Skip to content

for tensorflow 1.6+ #18

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

for tensorflow 1.6+ #18

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
27 changes: 15 additions & 12 deletions tensorflow/rnn.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,8 +3,9 @@
import optparse
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import rnn

# from tensorflow.python.ops import rnn
from tensorflow.contrib import rnn
from six.moves import xrange

def get_feed_dict(x_data, y_data=None):
feed_dict = {}
Expand Down Expand Up @@ -40,7 +41,7 @@ def get_feed_dict(x_data, y_data=None):
xinput = np.random.rand(seq_length, batch_size, hidden_size).astype(np.float32)
ytarget = np.random.rand(batch_size, hidden_size).astype(np.float32)

with tf.device('/gpu:0'):
with tf.device('/cpu:0'):

x = [tf.placeholder(tf.float32, [batch_size, hidden_size], name="x") for i in range(seq_length)]
y = tf.placeholder(tf.float32, [batch_size, hidden_size], name="y")
Expand All @@ -54,30 +55,32 @@ def get_feed_dict(x_data, y_data=None):
else:
raise Exception('Unknown network! '+network_type)

print "Compiling..."
print("Compiling...")
start = time.time()
output, _cell_state = rnn.rnn(cell, x, dtype=tf.float32)
# output, _cell_state = rnn.rnn(cell, x, dtype=tf.float32)
output, _cell_state = rnn.static_rnn(cell, x, dtype=tf.float32)
cost = tf.reduce_sum((output[-1] - y) ** 2)

optim = tf.train.GradientDescentOptimizer(0.01)
train_op = optim.minimize(cost)

session = tf.Session()
session.run(tf.initialize_all_variables())
# session.run(tf.initialize_all_variables())
session.run(tf.global_variables_initializer())
session.run(train_op, feed_dict=get_feed_dict(xinput, ytarget))
print "Setup : compile + forward/backward x 1"
print "--- %s seconds" % (time.time() - start)
print("Setup : compile + forward/backward x 1")
print("--- %s seconds" % (time.time() - start))

start = time.time()
for i in xrange(0, n_batch):
session.run(output[-1], feed_dict=get_feed_dict(xinput))
end = time.time()
print "Forward:"
print "--- %i samples in %s seconds (%f samples/s, %.7f s/sample) ---" % (n_samples, end - start, n_samples / (end - start), (end - start) / n_samples)
print("Forward:")
print("--- %i samples in %s seconds (%f samples/s, %.7f s/sample) ---" % (n_samples, end - start, n_samples / (end - start), (end - start) / n_samples))

start = time.time()
for i in xrange(0, n_batch):
session.run(train_op, feed_dict=get_feed_dict(xinput, ytarget))
end = time.time()
print "Forward + Backward:"
print "--- %i samples in %s seconds (%f samples/s, %.7f s/sample) ---" % (n_samples, end - start, n_samples / (end - start), (end - start) / n_samples)
print("Forward + Backward:")
print("--- %i samples in %s seconds (%f samples/s, %.7f s/sample) ---" % (n_samples, end - start, n_samples / (end - start), (end - start) / n_samples))