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Beim Ausführen eines Tests CNN bekomme ich immer diesen Fehler, wenn Sie versuchen, die Sitzung mit sess.close() zu schließen oder Coordinator zu bitten, die Threads zu stoppen und zu sammeln. Anscheinend versucht die Sitzung zu schließen, während noch Threads ausgeführt werden. Ich kann einfach keinen Weg finden, dies zu verhindern. Oder wenn es eine bessere/korrekte Art gibt, Warteschlangen und Threads im Tensorfluss zu verwenden ...Tensor Flow-Warteschlange schließt nicht. Probleme mit tf.train.start_queue_runners (sess)
Vielen Dank im Voraus!
Es gibt immer ein Bündel von:
2017-10-24 15:48:02.625448: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\kernels\queue_base.cc:295] _20_input_p
roducer/input_producer: Skipping cancelled enqueue attempt with queue not closed
Gefolgt von:
ERROR:tensorflow:Exception in QueueRunner: Enqueue operation was cancelled
[[Node: batch/fifo_queue_enqueue = QueueEnqueueV2[Tcomponents=[DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0
/task:0/cpu:0"](batch/fifo_queue, Squeeze_1/_13, input_producer_1/Gather_1/_15)]]
Traceback (most recent call last):
File "<stdin>", line 30, in <module>
ERROR:tensorflow:Exception in QueueRunner: Enqueue operation was cancelled
[[Node: batch_1/fifo_queue_enqueue = QueueEnqueueV2[Tcomponents=[DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica
:0/task:0/cpu:0"](batch_1/fifo_queue, Squeeze/_37, input_producer/Gather_1/_39)]]
ERROR:tensorflow:Exception in QueueRunner: Enqueue operation was cancelled
[[Node: batch_1/fifo_queue_enqueue = QueueEnqueueV2[Tcomponents=[DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica
:0/task:0/cpu:0"](batch_1/fifo_queue, Squeeze/_37, input_producer/Gather_1/_39)]]
Exception in thread Thread-53:
Traceback (most recent call last):
File "C:\Program Files\Anaconda3\lib\threading.py", line 916, in _bootstrap_inner
self.run()
File "C:\Program Files\Anaconda3\lib\threading.py", line 864, in run
self._target(*self._args, **self._kwargs)
File "C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\training\queue_runner_impl.py", line 238, in _run
enqueue_callable()
File "C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1235, in _single_operation_run
target_list_as_strings, status, None)
File "C:\Program Files\Anaconda3\lib\contextlib.py", line 89, in __exit__
next(self.gen)
File "C:\Program Files\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 466, in raise_exception_on_not_ok_stat
us
Unten finden Sie den Code, der auf Beispiele aus tf Handbuch und GitHub basierend schrieb:
"""My general framework to construct a tensor flow data set of images for regression.
The genetal idea is to: create a list of image names (i.e the path to each image).
The image list must have also labels. In the case of a regression this can be multiple variables.
"""
import csv
import os
import sys
import plotly as py
import plotly.graph_objs as go
import math
import numpy as np
import tensorflow as tf
#First neet to get image paths and their respective labels
chn = 1
im_h = 424
im_w = 511
#resize image
size = 0.1
#size of test set
p = 0.25
def imtensors(im_path, chn, im_h, im_w, size):
im_h = int(im_h*size)
im_w = int(im_w*size)
ima_tensors = tf.read_file(im_path)
ima_tensors = tf.image.decode_png(ima_tensors, channels=chn)
ima_tensors = tf.image.resize_images(ima_tensors, [im_h, im_w])
return ima_tensors
dbname = 'simpRDB.csv'
imagepaths, y = list(), list()
#read the csv as a dictionary
with open(dbname, newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
imagepaths.append(row['path'])
y.append(float(row['w']))
n = len(y)
ntest = int(n*p)
#remember that in py the index starts at 0 and x[a:d] -> a,b,c
impath_test = imagepaths[0:ntest]
y_test = y[0:ntest]
impath_train = imagepaths[ntest+1:n]
y_train = y[ntest+1:n]
#now convert to tensors
impath_test = tf.convert_to_tensor(impath_test, dtype=tf.string)
y_test = tf.convert_to_tensor(y_test, dtype=tf.float32)
im_test, y_test = tf.train.slice_input_producer([impath_test, y_test])
im_test = imtensors(im_test, chn, im_h, im_w, size)
impath_train = tf.convert_to_tensor(impath_train, dtype=tf.string)
y_train = tf.convert_to_tensor(y_train, dtype=tf.float32)
im_train, y_train = tf.train.slice_input_producer([impath_train, y_train])
im_train = imtensors(im_train, chn, im_h, im_w, size)
##################################################
# -----------------------------------------------
# This is a classic CNN with some spice.
# The basic change isat the output node. Instead of
# use a softmax or other multiclass we a re using a
# a fully regressor estimator to the last layer of
# nodes.
# -----------------------------------------------
# Parameters
learning_rate = 0.001
num_steps = 10000
b_size = 8
display_step = 100
# Network Parameters
dropout = 0.3 # rate to drop input
#create batched train set
#Use small batchs because CPU/GPU can run out of memory
X, Y = tf.train.batch([im_train, y_train], batch_size=b_size, capacity=b_size*4, num_threads=4,
allow_smaller_final_batch=True)
X_test, Y_test = tf.train.batch([im_test, y_test], batch_size=b_size, capacity=b_size*4,
num_threads=4, allow_smaller_final_batch=True)
#First lets define the weights and bias in a more sistematic fashion.
