I just run a simple code and want to get accuracy after training. I load the model that I saved, but when I want to get accuracy, I get something wrong. Why?
# coding=utf-8
from color_1 import read_and_decode, get_batch, get_test_batch
import AlexNet
import cv2
import os
import time
import numpy as np
import tensorflow as tf
import AlexNet_train
import math
batch_size=128
num_examples = 1000
crop_size=56
def evaluate(test_x, test_y):
image_holder = tf.placeholder(tf.float32, [batch_size, 56, 56, 3], name='x-input')
label_holder = tf.placeholder(tf.int32, [batch_size], name='y-input')
y = AlexNet.inference(image_holder,evaluate,None)
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(label_holder,1))
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
saver = tf.train.Saver()
with tf.Session() as sess:
init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())
coord = tf.train.Coordinator()
sess.run(init_op)
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
ckpt=tf.train.get_checkpoint_state(AlexNet_train.MODEL_SAVE_PATH)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, os.path.join(AlexNet_train.MODEL_SAVE_PATH, ckpt_name))
print('Loading success, global_step is %s' % global_step)
step=0
image_batch, label_batch = sess.run([test_x, test_y])
accuracy_score=sess.run(accuracy,feed_dict={image_holder: image_batch,
label_holder: label_batch})
print("After %s training step(s),validation "
"precision=%g" % (global_step, accuracy_score))
coord.request_stop()
coord.join(threads)
def main(argv=None):
test_image, test_label = read_and_decode('val.tfrecords')
test_images, test_labels = get_test_batch(test_image, test_label, batch_size, crop_size)
evaluate(test_images, test_labels)
if __name__=='__main__':
tf.app.run()
And here is error,it said that this line in my code is wrong:" correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(label_holder,1))"
Traceback (most recent call last):
File "/home/vrview/tensorflow/example/char/tfrecords/AlexNet/Alex_save/AlexNet_test.py", line 80, in <module>
tf.app.run()
File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 44, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "/home/vrview/tensorflow/example/char/tfrecords/AlexNet/Alex_save/AlexNet_test.py", line 76, in main
evaluate(test_images, test_labels)
File "/home/vrview/tensorflow/example/char/tfrecords/AlexNet/Alex_save/AlexNet_test.py", line 45, in evaluate
label_holder: label_batch})
File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 767, in run
run_metadata_ptr)
File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 965, in _run
feed_dict_string, options, run_metadata)
File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1015, in _do_run
target_list, options, run_metadata)
File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1035, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected dimension in the range [-1, 1), but got 1
[[Node: ArgMax_1 = ArgMax[T=DT_INT32, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_y-input_0, ArgMax_1/dimension)]]
Caused by op u'ArgMax_1', defined at:
File "/home/vrview/tensorflow/example/char/tfrecords/AlexNet/Alex_save/AlexNet_test.py", line 80, in <module>
tf.app.run()
File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 44, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "/home/vrview/tensorflow/example/char/tfrecords/AlexNet/Alex_save/AlexNet_test.py", line 76, in main
evaluate(test_images, test_labels)
File "/home/vrview/tensorflow/example/char/tfrecords/AlexNet/Alex_save/AlexNet_test.py", line 22, in evaluate
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(label_holder,1))
File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.py", line 263, in argmax
return gen_math_ops.arg_max(input, axis, name)
File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/ops/gen_math_ops.py", line 168, in arg_max
name=name)
File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2395, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/vrview/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1264, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): Expected dimension in the range [-1, 1), but got 1
[[Node: ArgMax_1 = ArgMax[T=DT_INT32, Tidx=DT_INT32, _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_y-input_0, ArgMax_1/dimension)]]
How to solve it?
Taking the part of this answer related to the problem here:
tf.argmax's definition states:
axis: A Tensor. Must be one of the following types: int32, int64.
int32, 0 <= axis < rank(input). Describes which axis of the input
Tensor to reduce across.
It seems, then, that the only way to run argmax on the last axis of the tensor is by giving it axis=-1, because of the "strictly less than" sign in the definition of the function.
