Tensorflow: InvalidArgumentError mit MNIST, [55000] gegen [10000]

Question

Ich bin von dieser Darstellung durch den Code arbeiten http://www.youtube.com/watch?v=vq2nnJ4g6N0&t=20m28s und bin immer diese Fehlermeldung: InvalidArgumentError (see above for traceback): Incompatible shapes: [55000] vs. [10000]

Ich habe schon durch ein paar Fehler gearbeitet Tensor in Bezug auf Form/Dimensionen, aber keine Ahnung wie man das konkret versteht oder gar korrigieren kann.

Ich bin neu zu tf und jeder Beratung ist sehr willkommen, hier ist der Code:

# 1 ~ import tf + data 
import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) 

#2 ~ paras + init 
X = tf.placeholder(tf.float32, [None, 784]) 
W = tf.Variable(tf.zeros([784, 10])) 
b = tf.Variable(tf.zeros([10])) 

init = tf.initialize_all_variables() 

#3 ~ model + correct answers 
Y = tf.nn.softmax(tf.matmul(X, W) + b) 
Y_ = tf.placeholder(tf.float32, [None, 10]) # one-hot encoding 

#4 ~ loss function 
cross_entropy = -tf.reduce_sum(Y_ * tf.log(Y)) 

#5 ~ correct answer + % accuracy 
is_correct = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1)) # one-hot decoding 
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32)) 

#6 ~ optimizer and training step 
optimizer = tf.train.GradientDescentOptimizer(0.003) # learning-rate 
train_step = optimizer.minimize(cross_entropy) 

#7 ~ session and training loop 
sess = tf.Session() 
sess.run(init) 

for i in range(1000): 
    # load a batch of images and correct answers 
    batch_X, batch_Y = mnist.train.next_batch(100) 
    train_data = {X: batch_X, Y_: batch_Y} 

    # train 
    sess.run(train_step, feed_dict=train_data) 

    # success? 
    a,c = sess.run([accuracy, cross_entropy], feed_dict=train_data) 

    # success on test data? 
    test_data = {X: mnist.train.images, Y_: mnist.test.labels} 
    a,c = sess.run([accuracy, cross_entropy], feed_dict=test_data) 

Die Gesamtfehlerausgabe ist:

Traceback (most recent call last): 
     File "/Users/joelmcleod/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1021, in _do_call 
    return fn(*args) 
    File "/Users/joelmcleod/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1003, in _run_fn 
    status, run_metadata) 
    File "/Users/joelmcleod/anaconda3/lib/python3.5/contextlib.py", line 66, in __exit__ 
    next(self.gen) 
    File "/Users/joelmcleod/anaconda3/lib/python3.5/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.InvalidArgumentError: Incompatible shapes: [55000] vs. [10000] 
    [[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]] 

During handling of the above exception, another exception occurred: 

Traceback (most recent call last): 
    File "deep_nn1.py", line 71, in <module> 
    a,c = sess.run([accuracy, cross_entropy], feed_dict=test_data) 
    File "/Users/joelmcleod/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 766, in run 
    run_metadata_ptr) 
    File "/Users/joelmcleod/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 964, in _run 
    feed_dict_string, options, run_metadata) 
    File "/Users/joelmcleod/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1014, in _do_run 
    target_list, options, run_metadata) 
    File "/Users/joelmcleod/anaconda3/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1034, in _do_call 
    raise type(e)(node_def, op, message) 
tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [55000] vs. [10000] 
    [[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]] 

Caused by op 'Equal', defined at: 
    File "deep_nn1.py", line 47, in <module> 
    is_correct = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1)) # one-hot decoding 
    File "/Users/joelmcleod/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/gen_math_ops.py", line 728, in equal 
    result = _op_def_lib.apply_op("Equal", x=x, y=y, name=name) 
    File "/Users/joelmcleod/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op 
    op_def=op_def) 
    File "/Users/joelmcleod/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2240, in create_op 
    original_op=self._default_original_op, op_def=op_def) 
    File "/Users/joelmcleod/anaconda3/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1128, in __init__ 
    self._traceback = _extract_stack() 

InvalidArgumentError (see above for traceback): Incompatible shapes: [55000] vs. [10000] 
    [[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]] 

Answer 1

Sie füttern Trainingsdaten (Anzahl der Reihen: 55.000) anstelle von Testdaten (Anzahl der Zeilen: 10.000) in dieser Zeile:

test_data = {X: mnist.train.images, Y_: mnist.test.labels} 

einfach befestigen sie ihn mit:

test_data = {X: mnist.test.images, Y_: mnist.test.labels}