I am running tensorflow version 1.3.0 on ubuntu 16.04.I am playing with a code where my main intention is to visualise a graph on tensorboard. While running the code everything seems to be perfectly fine when the code is ran for the very first time. However after that when I ran the code for the 2nd time I get this error:
InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [?,784]
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Here is the traceback:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-26-149c9b9d8878> in <module>()
11 sess.run(optimizer, feed_dict={x: batch_xs, y:
batch_ys})
12 avg_cost += sess.run(cost_function, feed_dict={x:
batch_xs, y: batch_ys})/total_batch
---> 13 summary_str = sess.run(merged_summary_op,
feed_dict={x: batch_xs, y: batch_ys})
14 summary_writer.add_summary(summary_str,
iteration*total_batch + i)
15 if iteration % display_step == 0:
/home/niraj/anaconda2/lib/python2.7/site-
packages/tensorflow/python/client/session.pyc in run(self, fetches,
feed_dict, options, run_metadata)
893 try:
894 result = self._run(None, fetches, feed_dict, options_ptr,
--> 895 run_metadata_ptr)
896 if run_metadata:
897 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
InvalidArgumentError Traceback (most recent call
last)
<ipython-input-26-149c9b9d8878> in <module>()
11 sess.run(optimizer, feed_dict={x: batch_xs, y:
batch_ys})
12 avg_cost += sess.run(cost_function, feed_dict={x:
batch_xs, y: batch_ys})/total_batch
---> 13 summary_str = sess.run(merged_summary_op,
feed_dict={x: batch_xs, y: batch_ys})
14 summary_writer.add_summary(summary_str,
iteration*total_batch + i)
15 if iteration % display_step == 0:
/home/niraj/anaconda2/lib/python2.7/site-
packages/tensorflow/python/client/session.pyc in run(self, fetches,
feed_dict, options, run_metadata)
893 try:
894 result = self._run(None, fetches, feed_dict, options_ptr,
--> 895 run_metadata_ptr)
896 if run_metadata:
897 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/home/niraj/anaconda2/lib/python2.7/site-
packages/tensorflow/python/client/session.pyc in _run(self, handle,
fetches, feed_dict, options, run_metadata)
1122 if final_fetches or final_targets or (handle and
feed_dict_tensor):
1123 results = self._do_run(handle, final_targets,
final_fetches,
-> 1124 feed_dict_tensor, options,
run_metadata)
1125 else:
1126 results = []
/home/niraj/anaconda2/lib/python2.7/site-
packages/tensorflow/python/client/session.pyc in _do_run(self, handle,
target_list, fetch_list, feed_dict, options, run_metadata)
1319 if handle is None:
1320 return self._do_call(_run_fn, self._session, feeds,
fetches, targets,
-> 1321 options, run_metadata)
1322 else:
1323 return self._do_call(_prun_fn, self._session, handle,
feeds, fetches)
/home/niraj/anaconda2/lib/python2.7/site-
packages/tensorflow/python/client/session.pyc in _do_call(self, fn,
*args)
1338 except KeyError:
1339 pass
-> 1340 raise type(e)(node_def, op, message)
1341
1342 def _extend_graph(self):
Here is the code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/home/niraj/Documents/artificial
intelligence/projects/tensorboard", one_hot=True)
learning_rate = 0.01
training_iteration = 200
batch_size = 100
display_step = 2
# TF graph input
x = tf.placeholder('float32', [None, 784]) # mnist data image of shape
28*28=784
y = tf.placeholder('float32',[None, 10]) # 0-9 digits recognition =>
10 classes
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
with tf.name_scope("Wx_b") as scope:
model = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
w_h = tf.summary.histogram("weights", W)
b_h = tf.summary.histogram("biases", b)
with tf.name_scope("cost_function") as scope:
cost_function = -tf.reduce_sum(y*tf.log(model))
tf.summary.scalar("cost_function", cost_function)
with tf.name_scope("train") as scope:
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)
init = tf.global_variables_initializer()
merged_summary_op = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(init)
summary_writer = tf.summary.FileWriter('/home/niraj/Documents/artificial intelligence/projects/tensorboard', graph=sess.graph)
for iteration in range(training_iteration):
avg_cost = 0
total_batch = int(mnist.train.num_examples/batch_size)
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys})
avg_cost += sess.run(cost_function, feed_dict={x: batch_xs, y: batch_ys})/total_batch
summary_str = sess.run(merged_summary_op, feed_dict={x: batch_xs, y: batch_ys})
summary_writer.add_summary(summary_str, iteration*total_batch + i)
if iteration % display_step == 0:
print "Iteration:", '%04d' % (iteration + 1), "cost=", "{:.9f}".format(avg_cost)
print "Tuning completed!"
predictions = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(predictions, "float"))
print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})
Remind you this code works perfectly fine when I am running it for the very first time.On the 2nd run it is giving the error.However when I close the notebook and my jupyter terminal and then reopening it and running it again,it will again run without any errors and on the 2nd run it gives the above error.
I'm having the same issue, and have so far found that the error does not occur when I remove the summary operation. I'll update this if I find a way to get it to work with the summary...
UPDATE:
I fixed this by following the suggestion here: Error with feed values for placeholders when running the merged summary op
I replaced
tf.summary.merge_allwithtf.summary.merge([summary_var1, summary_var2])An easier way to fix this is to call
tf.reset_default_graph()at the end of your loop, before you start training again.