Related
This is the part of the code
def train(x):
prediction = cnn(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction, labels=y))
optimizer = tf.train.AdadeltaOptimizer().minimize(cost)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in xrange(num_epochs):
epoch_loss = 0
for _ in xrange(batch_size):
_, c = sess.run([optimizer, cost])
epoch_loss += c
print('Epoch {} completed out of {} - loss {}'.format(epoch + 1, num_epochs, epoch_loss))
n_classes = 17
batch_size = 32
dropout_rate = 0.4
num_epochs = 10
train_set = read_image_dataset_tfrecordfile('train.tfrecord', resize=True)
train_set = train_set.batch(batch_size)
train_set.repeat(num_epochs)
train_iterator = train_set.make_one_shot_iterator()
x, y = train_iterator.get_next()
train(x)
When I run this it does only the first epoch and then throws OutOfRangeError, here the stack
Epoch 1 completed out of 10 - loss 5.82853866496e+11
Traceback (most recent call last):
File "/Users/user/PycharmProjects/ProveTF/main.py", line 113, in <module>
train(x)
File "/Users/user/PycharmProjects/ProveTF/main.py", line 83, in train
_, c = sess.run([optimizer, cost])
File "/Users/user/venv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 905, in run
run_metadata_ptr)
File "/Users/user/venv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1137, in _run
feed_dict_tensor, options, run_metadata)
File "/Users/user/venv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1355, in _do_run
options, run_metadata)
File "/Users/user/venv/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1374, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.OutOfRangeError: End of sequence
[[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[?,100,100,1], [?,17]], output_types=[DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](OneShotIterator)]]
Caused by op u'IteratorGetNext', defined at:
File "/Users/user/PycharmProjects/ProveTF/main.py", line 110, in <module>
x, y = train_iterator.get_next()
File "/Users/user/venv/lib/python2.7/site-packages/tensorflow/python/data/ops/iterator_ops.py", line 330, in get_next
name=name)), self._output_types,
File "/Users/user/venv/lib/python2.7/site-packages/tensorflow/python/ops/gen_dataset_ops.py", line 866, in iterator_get_next
output_shapes=output_shapes, name=name)
File "/Users/user/venv/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/Users/user/venv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3271, in create_op
op_def=op_def)
File "/Users/user/venv/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1650, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
OutOfRangeError (see above for traceback): End of sequence
[[Node: IteratorGetNext = IteratorGetNext[output_shapes=[[?,100,100,1], [?,17]], output_types=[DT_FLOAT, DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](OneShotIterator)]]
I tried to move the repeat() method in other places and I tried to write simply repeat() without the parameter, but it doesn't work anyway.
Any solutions or suggestions?
You need to assign train_set = train_set.repeat() just as you do with the batch method. It doesn't modify the dataset in place.
I'm trying to get an mnist cnn working so as to do predictions on one image at a time. I have taken the tensorflow tutorial code and am trying to use estimator.predict with the model but am currently getting the error:
InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 1 values, but the requested shape requires a multiple of 784
[[Node: Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](fifo_queue_DequeueUpTo/_53, Reshape/shape)]]
If I print the predict_data list given to the predict input function it contains 784 elements.
The model trained OK and evaluated OK.
The model is already trained so I have skipped the training code here but this is what I have:
def main(unused_argv):
# Load training and eval data
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
train_data = mnist.train.images # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)
# Create the Estimator
mnist_classifier = tf.estimator.Estimator(
model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
# Set up logging for predictions
# Log the values in the "Softmax" tensor with label "probabilities"
tensors_to_log = {"probabilities": "softmax_tensor"}
logging_hook = tf.train.LoggingTensorHook(
tensors=tensors_to_log, every_n_iter=50)
# # Train the model
# train_input_fn = tf.estimator.inputs.numpy_input_fn(
# x={"x": train_data},
# y=train_labels,
# batch_size=100,
# num_epochs=None,
# shuffle=True)
# mnist_classifier.train(
# input_fn=train_input_fn,
# steps=20000,
# hooks=[logging_hook])
# Evaluate the model and print results
# eval_input_fn = tf.estimator.inputs.numpy_input_fn(
# x={"x": eval_data},
# y=eval_labels,
# num_epochs=1,
# shuffle=False)
# eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
# print(eval_results)
predict_data = eval_data[1]
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": predict_data},
y=None,
batch_size=1,
num_epochs=1,
shuffle=False,
num_threads=1)
predict_results = mnist_classifier.predict(predict_input_fn)
print(predict_data)
for idx, prediction in enumerate(predict_results):
print(idx)
# print(prediction)
Any help getting this working would be greatly appreciated.
