0, 3.0, 3.0] | |
# [6.0, 4.0, 4.0, 4.0, 3.0] | |
# [8.0, 5.0, 5.0, 5.0, 5.0] | |
# = | |
# [4.0, 2.0, 2.0, 2.0, 1.0] | |
# [5.0, 3.0, 3.0, 3.0, 2.0] | |
# [6.0, 4.0, 4.0, 4.0, 3.0] | |
# [7.0, 5.0, 5.0, 5.0, 4.0] |
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