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Ich möchte die Wahrscheinlichkeiten jedes Testbild betrachten, so modifiziert ich den Code (cifar10_eval.py) wie diesertensorflow cifar-10 Bewertungsbeispiel softmax Ausgänge
def eval_once(saver, summary_writer, logits, labels, top_k_op, summary_op):
...............
while step < num_iter and not coord.should_stop():
result1, result2 = sess.run([logits, labels])
print('Step:', step, 'result',result1, 'Label:', result2)
...............
und I führen das Python Code wie folgt.
# python cifar10_eval.py --batch_size=1 --run_once=True
Die Bildschirm Ergebnisse sind wie diese
Step: 0 result [[ 0.01539493 -0.00109618 -0.00364288 -0.00898853 -0.00086198 0.00587899 0.00981337 -0.00785329 -0.00282823 -0.00171288]] Label: [4]
Step: 1 result [[ 0.01539471 -0.00109601 -0.00364273 -0.00898863 -0.00086192 0.005879 0.00981339 -0.00785322 -0.00282811 -0.00171296]] Label: [7]
Step: 2 result [[ 0.01539475 -0.00109617 -0.00364274 -0.00898876 -0.00086183 0.00587886 0.00981328 -0.00785333 -0.00282814 -0.00171295]] Label: [8]
Step: 3 result [[ 0.01539472 -0.00109597 -0.00364275 -0.0089886 -0.00086183 0.00587902 0.00981344 -0.00785326 -0.00282817 -0.00171299]] Label: [4]
Step: 4 result [[ 0.01539488 -0.00109631 -0.00364294 -0.00898863 -0.00086199 0.00587896 0.00981327 -0.00785329 -0.00282809 -0.00171307]] Label: [0]
Step: 5 result [[ 0.01539478 -0.00109607 -0.00364292 -0.00898858 -0.00086194 0.00587904 0.00981335 -0.0078533 -0.00282818 -0.00171321]] Label: [4]
Step: 6 result [[ 0.01539493 -0.00109627 -0.00364277 -0.00898873 -0.0008618 0.00587892 0.00981339 -0.00785325 -0.00282807 -0.00171289]] Label: [9]
Step: 7 result [[ 0.01539504 -0.00109619 -0.0036429 -0.00898865 -0.00086194 0.00587894 0.0098133 -0.00785331 -0.00282818 -0.00171294]] Label: [4]
Step: 8 result [[ 0.01539493 -0.00109627 -0.00364286 -0.00898867 -0.00086183 0.00587899 0.00981332 -0.00785329 -0.00282825 -0.00171283]] Label: [8]
Step: 9 result [[ 0.01539495 -0.00109617 -0.00364286 -0.00898852 -0.00086186 0.0058789 0.00981337 -0.00785326 -0.00282827 -0.00171287]] Label: [9]
Die Label-Werte scheinen gut zu sein, aber die Logits Ausgänge scheinen dieselben Werte zu sein! Warum? Jeder kann mir den Grund sagen?
Dies ist der neue Quellcode cifar10_eval.py.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import math
import time
import numpy as np
import tensorflow as tf
#from tensorflow.models.image.cifar10 import cifar10
import cifar10
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('eval_dir', '/tmp/cifar10_eval',
"""Directory where to write event logs.""")
tf.app.flags.DEFINE_string('eval_data', 'test',
"""Either 'test' or 'train_eval'.""")
tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/cifar10_train',
"""Directory where to read model checkpoints.""")
tf.app.flags.DEFINE_integer('eval_interval_secs', 60 * 5,
"""How often to run the eval.""")
tf.app.flags.DEFINE_integer('num_examples', 10000,
"""Number of examples to run.""")
tf.app.flags.DEFINE_boolean('run_once', True,
"""Whether to run eval only once.""")
def eval_once(saver, summary_writer, logits, labels, top_k_op, summary_op):
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
else:
print('No checkpoint file found')
return
# Start the queue runners.
coord = tf.train.Coordinator()
try:
threads = []
for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
start=True))
#num_iter = int(math.ceil(FLAGS.num_examples/FLAGS.batch_size))
#total_sample_count = num_iter * FLAGS.batch_size
num_iter = FLAGS.num_examples
total_sample_count = FLAGS.num_examples
print (num_iter, FLAGS.batch_size, total_sample_count)
true_count = 0 # Counts the number of correct predictions.
step = 0
time.sleep(1)
while step < num_iter and not coord.should_stop():
result1, result2 = sess.run([logits, labels])
#label = sess.run(labels)
print('Step:', step, 'result',result1, 'Label:', result2)
step += 1
precision = true_count/step
print('Summary -- Step:', step, 'Accurcy:',true_count * 100.0/step * 1.0,)
print('%s: total:%d true:%d precision @ 1 = %.3f' % (datetime.now(), total_sample_count, true_count, precision))
except Exception as e: # pylint: disable=broad-except
coord.request_stop(e)
coord.request_stop()
coord.join(threads, stop_grace_period_secs=10)
def evaluate():
"""Eval CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
# Get images and labels for CIFAR-10.
eval_data = FLAGS.eval_data == 'test'
images, labels = cifar10.inputs(eval_data=eval_data,)
# Build a Graph that computes the logits predictions from the
# inference model. logits is softmax
logits = cifar10.inference(images)
# Calculate predictions.
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
cifar10.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
graph_def = tf.get_default_graph().as_graph_def()
summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir,
graph_def=graph_def)
while True:
eval_once(saver, summary_writer, logits, labels,top_k_op, summary_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def main(argv=None): # pylint: disable=unused-argument
cifar10.maybe_download_and_extract()
if tf.gfile.Exists(FLAGS.eval_dir):
tf.gfile.DeleteRecursively(FLAGS.eval_dir)
tf.gfile.MakeDirs(FLAGS.eval_dir)
print('Evaluate Start')
evaluate()
if __name__ == '__main__':
tf.app.run()
@OmG sehr gut, Sie sind viele dieser softmax Hinzufügen von Tags, aber der Tag hat keine Beschreibung. Dies macht Ihre Bewertungen sehr schwierig zu beurteilen. Könnten Sie das auch hinzufügen? https://stackoverflow.com/edit-tag-wiki/117307 –