Update: I tried reshape as suggested below, but get the same error. The full trace is:
Traceback (most recent call last):
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 1323, in _do_call
return fn(*args)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 1302, in _run_fn
status, run_metadata)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 473, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 1 values, but the requested shape requires a multiple of 784
[[Node: Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](fifo_queue_DequeueUpTo/_53, Reshape/shape)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "D:\Workspace\eclipse\mnist_cnn\cnn_mnist.py", line 180, in <module>
tf.app.run()
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\platform\app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "D:\Workspace\eclipse\mnist_cnn\cnn_mnist.py", line 170, in main
for idx, prediction in enumerate(predict_results):
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\estimator\estimator.py", line 420, in predict
preds_evaluated = mon_sess.run(predictions)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\training\monitored_session.py", line 521, in run
run_metadata=run_metadata)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\training\monitored_session.py", line 892, in run
run_metadata=run_metadata)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\training\monitored_session.py", line 967, in run
raise six.reraise(*original_exc_info)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\six.py", line 693, in reraise
raise value
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\training\monitored_session.py", line 952, in run
return self._sess.run(*args, **kwargs)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\training\monitored_session.py", line 1024, in run
run_metadata=run_metadata)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\training\monitored_session.py", line 827, in run
return self._sess.run(*args, **kwargs)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 889, in run
run_metadata_ptr)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 1120, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 1317, in _do_run
options, run_metadata)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\client\session.py", line 1336, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Input to reshape is a tensor with 1 values, but the requested shape requires a multiple of 784
[[Node: Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](fifo_queue_DequeueUpTo/_53, Reshape/shape)]]
Caused by op 'Reshape', defined at:
File "D:\Workspace\eclipse\mnist_cnn\cnn_mnist.py", line 180, in <module>
tf.app.run()
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\platform\app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "D:\Workspace\eclipse\mnist_cnn\cnn_mnist.py", line 170, in main
for idx, prediction in enumerate(predict_results):
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\estimator\estimator.py", line 411, in predict
features, None, model_fn_lib.ModeKeys.PREDICT, self.config)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\estimator\estimator.py", line 694, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "D:\Workspace\eclipse\mnist_cnn\cnn_mnist.py", line 31, in cnn_model_fn
input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 3937, in reshape
"Reshape", tensor=tensor, shape=shape, name=name)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\framework\ops.py", line 2956, in create_op
op_def=op_def)
File "C:\Users\artma\Miniconda3\envs\vpilot\lib\site-packages\tensorflow\python\framework\ops.py", line 1470, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): Input to reshape is a tensor with 1 values, but the requested shape requires a multiple of 784
[[Node: Reshape = Reshape[T=DT_FLOAT, Tshape=DT_INT32, _device="/job:localhost/replica:0/task:0/device:GPU:0"](fifo_queue_DequeueUpTo/_53, Reshape/shape)]]
Update: Seems to have cracked it. Thanks to xdurch0 for putting me on the right track.
predict_data is just a 784-element vector. This will be treated as a dataset containing 784 elements (i.e. distinct inputs), each of which is a scalar. You need to reshape your predict_data to (1, 784) to let TF know this is a dataset with one element, which is a 784-element vector. E.g. predict_data[np.newaxis, :] or predict_data.reshape((1, 784)).
It seems the line:
predict_data = eval_data[1]
Was generating an np array of (784,), which even predict_data.reshape((1,784)) would not fix.
Whereas:
predict_data = eval_data[1:2]
Produces an np array of (1,784) which estimator.predict is now happy with.
I am implementing a convolutional autoencoder that has to reduce the dimension of camera images per image at real time. Therefore my batch size is one. Y_pred is the output of the network. I want to evaluate this so I can see what my network is outputting.
