Logging 尝试在张力板上记录CNN张力流的精度

我修改了用于图像分类的CNN tensorflow模型，以包含更多卷积层。它工作得很好。我想用张力板每50步记录一次模型的精度。我一直在尝试添加FileWriter，但没有成功。你能帮我做这件事吗

谢谢 这是我的密码：

"""Convolutional Neural Network Estimator for MNIST, built with tf.layers."""
import numpy as np
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.INFO)


def cnn_model_fn(features, labels, mode):
  """Model function for CNN."""
  input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])

  # Convolutional Layer #1
  conv1 = tf.layers.conv2d(...)

  # Pooling Layer #1
  pool1 = tf.layers.max_pooling2d(...)


  # Flatten tensor into a batch of vectors
  pool2_flat = tf.reshape(...)

  # Dense Layer
  dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)

  # Add dropout operation; 0.6 probability that element will be kept
  dropout = tf.layers.dropout(
      inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)

  # Logits layer
  logits = tf.layers.dense(inputs=dropout, units=10)

  predictions = {
      # Generate predictions (for PREDICT and EVAL mode)
      "classes": tf.argmax(input=logits, axis=1),
      # Add `softmax_tensor` to the graph. It is used for PREDICT and by the
      # `logging_hook`.
      "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
  }
  if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

  # Calculate Loss (for both TRAIN and EVAL modes)
  loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)

  # Configure the Training Op (for TRAIN mode)
  if mode == tf.estimator.ModeKeys.TRAIN:
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
    train_op = optimizer.minimize(
        loss=loss,
        global_step=tf.train.get_global_step())
    return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)

  # Add evaluation metrics (for EVAL mode)
  eval_metric_ops = {
      "accuracy": tf.metrics.accuracy(
          labels=labels, predictions=predictions["classes"])}
  return tf.estimator.EstimatorSpec(
      mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

def main(unused_argv):
  # Load training and eval data
  .
  .
  .

  # 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=2000,
      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)


if __name__ == "__main__":
  tf.app.run()

---

对于使用

tf.estimator

进行培训，您实际上不需要使用文件编写器编写摘要。它将合并所有摘要并每隔几步保存它们。默认情况下，它是每100步一次。要记录培训期间的准确性，您只需定义培训模式的摘要操作，如下所示：

accuracy = tf.metrics.accuracy(
    labels=labels, predictions=predictions["classes"])
tf.summary.scalar('accuracy', accuracy[1])

mnist_classifier = tf.estimator.Estimator(
    model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model",
    config=tf.estimator.RunConfig(save_summary_steps=50))

如果希望每50次执行一次，则需要在estimator实例化期间通过传递

config

来更改默认行为，如下所示：

accuracy = tf.metrics.accuracy(
    labels=labels, predictions=predictions["classes"])
tf.summary.scalar('accuracy', accuracy[1])

mnist_classifier = tf.estimator.Estimator(
    model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model",
    config=tf.estimator.RunConfig(save_summary_steps=50))

---

现在，您应该可以使用张力板每50步查看一次模型的准确性。

谢谢，伙计，帮了大忙。@BehroozMoradiBajestani欢迎您。感谢您的投票。为什么您要使用

准确性[1]

？在本文中，精确性是返回元组的第一个元素