Created
March 3, 2016 07:14
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| import tensorflow as tf | |
| tf.reset_default_graph() | |
| isTrain = tf.placeholder(tf.bool) | |
| user_input = tf.placeholder(tf.float32) | |
| # ema = tf.train.ExponentialMovingAverage(decay=.5) | |
| with tf.device('/cpu:0'): | |
| beta = tf.Variable(tf.ones([1])) | |
| batch_mean = tf.Print(beta.assign(user_input), ['beta assign']) | |
| ema = tf.train.ExponentialMovingAverage(decay=0.5) | |
| ema_apply_op = ema.apply([batch_mean]) | |
| # ema_apply_op = tf.Print(ema.apply([batch_mean]), ["ema_apply_op"]) | |
| ema_mean = tf.Print(ema.average(batch_mean), ['ema_mean']) | |
| def mean_var_with_update(): | |
| with tf.control_dependencies([ema_apply_op]): | |
| return tf.Print(tf.identity(batch_mean), ["mean_var_with_update"]) | |
| # return tf.identity(batch_mean) | |
| mean = tf.Print(tf.cond(isTrain, | |
| mean_var_with_update, | |
| lambda: (tf.Print(ema_mean, ["ema_mean(cond)"]))), | |
| ["evaluating mean", isTrain]) | |
| # ======= End Here ========== | |
| saver = tf.train.Saver() | |
| init = tf.initialize_all_variables() | |
| sess = tf.Session() | |
| sess.run(init) | |
| u_input = [[2], [3], [4] ] | |
| for u in u_input: | |
| aa = sess.run([mean], feed_dict={user_input:u, isTrain: True }) | |
| print("Train", aa) | |
| # for u in u_input: | |
| # aa = sess.run([ema_mean], feed_dict={user_input:u, isTrain: False }) | |
| # print("Test correct", aa) | |
| print("Testing") | |
| for u in u_input: | |
| aa = sess.run([mean], feed_dict={user_input:u, isTrain: False }) | |
| print("Test", aa) |
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