This is the code I use to import my data and feed the placeholders:
def inputs(image_file_path):
filenames = [image_file_path]
filename_queue = tf.train.string_input_producer(filenames)
read_input = read_image(filename_queue)
return read_input
with tf.Session() as sess:
image = inputs(file_path)
coord = tf.train.Coordinator()
init = tf.global_variables_initializer()
sess.run(init)
threads = tf.train.start_queue_runners(sess=sess, coord = coord)
image = tf.cast(image/255, tf.float32)
###data is just one image, so I set the dimension [batch, height, width, channels] to [1,120,160,3]
image = tf.reshape(image, [1, 120,160,3])
X_data = image.eval()
sess.run(train_step,{X_input:X_data, Y_true: X_data})
output = Y_pred
###Everything works fine up until this point.
print(Y_pred.eval())
coord.request_stop()
coord.join(threads)
Then the placeholders are here:
X_input = tf.placeholder(tf.float32, [None,120,160,3])
Y_true = tf.placeholder(tf.float32, [None,120,160,3])
The error I'm getting:
Traceback (most recent call last):
File "C:\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1039, in _do_call
return fn(*args)
File "C:\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1021, in _run_fn
status, run_metadata)
File "C:\Python35\lib\contextlib.py", line 66, in __exit__
next(self.gen)
File "C:\Python35\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\Julian\workspaceNeon\Scriptie\Autoencoder\__init__.py", line 164, in <module>
print(output.eval())
File "C:\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 569, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "C:\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 3741, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "C:\Python35\lib\site-packages\tensorflow\python\client\session.py", line 778, in run
run_metadata_ptr)
File "C:\Python35\lib\site-packages\tensorflow\python\client\session.py", line 982, in _run
feed_dict_string, options, run_metadata)
File "C:\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1032, in _do_run
target_list, options, run_metadata)
File "C:\Python35\lib\site-packages\tensorflow\python\client\session.py", line 1052, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Caused by op 'Placeholder', defined at:
File "C:\Users\Julian\workspaceNeon\Scriptie\Autoencoder\__init__.py", line 50, in <module>
X_input = tf.placeholder(tf.float32, [None,120,160,3])
File "C:\Python35\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1507, in placeholder
name=name)
File "C:\Python35\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 1997, in _placeholder
name=name)
File "C:\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 768, in apply_op
op_def=op_def)
File "C:\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 2336, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Python35\lib\site-packages\tensorflow\python\framework\ops.py", line 1228, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float
[[Node: Placeholder = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
tf.contrib.crf doesn't seem to support sequences of length 1.
For example, if I run the example on https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/crf (mirror) and replace num_words = 20 by num_words = 1:
import numpy as np
import tensorflow as tf
# Data settings.
num_examples = 10
num_words = 1
num_features = 100
num_tags = 5
# Random features.
x = np.random.rand(num_examples, num_words, num_features).astype(np.float32)
# Random tag indices representing the gold sequence.
y = np.random.randint(num_tags, size=[num_examples, num_words]).astype(np.int32)
# All sequences in this example have the same length, but they can be variable in a real model.
sequence_lengths = np.full(num_examples, num_words - 1, dtype=np.int32)
# Train and evaluate the model.
with tf.Graph().as_default():
with tf.Session() as session:
# Add the data to the TensorFlow graph.
x_t = tf.constant(x)
y_t = tf.constant(y)
sequence_lengths_t = tf.constant(sequence_lengths)
# Compute unary scores from a linear layer.
weights = tf.get_variable("weights", [num_features, num_tags])
matricized_x_t = tf.reshape(x_t, [-1, num_features])
matricized_unary_scores = tf.matmul(matricized_x_t, weights)
unary_scores = tf.reshape(matricized_unary_scores,
[num_examples, num_words, num_tags])
# Compute the log-likelihood of the gold sequences and keep the transition
# params for inference at test time.
log_likelihood, transition_params = tf.contrib.crf.crf_log_likelihood(
unary_scores, y_t, sequence_lengths_t)
# Add a training op to tune the parameters.
loss = tf.reduce_mean(-log_likelihood)
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
# Train for a fixed number of iterations.
session.run(tf.global_variables_initializer())
for i in range(1000):
tf_unary_scores, tf_transition_params, _ = session.run(
[unary_scores, transition_params, train_op])
if i % 100 == 0:
correct_labels = 0
total_labels = 0
for tf_unary_scores_, y_, sequence_length_ in zip(tf_unary_scores, y,
sequence_lengths):
