def evaluate_orig():
"""Eval CIFAR-10 for a number of steps."""
with tf.Graph().as_default() as g:
# Get images and labels for CIFAR-10.
eval_data = FLAGS.eval_data == 'test'
images, labels = itNet.inputs(eval_data=eval_data)
# Build a Graph that computes the logits predictions from the
# inference model.
logits = itNet.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(
itNet.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.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)
while True:
eval_once(saver, summary_writer, top_k_op, summary_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def one_eval():
images_test, labels_test = itNet.inputs(eval_data=True)
num_iter = int(FLAGS.num_examples / FLAGS.batch_size)
print("Number of iterations = {}".format(num_iter))
true_count = 0 # Counts the number of correct predictions.
total_sample_count = num_iter * FLAGS.batch_size
print("total_sample_count = {}".format(total_sample_count))
step = 0
top_k_op = tf.nn.in_top_k(logits_test, labels_test, 1)
while step < num_iter:
print("Step is {}".format(step))
logits_test = itNet.inference_switch(images_test_batch, FLAGS.network_architecture)
print(logits_test)
top_k_op = tf.nn.in_top_k(logits_test, labels_test, 1)
predictions = top_k_op
batchPredictions = np.asarray(predictions)
print("Here's the batch predictions")
print(batchPredictions)
true_count += np.sum(predictions)
step += 1
# Compute precision @ 1.
precision = true_count / total_sample_count
print('%s: precision @ 1 = %.5f' % (datetime.now(), precision))
return precision
def evaluate(returnConfusionMatrix=True):
num_classes = itNet_input.NUM_CLASSES
#if FLAGS.binarise_label > 0:
# num_classes = 2
#elif FLAGS.binarise_label == -2:
# num_classes = 4
#elif FLAGS.binarise_label == -3:
# num_classes = 3
#print("And again {} classes".format(num_classes))
"""Eval CIFAR-10 for a number of steps."""
with tf.Graph().as_default() as g:
# Get images and labels for CIFAR-10.
#print("getting input data {}".format(FLAGS.eval_data))
eval_data = FLAGS.eval_data == 'test'
images, labels = itNet.inputs(eval_data=True)
# Build a Graph that computes the logits predictions from the
# inference model.
#print("calling inference")
logits = itNet.inference_switch(images, FLAGS.network_architecture)
predictions = tf.argmax(logits, 1)
gen_confusionMatrix = tf.confusion_matrix(labels,
predictions,
num_classes=num_classes)
# Calculate predictions.
#print("calculating predictions")
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# Restore the moving average version of the learned variables for eval.
#print("restore moving average version of learned variables")
variable_averages = tf.train.ExponentialMovingAverage(
itNet.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.
#print("Building a summary")
summary_op = tf.summary.merge_all()
#print("And a summary writer")
summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)
logfile = os.path.join(FLAGS.mylog_dir_eval, "log_evals.txt")
log = open(logfile, 'w')
start = datetime.now()
runtimes = 0
while True:
#print("Calling eval_once")
precision, confusionMatrix, predLabels = eval_once(
saver, summary_writer, top_k_op, summary_op,
gen_confusionMatrix, predictions)
#precision = eval_once(saver, summary_writer, top_k_op, summary_op)
if FLAGS.run_once:
if returnConfusionMatrix:
return precision, confusionMatrix
#return precision
else:
return precision
break
if FLAGS.run_times != 0 and runtimes > FLAGS.run_times:
break
if FLAGS.run_times == 0: #only sleep when it's not a limited run
time.sleep(FLAGS.eval_interval_secs)
np.set_printoptions(threshold='nan')
print('%s: precision @ 1 = %.3f' % (datetime.now(), precision))
print('{}: confusionMatrix: \n {}'.format(datetime.now(),
confusionMatrix))
print(
'AND predictions (provided you only used one thread!!!): \n ')
print(np.array2string(predLabels, separator=', '))
#print('AND predictions: \n')
#print("{}".format(repr(predLabels)))
#avg = np.average(predLabels)
#print("Average is {}".format(avg))
rightNow = datetime.now()
difference = rightNow - start
log.write(
"*******************************************************\n")
ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
log.write("The checkpoint was: {} ok now".format(
ckpt.model_checkpoint_path))
else:
log.write("No checkpoint found yet")
log.write("time: {} seconds \n".format(difference.total_seconds()))
log.write('precision @ 1 = %.5f \n' % (precision))
#log.write('confusionMatrix: \n {} \n'.format(confusionMatrix))
cmString = np.array2string(confusionMatrix, separator='\t')
cmString = cmString.replace('[', '')
cmString = cmString.replace(']', '')
log.write('confusionMatrix: \n {} \n'.format(cmString))
#log.write("******************************************************* \n")
log.flush()
runtimes = runtimes + 1