Python: DeprecationWarning: elementwise == 比较失败; 这将在未来引发错误

Python: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future

提问by Arslan Thobani

While trying out the udacity course deep learning assignment, I came across a problem with comparing the predictions of my model with the labels of training set

在尝试 udacity 课程深度学习作业时，我遇到了将模型的预测与训练集的标签进行比较的问题

from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range

pickle_file = 'notMNIST.pickle'

with open(pickle_file, 'rb') as f:
  save = pickle.load(f)
  train_dataset = save['train_dataset']
  train_labels = save['train_labels']
  valid_dataset = save['valid_dataset']
  valid_labels = save['valid_labels']
  test_dataset = save['test_dataset']
  test_labels = save['test_labels']
  del save  # hint to help gc free up memory
  print('Training set', train_dataset.shape, train_labels.shape)
  print('Validation set', valid_dataset.shape, valid_labels.shape)
  print('Test set', test_dataset.shape, test_labels.shape)

This gives output as:
Training set (200000, 28, 28) (200000,)
Validation set (10000, 28, 28) (10000,)
Test set (10000, 28, 28) (10000,)

输出如下：
训练集 (200000, 28, 28) (200000,)
验证集 (10000, 28, 28) (10000,)
测试集 (10000, 28, 28) (10000,)

# With gradient descent training, even this much data is prohibitive.
# Subset the training data for faster turnaround.
train_subset = 10000

graph = tf.Graph()
with graph.as_default():

  # Input data.
  # Load the training, validation and test data into constants that are
  # attached to the graph.
  tf_train_dataset = tf.constant(train_dataset[:train_subset, :])
  tf_train_labels = tf.constant(train_labels[:train_subset])
  tf_valid_dataset = tf.constant(valid_dataset)
  tf_test_dataset = tf.constant(test_dataset)

  # Variables.
  # These are the parameters that we are going to be training. The weight
  # matrix will be initialized using random values following a (truncated)
  # normal distribution. The biases get initialized to zero.
  weights = tf.Variable(
    tf.truncated_normal([image_size * image_size, num_labels]))
  biases = tf.Variable(tf.zeros([num_labels]))

  # Training computation.
  # We multiply the inputs with the weight matrix, and add biases. We compute
  # the softmax and cross-entropy (it's one operation in TensorFlow, because
  # it's very common, and it can be optimized). We take the average of this
  # cross-entropy across all training examples: that's our loss.
  logits = tf.matmul(tf_train_dataset, weights) + biases
  loss = tf.reduce_mean(
    tf.nn.softmax_cross_entropy_with_logits(labels=tf_train_labels, logits=logits))

  # Optimizer.
  # We are going to find the minimum of this loss using gradient descent.
  optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

  # Predictions for the training, validation, and test data.
  # These are not part of training, but merely here so that we can report
  # accuracy figures as we train.
  train_prediction = tf.nn.softmax(logits)
  valid_prediction = tf.nn.softmax(
    tf.matmul(tf_valid_dataset, weights) + biases)
  test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)


num_steps = 801

def accuracy(predictions, labels):
    return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
          / predictions.shape[0])


with tf.Session(graph=graph) as session:
  # This is a one-time operation which ensures the parameters get initialized as
  # we described in the graph: random weights for the matrix, zeros for the
  # biases. 
  tf.global_variables_initializer().run()
  print('Initialized')
  for step in range(num_steps):
    # Run the computations. We tell .run() that we want to run the optimizer,
    # and get the loss value and the training predictions returned as numpy
    # arrays.
    _, l, predictions = session.run([optimizer, loss, train_prediction])
    if (step % 100 == 0):
      print('Loss at step %d: %f' % (step, l))
      print('Training accuracy: %.1f%%' % accuracy(
        predictions, train_labels[:train_subset, :]))
      # Calling .eval() on valid_prediction is basically like calling run(), but
      # just to get that one numpy array. Note that it recomputes all its graph
      # dependencies.
      print('Validation accuracy: %.1f%%' % accuracy(
        valid_prediction.eval(), valid_labels))
  print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval(), test_labels))

it outputs:

它输出：

C:\Users\Arslan\Anaconda3\lib\site-packages\ipykernel_launcher.py:5: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future. """

C:\Users\Arslan\Anaconda3\lib\site-packages\ipykernel_launcher.py:5: DeprecationWarning: elementwise == 比较失败；这将在未来引发错误。"""

And it gives the accuracy as 0% for all datasets. I think we cannot compare the arrays using '=='.
Any help would be appreciated

它为所有数据集提供了 0% 的准确度。我认为我们不能使用“==”来比较数组。
任何帮助，将不胜感激

回答by hpaulj

I assume the error occurs in this expression:

我假设错误发生在这个表达式中：

np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))

can you tell us something about the 2 arrays, predictions, labels? The usual stuff - dtype, shape, some sample values. Maybe go the extra step and show the np.argmax(...)for each.

你能告诉我们一些关于2个阵列，predictions，labels？通常的东西 - dtype，形状，一些示例值。也许多走一步并显示np.argmax(...)每个。

In numpyyou can compare arrays of the same size, but it has become pickier about comparing arrays that don't match in size:

在numpy你可以比较相同大小的数组，但它已成为有关比较不中大小相匹配阵列挑剔：

In [522]: np.arange(10)==np.arange(5,15)
Out[522]: array([False, False, False, False, False, False, False, False, False, False], dtype=bool)
In [523]: np.arange(10)==np.arange(5,14)
/usr/local/bin/ipython3:1: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.
  #!/usr/bin/python3
Out[523]: False

回答by Christian Morbidoni

I solved this by upgrading python to 3.6.4 (latest)

我通过将 python 升级到 3.6.4（最新）解决了这个问题

conda update python