# Remove padding from the scores and tag sequence.
tf_unary_scores_ = tf_unary_scores_[:sequence_length_]
y_ = y_[:sequence_length_]
# Compute the highest scoring sequence.
viterbi_sequence, _ = tf.contrib.crf.viterbi_decode(
tf_unary_scores_, tf_transition_params)
# Evaluate word-level accuracy.
correct_labels += np.sum(np.equal(viterbi_sequence, y_))
total_labels += sequence_length_
accuracy = 100.0 * correct_labels / float(total_labels)
print("Accuracy: %.2f%%" % accuracy)
I get the error message:
Traceback (most recent call last):
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\client\session.py", line 1022, in _do_call
return fn(*args)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\client\session.py", line 1004, in _run_fn
status, run_metadata)
File "C:\Anaconda\envs\py35\lib\contextlib.py", line 66, in __exit__
next(self.gen)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 469, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.UnimplementedError: TensorArray has size zero, but element shape <unknown> is not fully defined. Currently only static shapes are supported when packing zero-size TensorArrays.
[[Node: gradients/rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3_grad/TensorArrayGatherV3 = TensorArrayGatherV3[_class=["loc:#rnn/TensorArray_1"], dtype=DT_FLOAT, element_shape=<unknown>, _device="/job:localhost/replica:0/task:0/cpu:0"](gradients/rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3_grad/TensorArrayGrad/TensorArrayGradV3, rnn/TensorArrayUnstack/range, gradients/rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3_grad/TensorArrayGrad/gradient_flow)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\Francky\Documents\GitHub\nlp\neurodeid\test\CRF_v2.py", line 47, in <module>
[unary_scores, transition_params, train_op])
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\client\session.py", line 767, in run
run_metadata_ptr)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\client\session.py", line 965, in _run
feed_dict_string, options, run_metadata)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\client\session.py", line 1015, in _do_run
target_list, options, run_metadata)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\client\session.py", line 1035, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.UnimplementedError: TensorArray has size zero, but element shape <unknown> is not fully defined. Currently only static shapes are supported when packing zero-size TensorArrays.
[[Node: gradients/rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3_grad/TensorArrayGatherV3 = TensorArrayGatherV3[_class=["loc:#rnn/TensorArray_1"], dtype=DT_FLOAT, element_shape=<unknown>, _device="/job:localhost/replica:0/task:0/cpu:0"](gradients/rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3_grad/TensorArrayGrad/TensorArrayGradV3, rnn/TensorArrayUnstack/range, gradients/rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3_grad/TensorArrayGrad/gradient_flow)]]
Caused by op 'gradients/rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3_grad/TensorArrayGatherV3', defined at:
File "C:\Users\Francky\Documents\GitHub\nlp\neurodeid\test\CRF_v2.py", line 41, in <module>
train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\training\optimizer.py", line 288, in minimize
grad_loss=grad_loss)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\training\optimizer.py", line 354, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 482, in gradients
in_grads = grad_fn(op, *out_grads)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\ops\tensor_array_grad.py", line 186, in _TensorArrayScatterGrad
grad = g.gather(indices)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\ops\tensor_array_ops.py", line 348, in gather
element_shape=element_shape)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\ops\gen_data_flow_ops.py", line 2226, in _tensor_array_gather_v3
element_shape=element_shape, name=name)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 763, in apply_op
op_def=op_def)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\framework\ops.py", line 2395, in create_op
original_op=self._default_original_op, op_def=op_def)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\framework\ops.py", line 1264, in __init__
self._traceback = _extract_stack()
...which was originally created as op 'rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3', defined at:
File "C:\Users\Francky\Documents\GitHub\nlp\neurodeid\test\CRF_v2.py", line 37, in <module>
unary_scores, y_t, sequence_lengths_t)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\contrib\crf\python\ops\crf.py", line 156, in crf_log_likelihood
log_norm = crf_log_norm(inputs, sequence_lengths, transition_params)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\contrib\crf\python\ops\crf.py", line 123, in crf_log_norm
dtype=dtypes.float32)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\ops\rnn.py", line 545, in dynamic_rnn
dtype=dtype)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\ops\rnn.py", line 663, in _dynamic_rnn_loop
for ta, input_ in zip(input_ta, flat_input))
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\ops\rnn.py", line 663, in <genexpr>
for ta, input_ in zip(input_ta, flat_input))
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\ops\tensor_array_ops.py", line 400, in unstack
indices=math_ops.range(0, num_elements), value=value, name=name)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\ops\tensor_array_ops.py", line 428, in scatter
name=name)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\ops\gen_data_flow_ops.py", line 2492, in _tensor_array_scatter_v3
name=name)
File "C:\Anaconda\envs\py35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 763, in apply_op
op_def=op_def)
UnimplementedError (see above for traceback): TensorArray has size zero, but element shape <unknown> is not fully defined. Currently only static shapes are supported when packing zero-size TensorArrays.
