问题
I have figured out what was causing this error, it was due to mismatch between labels and outputs, like I'm doing 8 class sentiment classification and my labels are (1,2,3,4,7,8,9,10) so it was unable to match predictions(1,2,3,4,5,6,7,8) with my labels, so that's why it was giving out of range error. My question is, why it didn't gave me error in this line c_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits,Y) , how it's matching labels with predictions in this case as opposed to in in_top_k? I think c_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits,Y) should give me error because predictions and labels are not same. Why I'm not getting targets out of range error in cross entropy function?
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
import math
import os
from nltk.tokenize import TweetTokenizer
batch = 500
start = 0
end = batch - 1
learning_rate = 0.2
num_classes = 8
path = "/home/indy/Downloads/aclImdb/train/pos"
time_steps = 250
embedding = 50
def get_embedding():
gfile_path = os.path.join("/home/indy/Downloads/glove.6B", "glove.6B.50d.txt")
f = open(gfile_path,'r')
embeddings = {}
for line in f:
sp_value = line.split()
word = sp_value[0]
embedding = [float(value) for value in sp_value[1:]]
embeddings[word] = embedding
return embeddings
ebd = get_embedding()
def get_y(file_name):
y_value = file_name.split('_')
y_value = y_value[1].split('.')
return y_value[0]
def get_x(path,file_name):
file_path = os.path.join(path,file_name)
x_value = open(file_path,'r')
for line in x_value:
x_value = line.replace("<br /><br />","")
x_value = x_value.lower()
tokeniz = TweetTokenizer()
x_value = tokeniz.tokenize(x_value)
padding = 250 - len(x_value)
if padding > 0:
p_value = ['pad' for i in range(padding)]
x_value = np.concatenate((x_value,p_value))
x_value = [ebd['value'] for value in x_value]
return x_value
def batch_f(path):
directory = os.listdir(path)
y = [get_y(directory[i]) for i in range(len(directory))]
x = [get_x(path,directory[i]) for i in range(len(directory))]
return x,y
X = tf.placeholder(tf.float32, [batch,time_steps,embedding])
Y = tf.placeholder(tf.int32, [batch])
def build_nlp_model(x, _units, lstm_layers,num_classes):
x = tf.transpose(x, [1, 0, 2])
x = tf.reshape(x, [-1, embedding])
x = tf.split(0, time_steps, x)
lstm = tf.nn.rnn_cell.LSTMCell(num_units = _units, state_is_tuple = True)
multi_lstm = tf.nn.rnn_cell.MultiRNNCell([lstm] * lstm_layers, state_is_tuple = True)
outputs , state = tf.nn.rnn(multi_lstm,x, dtype = tf.float32)
weights = tf.Variable(tf.random_normal([_units,num_classes]))
biases = tf.Variable(tf.random_normal([num_classes]))
logits = tf.matmul(outputs[-1], weights) + biases
return logits
logits = build_nlp_model(X,400,4,num_classes)
c_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits,Y)
loss = tf.reduce_mean(c_loss)
decayed_learning_rate = tf.train.exponential_decay(learning_rate,0,10000,0.9)
optimizer= tf.train.AdamOptimizer(decayed_learning_rate)
minimize_loss = optimizer.minimize(loss)
correct_predict = tf.nn.in_top_k(logits, Y, 1)
accuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
for i in range(25):
x, y = batch_f(path)
sess.run(minimize_loss,feed_dict={X : x, Y : y})
accu = sess.run(accuracy,feed_dict = {X: x, Y: y})
cost = sess.run(loss,feed_dict = {X: x,Y: y})
start = end
end = (start + batch)
print ("Minibatch Loss = " + "{:.6f}".format(cost) + ", Training Accuracy= " + "{:.5f}".format(accu))
This is the error stack.
(500, 250, 50)
(500,)
Traceback (most recent call last):
File "nlp.py", line 115, in <module>
accu = sess.run(accuracy,feed_dict = {X: x, Y: y})
File "/home/indy/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 372, in run
run_metadata_ptr)
File "/home/indy/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 636, in _run
feed_dict_string, options, run_metadata)
File "/home/indy/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 708, in _do_run
target_list, options, run_metadata)
File "/home/indy/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 728, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.InvalidArgumentError: targets[0] is out of range
[[Node: InTopK = InTopK[T=DT_INT32, k=1, _device="/job:localhost/replica:0/task:0/cpu:0"](add, _recv_Placeholder_1_0)]]
Caused by op u'InTopK', defined at:
File "nlp.py", line 102, in <module>
correct_predict = tf.nn.in_top_k(logits, Y, 1)
File "/home/indy/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 890, in in_top_k
targets=targets, k=k, name=name)
File "/home/indy/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/ops/op_def_library.py", line 704, in apply_op
op_def=op_def)
File "/home/indy/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2260, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/indy/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1230, in __init__
self._traceback = _extract_stack()
来源：https://stackoverflow.com/questions/38881918/tensorflow-tf-nn-in-top-k-targets-out-of-range-error