>

Tensorflow 예 를 기반으로 코드를 작성했습니다.

   def variable_summaries(var):
  """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
  with tf.name_scope('summaries'):
    mean = tf.reduce_mean(var)
    tf.summary.scalar('mean', mean)
    with tf.name_scope('stddev'):
      stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
    tf.summary.scalar('stddev', stddev)
    tf.summary.scalar('max', tf.reduce_max(var))
    tf.summary.scalar('min', tf.reduce_min(var))
    tf.summary.histogram('histogram', var)
def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
  """Reusable code for making a simple neural net layer.
  It does a matrix multiply, bias add, and then uses relu to nonlinearize.
  It also sets up name scoping so that the resultant graph is easy to read,
  and adds a number of summary ops.
  """
  # Adding a name scope ensures logical grouping of the layers in the graph.
  with tf.name_scope(layer_name):
    # This Variable will hold the state of the weights for the layer
    with tf.name_scope('weights'):
      weights = weight_variable([input_dim, output_dim])
      variable_summaries(weights)
    with tf.name_scope('biases'):
      biases = bias_variable([output_dim])
      variable_summaries(biases)
    with tf.name_scope('Wx_plus_b'):
      preactivate = tf.matmul(input_tensor, weights) + biases
      tf.summary.histogram('pre_activations', preactivate)
    activations = act(preactivate, name='activation')
    tf.summary.histogram('activations', activations)
    return activations
hidden1 = nn_layer(x, 784, 500, 'layer1')
with tf.name_scope('dropout'):
  keep_prob = tf.placeholder(tf.float32)
  tf.summary.scalar('dropout_keep_probability', keep_prob)
  dropped = tf.nn.dropout(hidden1, keep_prob)
# Do not apply softmax activation yet, see below.
y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)
with tf.name_scope('cross_entropy'):
  # The raw formulation of cross-entropy,
  #
  # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),
  #                               reduction_indices=[1]))
  #
  # can be numerically unstable.
  #
  # So here we use tf.nn.softmax_cross_entropy_with_logits on the
  # raw outputs of the nn_layer above, and then average across
  # the batch.
  diff = tf.nn.softmax_cross_entropy_with_logits(targets=y_, logits=y)
  with tf.name_scope('total'):
    cross_entropy = tf.reduce_mean(diff)
tf.summary.scalar('cross_entropy', cross_entropy)
with tf.name_scope('train'):
  train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
      cross_entropy)
with tf.name_scope('accuracy'):
  with tf.name_scope('correct_prediction'):
    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
  with tf.name_scope('accuracy'):
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',
                                      sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/test')
tf.global_variables_initializer().run()

교육 및 테스트 중에 정확도 변수를 부동 소수점으로 얻고 싶습니다. 여기서 내가 한 일이 있습니다.

..........................................................
for i in range(FLAGS.max_steps):
  if i % 10 == 0:  # Record summaries and test-set accuracy
    summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
    test_writer.add_summary(summary, i)
    print('Accuracy at step %s: %s' % (i, acc))
  else:  # Record train set summaries, and train
    summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
    train_writer.add_summary(summary, i)
    print("accuracy  ", accuracy.eval) ## Here's the problem 

마지막 인쇄의 결과물

<bound method Tensor.eval of <tf.Tensor 'cross_entropy/accuracy/accuracy/Mean:0' shape=() dtype=float32>>

제 질문은 시험 단계와 같은 방법으로 훈련 중에 정확도 값을 어떻게 얻을 수 있습니까?


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