[[Node: gradients/rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3_grad/TensorArrayGatherV3 = TensorArrayGatherV3[_class=["loc:#rnn/TensorArray_1"], dtype=DT_FLOAT, element_shape=<unknown>, _device="/job:localhost/replica:0/task:0/cpu:0"](gradients/rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3_grad/TensorArrayGrad/TensorArrayGradV3, rnn/TensorArrayUnstack/range, gradients/rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3_grad/TensorArrayGrad/gradient_flow)]]
num_words = 2 or num_words = 5 work. I see on
https://github.com/tensorflow/tensorflow/blob/e121667dc609de978a223c56ee906368d2c4ceef/tensorflow/contrib/crf/python/ops/crf.py#L121 (mirror) is already decrementing the sequence_length by 1:
# Compute the alpha values in the forward algorithm in order to get the
# partition function.
forward_cell = CrfForwardRnnCell(transition_params)
_, alphas = rnn.dynamic_rnn(
cell=forward_cell,
inputs=rest_of_input,
sequence_length=sequence_lengths - 1,
initial_state=first_input,
dtype=dtypes.float32)
log_norm = math_ops.reduce_logsumexp(alphas, [1])
return log_norm
However, changing sequence_lengths = np.full(num_examples, num_words - 1, dtype=np.int32) to sequence_lengths = np.full(num_examples, num_words, dtype=np.int32) does not solve the issue when num_words = 1 .
How to fix this issue so that the CRF layer supports sequences of length 1?
Tested with TensorFlow 1.0.0 on Windows 7 SP1 x64 Ultimate and TensorFlow-GPU 1.0.0 on Ubuntu 14.04.4 LTS x64. I created an issue in the TensorFlow repository but it got closed due to inactivity: https://github.com/tensorflow/tensorflow/issues/7751 (mirror)
I came across this so posting showing us how to setup the code to read in csv files using a queue. However, each time I run it, I run into an error. I've tried debugging it, but can't figure out what the error means. Can anyone help me out?
The code I'm using is almost verbatim what was posted in the above post:
import tensorflow as tf
dataset = '/Users/hdadmin/Data/actions/testing.csv'
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
def read_from_csv(filename_queue):
reader = tf.TextLineReader(skip_header_lines=1)
_, csv_row = reader.read(filename_queue)
record_defaults = [[0],[0],[0],[0],[0]]
colHour,colQuarter,colAction,colUser,colLabel = tf.decode_csv(csv_row, record_defaults=record_defaults)
features = tf.pack([colHour,colQuarter,colAction,colUser])
label = tf.pack([colLabel])
return features, label
def input_pipeline(batch_size, num_epochs=None):
filename_queue = tf.train.string_input_producer([dataset], num_epochs=num_epochs, shuffle=True)
example, label = read_from_csv(filename_queue)
min_after_dequeue = 1000
capacity = min_after_dequeue + 3 * batch_size
example_batch, label_batch = tf.train.shuffle_batch(
[example, label], batch_size=batch_size, capacity=capacity,
min_after_dequeue=min_after_dequeue)
return example_batch, label_batch
file_length = file_len(dataset) - 1
examples, labels = input_pipeline(file_length, 1)
with tf.Session() as sess:
tf.initialize_all_variables().run()
# start populating filename queue
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
try:
while not coord.should_stop():
example_batch, label_batch = sess.run([examples, labels])
print(example_batch)
except tf.errors.OutOfRangeError:
print('Done training, epoch reached')
finally:
coord.request_stop()
coord.join(threads)
The error I'm getting is:
E tensorflow/core/client/tensor_c_api.cc:485] Attempting to use uninitialized value input_producer/limit_epochs/epochs
[[Node: input_producer/limit_epochs/CountUpTo = CountUpTo[T=DT_INT64, _class=["loc:#input_producer/limit_epochs/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/cpu:0"](input_producer/limit_epochs/epochs)]]
E tensorflow/core/client/tensor_c_api.cc:485] RandomShuffleQueue '_2_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 10000, current size 0)
