DojoGojira - 8 months ago 112

Python Question

I have a Python script that uses TensorFlow to create a multilayer perceptron net (with dropout) in order to do binary classification. Even though I've been careful to set both the Python and TensorFlow seeds, I get non-repeatable results. If I run once and then run again, I get different results. I can even run once, quit Python, restart Python, run again and get different results.

I know some people posted questions about getting non-repeatable results in TensorFlow (e.g., "How to get stable results...", "set_random_seed not working...", "How to get reproducible result in TensorFlow"), and the answers usually turn out to be an incorrect use/understanding of

`tf.set_random_seed()`

A common mistake is not realizing that

`tf.set_random_seed()`

`print [n.name for n in tf.get_default_graph().as_graph_def().node]`

I've also used function calls like

`tf.reset_default_graph()`

`tf.get_default_graph().finalize()`

My script is ~360 lines long so here are the relevant lines (with snipped code indicated). Any items that are in ALL_CAPS are constants that are defined in my

`Parameters`

`import numpy as np`

import tensorflow as tf

from copy import deepcopy

from tqdm import tqdm # Progress bar

# --------------------------------- Parameters ---------------------------------

(snip)

# --------------------------------- Functions ---------------------------------

(snip)

# ------------------------------ Obtain Train Data -----------------------------

(snip)

# ------------------------------ Obtain Test Data -----------------------------

(snip)

random.seed(12345)

tf.set_random_seed(12345)

(snip)

# ------------------------- Build the TensorFlow Graph -------------------------

tf.reset_default_graph()

with tf.Graph().as_default():

x = tf.placeholder("float", shape=[None, N_INPUT])

y_ = tf.placeholder("float", shape=[None, N_CLASSES])

# Store layers weight & bias

weights = {

'h1': tf.Variable(tf.random_normal([N_INPUT, N_HIDDEN_1])),

'h2': tf.Variable(tf.random_normal([N_HIDDEN_1, N_HIDDEN_2])),

'h3': tf.Variable(tf.random_normal([N_HIDDEN_2, N_HIDDEN_3])),

'out': tf.Variable(tf.random_normal([N_HIDDEN_3, N_CLASSES]))

}

biases = {

'b1': tf.Variable(tf.random_normal([N_HIDDEN_1])),

'b2': tf.Variable(tf.random_normal([N_HIDDEN_2])),

'b3': tf.Variable(tf.random_normal([N_HIDDEN_3])),

'out': tf.Variable(tf.random_normal([N_CLASSES]))

}

# Construct model

pred = multilayer_perceptron(x, weights, biases, USE_DROP_LAYERS, DROP_KEEP_PROB)

mean1 = tf.reduce_mean(weights['h1'])

mean2 = tf.reduce_mean(weights['h2'])

mean3 = tf.reduce_mean(weights['h3'])

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y_))

regularizers = (tf.nn.l2_loss(weights['h1']) + tf.nn.l2_loss(biases['b1']) +

tf.nn.l2_loss(weights['h2']) + tf.nn.l2_loss(biases['b2']) +

tf.nn.l2_loss(weights['h3']) + tf.nn.l2_loss(biases['b3']))

cost += COEFF_REGULAR * regularizers

optimizer = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(cost)

out_labels = tf.nn.softmax(pred)

sess = tf.InteractiveSession()

sess.run(tf.initialize_all_variables())

tf.get_default_graph().finalize() # Lock the graph as read-only

#Print the default graph in text form

print [n.name for n in tf.get_default_graph().as_graph_def().node]

# --------------------------------- Training ----------------------------------

print "Start Training"

pbar = tqdm(total = TRAINING_EPOCHS)

for epoch in range(TRAINING_EPOCHS):

avg_cost = 0.0

batch_iter = 0

train_outfile.write(str(epoch))

while batch_iter < BATCH_SIZE:

train_features = []

train_labels = []

batch_segments = random.sample(train_segments, 20)

for segment in batch_segments:

train_features.append(segment[0])

train_labels.append(segment[1])

sess.run(optimizer, feed_dict={x: train_features, y_: train_labels})

line_out = "," + str(batch_iter) + "\n"

train_outfile.write(line_out)

line_out = ",," + str(sess.run(mean1, feed_dict={x: train_features, y_: train_labels}))

line_out += "," + str(sess.run(mean2, feed_dict={x: train_features, y_: train_labels}))

line_out += "," + str(sess.run(mean3, feed_dict={x: train_features, y_: train_labels})) + "\n"

train_outfile.write(line_out)

avg_cost += sess.run(cost, feed_dict={x: train_features, y_: train_labels})/BATCH_SIZE

batch_iter += 1

line_out = ",,,,," + str(avg_cost) + "\n"

train_outfile.write(line_out)

pbar.update(1) # Increment the progress bar by one

train_outfile.close()

print "Completed training"

# ------------------------------ Testing & Output ------------------------------

keep_prob = 1.0 # Do not use dropout when testing

print "now reducing mean"

print(sess.run(mean1, feed_dict={x: test_features, y_: test_labels}))

print "TRUE LABELS"

print(test_labels)

print "PREDICTED LABELS"

pred_labels = sess.run(out_labels, feed_dict={x: test_features})

print(pred_labels)

output_accuracy_results(pred_labels, test_labels)

sess.close()

As you can see, I'm outputting results during each epoch to a file and also printing out accuracy numbers at the end. None of these match from run to run, even though I believe I've set the seed(s) correctly. I've used both

`random.seed(12345)`

`tf.set_random_seed(12345)`

Please let me know if I need to provide more information. And thanks in advance for any help.

-DG

TensorFlow version 0.8.0 (CPU only)

Enthought Canopy version 1.7.2 (Python 2.7, not 3.+)

Mac OS X version 10.11.3

Answer

You need to set operation level seed in addition to graph-level seed, ie

```
tf.reset_default_graph()
a = tf.constant([1, 1, 1, 1, 1], dtype=tf.float32)
graph_level_seed = 1
operation_level_seed = 1
tf.set_random_seed(graph_level_seed)
b = tf.nn.dropout(a, 0.5, seed=operation_level_seed)
```

Source (Stackoverflow)