这个错误InvalidArgumentError是什么（见上面的回溯）：预期的维度在[-1,1]范围内，但得到1均值？

Question

I'm getting an error when trying to run the following code:

correct = tf.equal(tf.argmax(activation,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct,'float'))
print ('Accuracy: ', sess.run(accuracy, feed_dict = {x: test_x, yL test_y})

The actual error is:

tensorflow.python.framework.errors_impl.InvalidArgumentError: Expected dimension in the range [-1, 1), but got 1
     [[Node: ArgMax_1 = ArgMax[T=DT_FLOAT, Tidx=DT_INT32, output_type=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_Placeholder_1_0_1, ArgMax_1/dimension)]]

The code is running a simple binary classification using logistic regression in TensorFlow.

I've been doing some research on the net but can't really find a satisfactory solution.

Thanks,

Answer 1

The problem is not in accuracy. Error clearly shows that problem is in argmax. please check your dimension of 'activation' and 'y' if anyone of them is of 1-D then remove the second operand of argmax and it will probably resolve your issue.

Answer 2

There is a full, running example on Github . Specifically, I was able to get the following code to run:

$ cat tf.py

from __future__ import print_function

import tensorflow as tf
assert tf.__version__ == '1.3.0'

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)

# Parameters
learning_rate = 0.01
training_epochs = 0
batch_size = 100
display_step = 1

# tf Graph Input
x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes

# Set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

# Construct model
pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax

# Minimize error using cross entropy
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))

# Gradient Descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()

# Test model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
sess = tf.InteractiveSession()

# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
sess.run(init)
print ('Accuracy: ', sess.run(accuracy, feed_dict = {x: mnist.test.images,
                                                     y: mnist.test.labels}))

$ python tf.py
Extracting /tmp/data/train-images-idx3-ubyte.gz
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
Accuracy:  0.098

This suggests that you might have an older version of Tensorflow. I would try installing 1.3.0 and seeing if that solves your problem.

Answer 3

In your code modify this

# Test model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

to this

# Test model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y))