[Python] How to pass parameters in tensorflow

I want to train a network using tensorflow, I choose the "Inception_resnet_v2" as the net(from here), here is my train code,

def train(train_dir, annotations, max_step, checkpoint_dir='./checkpoint2/'):
# train the model
features = tf.placeholder("float32", shape=[None, IMAGE_SIZE, IMAGE_SIZE, IMAGE_CHANNEL], name="features")
labels = tf.placeholder("float32", [None], name="labels")
one_hot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=80)
keep_prob = tf.placeholder("float32")
isTraining = tf.placeholder("bool")
#train_step, cross_entropy, logits, keep_prob = network.inference(features, one_hot_labels)
logits, _=inception_resnet_v2.inception_resnet_v2(features,80,isTraining,keep_prob)
# calculate loss
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_labels, logits=logits))

train_step = tf.train.AdamOptimizer(LEARNINGRATE).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))


image_list, label_list = scene_input2.get_files(train_dir, annotations)
image_batch, label_batch = scene_input2.get_batch(image_list, label_list, IMAGE_SIZE, IMAGE_SIZE, BATCH_SIZE)

with tf.Session() as sess:
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
print('Restore the model from checkpoint %s' % ckpt.model_checkpoint_path)
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
start_step = int(ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
else:
sess.run(tf.global_variables_initializer())
start_step = 0
print('start training from new state')
logger = scene_input.train_log(LOGNAME)

coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)

try:
# Check if stop was requested.
step=start_step
while not coord.should_stop() and step<start_step + max_step:
start_time = time.time()
x, y = sess.run([image_batch, label_batch])
#y = tf.one_hot(indices=tf.cast(y, tf.int32), depth=80)
#y = sess.run(y)
sess.run(train_step, feed_dict={features: x, labels: y, isTraining: True, keep_prob: 0.5})
if step % 50 == 0:
train_accuracy = sess.run(accuracy, feed_dict={features: x, labels: y, isTraining: False, keep_prob: 1})
train_loss = sess.run(cross_entropy, feed_dict={features: x, labels: y, isTraining:False, keep_prob: 1})
duration = time.time() - start_time
logger.info("step %d: training accuracy %g, loss is %g (%0.3f sec)" % (step, train_accuracy, train_loss, duration))
if step % 1000 == 1:
saver.save(sess, CHECKFILE, global_step=step)
print('writing checkpoint at step %s' % step)
step=step+1


except tf.errors.OutOfRangeError:
print('done!')
finally:
#Request that the threads stop.After this is called, calls to should_stop() will return True.
coord.request_stop()
coord.join(threads)


but when i train the net, i meet an error:

Traceback (most recent call last):
File "scene2.py", line 245, in <module>
train(FLAGS.train_dir, FLAGS. annotations, FLAGS.max_step)
File "scene2.py", line 82, in train
logits, _=inception_resnet_v2.inception_resnet_v2(features,80,isTraining,keep_prob)
File "/home/vision/inception_resnet_v2.py", line 357, in inception_resnet_v2
scope='Dropout')
File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1216, in dropout
_scope=sc)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/layers/core.py", line 247, in __init__
self.rate = min(1., max(0., rate))
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/ops.py", line 564, in __bool__
raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "
TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.
vision@Hjl:~/$ CUDA_VISIBLE_DEVICES=0 python3 scene2.py --mode train
Traceback (most recent call last):
File "scene2.py", line 245, in <module>
train(FLAGS.train_dir, FLAGS. annotations, FLAGS.max_step)
File "scene2.py", line 82, in train
logits, _=inception_resnet_v2.inception_resnet_v2(features,80,isTraining,keep_prob)
File "/home/vision/inception_resnet_v2.py", line 357, in inception_resnet_v2
scope='Dropout')
File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 181, in func_with_args
return func(*args, **current_args)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1216, in dropout
_scope=sc)
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/layers/core.py", line 247, in __init__
self.rate = min(1., max(0., rate))
File "/usr/local/lib/python3.4/dist-packages/tensorflow/python/framework/ops.py", line 564, in __bool__
raise TypeError("Using a `tf.Tensor` as a Python `bool` is not allowed. "
TypeError: Using a `tf.Tensor` as a Python `bool` is not allowed. Use `if t is not None:` instead of `if t:` to test if a tensor is defined, and use TensorFlow ops such as tf.cond to execute subgraphs conditioned on the value of a tensor.


when i pass keep_prob and keep_prob to inception_resnet_v2.inception_resnet_v2(features,80,isTraining,keep_prob), the error occurred. How can I solve this problem?

when i train the network, i want to set keep_prob = 0.5, isTraining = True, but at the same time , every 50 step, i want to watch the model's train_accuracy and train_loss, so i should set keep_prob = 1.0, isTraining = False, am I right? How can I implement it?

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