[[Node: shuffle_batch = QueueDequeueMany[_class=["loc:#shuffle_batch/random_shuffle_queue"], component_types=[DT_INT32, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](shuffle_batch/random_shuffle_queue, shuffle_batch/n)]]
Done training, epoch reached
E tensorflow/core/client/tensor_c_api.cc:485] FIFOQueue '_0_input_producer' is closed and has insufficient elements (requested 1, current size 0)
[[Node: ReaderRead = ReaderRead[_class=["loc:#TextLineReader", "loc:#input_producer"], _device="/job:localhost/replica:0/task:0/cpu:0"](TextLineReader, input_producer)]]
E tensorflow/core/client/tensor_c_api.cc:485] Queue '_2_shuffle_batch/random_shuffle_queue' is already closed.
[[Node: shuffle_batch/random_shuffle_queue_Close = QueueClose[_class=["loc:#shuffle_batch/random_shuffle_queue"], cancel_pending_enqueues=false, _device="/job:localhost/replica:0/task:0/cpu:0"](shuffle_batch/random_shuffle_queue)]]
Traceback (most recent call last):
File "csv_test.py", line 49, in <module>
coord.join(threads)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/coordinator.py", line 357, in join
six.reraise(*self._exc_info_to_raise)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/queue_runner.py", line 185, in _run
sess.run(enqueue_op)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 382, in run
run_metadata_ptr)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 655, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 723, in _do_run
target_list, options, run_metadata)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 743, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.FailedPreconditionError: Attempting to use uninitialized value input_producer/limit_epochs/epochs
[[Node: input_producer/limit_epochs/CountUpTo = CountUpTo[T=DT_INT64, _class=["loc:#input_producer/limit_epochs/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/cpu:0"](input_producer/limit_epochs/epochs)]]
Caused by op u'input_producer/limit_epochs/CountUpTo', defined at:
File "csv_test.py", line 31, in <module>
examples, labels = input_pipeline(file_length, 1)
File "csv_test.py", line 21, in input_pipeline
filename_queue = tf.train.string_input_producer([dataset], num_epochs=num_epochs, shuffle=True)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/input.py", line 194, in string_input_producer
summary_name="fraction_of_%d_full" % capacity)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/input.py", line 133, in input_producer
input_tensor = limit_epochs(input_tensor, num_epochs)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/input.py", line 84, in limit_epochs
counter = epochs.count_up_to(num_epochs)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/variables.py", line 577, in count_up_to
return state_ops.count_up_to(self._variable, limit=limit)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_state_ops.py", line 127, in count_up_to
result = _op_def_lib.apply_op("CountUpTo", ref=ref, limit=limit, name=name)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 703, in apply_op
op_def=op_def)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2310, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1232, in __init__
self._traceback = _extract_stack()
I made up data comprised of five columns to match with the example. It's something along the lines of:
"v1","v2","v3","v4","v5"
1,1,1,3,10
4,2,1,10,8
1,4,1,9,3
3,3,1,1,5
3,4,1,4,3
3,2,1,5,8
1,1,1,9,7
4,1,1,4,9
2,3,1,8,4
Thanks ahead of time.
I think what you are missing is initialization of local variables (e.g. input_producer/limit_epochs/epochs). Local variables are not initialized during initialization of all variables, which btw is quite confusing.
You can add the following initialization operation that will initialize everything at once:
init_op = tf.group(tf.initialize_all_variables(),
tf.initialize_local_variables())
and then:
sess.run(init_op)