I've been working on a large neural network code and a couple weeks ago
this weird error started occurring. (I am running Spyder (Python 3.6) on
Windows 10. )
If I make any change to the code, I have to (save, then) close Spyder and
reopen it for the change to take effect, otherwise Spyder will just run the
code as written when I opened Spyder.
Worth noting is that this doesn't always happen, I can new code without
having to close and reopen Spyder, this error seems restricted to the
neural network code. (Attached is main .py file, although it probably won't
function without the 5 other .py files and all of the imagenet images it
uses to properly run the neural network).
This error has wasted approximately 23.7% of my life, please send help.
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"""
Written by Matteo Dunnhofer - 2017
models training on ImageNet
"""
import tensorflow as tf
from Myoptimizer import Myoptimizer
import sys
import os.path
import time
from models import johnnet
import train_util as tu
import numpy as np
import threading
def train(
epochs,
batch_size,
learning_rate,
dropout,
momentum,
lmbda,
resume,
imagenet_path,
display_step,
test_step,
ckpt_path,
summary_path):
train_img_path = os.path.join(imagenet_path, 'train\\')
ts_size = tu.imagenet_size(train_img_path)
num_batches = int(float(ts_size) / batch_size)
with open (os.path.join(imagenet_path, 'wnids.txt')) as f:
text = f.read()
without_n = text.split('\n')
wnid_labels = [number for number in without_n if number != '']
x = tf.placeholder(tf.float32, [None, 224, 224, 3])
y = tf.placeholder(tf.float32, [None, 1000])
lr = tf.placeholder(tf.float32)
keep_prob = tf.placeholder(tf.float32)
# queue of examples being filled on the cpu
with tf.device('/cpu:0'):
q = tf.FIFOQueue(batch_size * 3, [tf.float32, tf.float32], shapes=[[224, 224, 3], [1000]])
enqueue_op = q.enqueue_many([x, y])
x_b, y_b = q.dequeue_many(batch_size)
pred, _ = johnnet.classifier(x_b, keep_prob)
# cross-entropy and weight decay
with tf.name_scope('cross_entropy'):
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y_b, name='cross-entropy'))
with tf.name_scope('l2_loss'):
l2_loss = tf.reduce_sum(lmbda * tf.stack([tf.nn.l2_loss(v) for v in tf.get_collection('weights')]))
tf.summary.scalar('l2_loss', l2_loss)
with tf.name_scope('loss'):
loss = cross_entropy + l2_loss
tf.summary.scalar('loss', loss)
# accuracy
with tf.name_scope('accuracy'):
correct = tf.equal(tf.argmax(pred, 1), tf.argmax(y_b, 1))
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
tf.summary.scalar('accuracy', accuracy)
global_step = tf.Variable(0, trainable=False)
epoch = tf.div(global_step, num_batches)
# momentum optimizer
with tf.name_scope('optimizer'):
optimizer = tf.train.MomentumOptimizer(learning_rate=lr, momentum=momentum).minimize(loss, global_step=global_step)
#optimizer = Myoptimizer(learning_rate=lr, momentum=momentum).minimize(loss, global_step=global_step)
# merge summaries to write them to file
merged = tf.summary.merge_all()
# checkpoint saver
saver = tf.train.Saver()
coord = tf.train.Coordinator()
#init = tf.initialize_all_variables()
init = tf.global_variables_initializer()
with tf.Session(config=tf.ConfigProto()) as sess:
if resume:
saver.restore(sess, os.path.join(ckpt_path, 'johnnet-cnn.ckpt'))
else:
sess.run(init)
# enqueuing batches procedure
def enqueue_batches():
while not coord.should_stop():
im, l = tu.read_batch(batch_size, train_img_path, wnid_labels)
sess.run(enqueue_op, feed_dict={x: im,y: l})
# creating and starting parallel threads to fill the queue
num_threads = 3
for i in range(num_threads):
t = threading.Thread(target=enqueue_batches)
t.setDaemon(True)
t.start()
# operation to write logs for tensorboard visualization
train_writer = tf.summary.FileWriter(os.path.join(summary_path, 'train\\'), sess.graph)
start_time = time.time()
for e in range(sess.run(epoch), epochs):
for i in range(num_batches):
_, step = sess.run([optimizer, global_step], feed_dict={lr: learning_rate, keep_prob: dropout})
#train_writer.add_summary(summary, step)
# decaying learning rate
if step == 170000 or step == 350000:
learning_rate /= 10
# display current training informations
if step % display_step == 0:
c, a = sess.run([loss, accuracy], feed_dict={lr: learning_rate, keep_prob: 1.0})
print ('Epoch: {:03d} Step/Batch: {:09d} --- Loss: {:.7f} Training accuracy: {:.8f}'.format(e, step, c, a))
# make test and evaluate validation accuracy
val_im, val_cls = tu.read_validation_batch(batch_size, os.path.join(imagenet_path, 'val\\images'), os.path.join(imagenet_path, 'val\\val_annotations.txt'), os.path.join(imagenet_path, 'wnids.txt'))
v_a = sess.run(accuracy, feed_dict={x_b: val_im, y_b: val_cls, lr: learning_rate, keep_prob: 1.0})
# intermediate time
int_time = time.time()
#print ('Elapsed time: {}'.format(tu.format_time(int_time - start_time)))
print ('Validation accuracy: {:.04f}'.format(v_a))
# save weights to file
save_path = saver.save(sess, os.path.join(ckpt_path, 'johnnet-cnn.ckpt'))
#print('Variables saved in file: %s' % save_path)
end_time = time.time()
toc = time.clock()
elapsed_time = (toc-tic)/3600
print('elapsed time:', elapsed_time, 'hours')
#print ('Elapsed time: {}').format(tu.format_time(end_time - start_time))
save_path = saver.save(sess, os.path.join(ckpt_path, 'johnnet-cnn.ckpt'))
#print('Variables saved in file: %s' % save_path)
coord.request_stop()
if __name__ == '__main__':
DROPOUT = 0.4
MOMENTUM = .9
LAMBDA = 5e-06 # for weight decay
LEARNING_RATE = 1e-03
EPOCHS = 1
BATCH_SIZE = 200
CKPT_PATH = 'ckpt-johnnet'
if not os.path.exists(CKPT_PATH):
os.makedirs(CKPT_PATH)
SUMMARY = 'summary'
if not os.path.exists(SUMMARY):
os.makedirs(SUMMARY)
IMAGENET_PATH = 'tiny-imagenet-200'
DISPLAY_STEP = 1
TEST_STEP = 5
tic = time.clock()
train(
EPOCHS,
BATCH_SIZE,
LEARNING_RATE,
DROPOUT,
MOMENTUM,
LAMBDA,
False,
IMAGENET_PATH,
DISPLAY_STEP,
TEST_STEP,
CKPT_PATH,
SUMMARY)