#The weights are going to be initialized as random weights wit values near zero. This is a good
#practice for neuralnets in general
inp_h = int(im_h*size)
inp_w = int(im_w*size)
#placeholders for model cheking
x = tf.placeholder(tf.float32, shape=[None, inp_h, inp_w, 1])
y_ = tf.placeholder(tf.float32, shape=[None, 1])
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
#The bias neurons are normaly initialized slightly positive for a ReLU activation function in order
# to prevent "dead neurons"
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
#Now convolution layers: They are going to have size one with zero pad.
# The arguent strides deifne the size of the window. So the output is of the same size.
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
#And the polling are going to be 2x2 blocks side by side.
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
#Define weights and biases
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
#after 2 max_poling 2x2 the 'image'size is reduce by 4. Need to use ceil (intrinsics of TF)
h_val = math.ceil(math.ceil(inp_h/2)/2)
w_val = math.ceil(math.ceil(inp_w/2)/2)
W_fc1 = weight_variable([h_val * w_val * 64, 1024])
b_fc1 = bias_variable([1024])
W_d1 = weight_variable([1024, 100])
b_d1 = bias_variable([100])
w_out = weight_variable([100, 1])
b_out = bias_variable([1])
# Create model
def conv_net(x, dropout, reuse, is_training):
# Define a scope for reusing the variables
with tf.variable_scope('ConvNet', reuse=reuse):
#no need for dropout if evaluating the model
# Convolution Layer with 32 filters and a kernel size of
conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)
# Max Pooling (down-sampling) with strides of 2 and kernel size of 2
pool1 = max_pool_2x2(conv1)
#In order to build a deep network, we stack several layers of this type.
# The second layer will have 64 features for each 5x5 patch.
conv2 = tf.nn.relu(conv2d(pool1, W_conv2) + b_conv2)
# Max Pooling (down-sampling) with strides of 2 and kernel size of 2
pool2 = max_pool_2x2(conv2)
#DENSELY CONNECTED LAYER
#Now that the image size has been reduced to nxm, we add a fully-connected layer
# with 1024(32X32) neurons to allow processing on the entire image.
# Flatten the data to a 1-D vector for the fully connected layer
fc1 = tf.contrib.layers.flatten(pool2)
# Fully connected layer
fc1_flat = tf.nn.relu(tf.matmul(fc1, W_fc1) + b_fc1)
# Apply Dropout (if is_training is False, dropout is not applied)
fc1_drop = tf.layers.dropout(fc1_flat, rate=dropout, training=is_training)
# dense layer with size reduction for input to final prediction layer
d1 = tf.nn.relu(tf.matmul(fc1_drop, W_d1) + b_d1)
# Only one output aka regression
out = tf.matmul(d1, w_out)+b_out
return out
#Create graph for trainig and a graph for prediction sharing the same weights
out_train = conv_net(X, dropout, reuse=False, is_training=True)
#no drop out at evaluation
out_test = conv_net(X_test, dropout, reuse=True, is_training=False)
# Define loss and optimizer.
#reduce absulute sum of squares
loss_op = tf.reduce_mean(tf.abs(out_train - Y))
loss_ts = tf.reduce_mean(tf.abs(out_test - Y_test))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)
#Save the loss for training and testing for plot latter
losses_op = []
losses_ts = []
steps = []
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
# Saver object
saver = tf.train.Saver()
##################
# Start training
with tf.Session() as sess:
# Run the initializer
sess.run(init)
# Start the data queue
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess)
# Training cycle
try:
for step in range(1, num_steps+1):
if step % display_step == 0:
# Run optimization and calculate batch loss and accuracy
_, loss, loss2 = sess.run([train_op, loss_op, loss_ts])
print("Step " + str(step) + ", Minibatch Loss = " + \
"{:.4f}".format(loss) + ", Loss Testbatch = " + "{:.4f}".format(loss2))
steps.append(step)
losses_op.append(loss)
losses_ts.append(loss2)
else:
# Only run the optimization op (backprop)
sess.run(train_op)
print("Optimization Finished!")
except Exception as e:
coord.request_stop(e)
finally:
#Something about it is safer to call twice
coord.request_stop()
coord.join(threads)
saver.save(sess, model_path)
#plota of loss over steps
p_loss1 = go.Scatter(
x=steps,
y=losses_op,
mode='lines',
name='Loss training minibatch'
)
p_loss2 = go.Scatter(
x=steps,
y=losses_ts,
mode='lines',
name='Loss evaluation minibatch'
)
data = [p_loss1, p_loss2]
# Save your model
#Loss of final model on the test set
#no dropout for the test set
m_name = 'bw_model_1'
save_path = os.path.join(os.getcwd(), 'bw_models')
if not os.path.exists(save_path):
os.mkdir(save_path)
model_path = os.path.join(save_path, m_name)
#save a graph of the loss over time
py.offline.plot(data, filename=model_path)
#end