如何将张量板可视化集成到 tf.Estimator？

Posted 2023-03-27

【中文标题】如何将张量板可视化集成到 tf.Estimator？

【英文标题】：How can I integrate tensorboard visualization to tf.Estimator?

【发布时间】：2019-07-08 12:46:17

【问题描述】：

我有用于识别手写数字 https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py 的经典 TensorFlow 代码，使用 tf.Estimator。我的问题很复杂，由两个问题组成

我是否应该在代码中为目标变量编写 tf.summary() 以在 Tensoboard 中可视化数据，只需键入 tensorboard -- logdir=/tmp/mnist_convnet_model 或 tf.Estimator 自动收集 */tmp/mnist_convnet_model 目录中的所有摘要，我可以调用 tensorboard -- logdir=/tmp/mnist_convnet_model ?

如果我必须写tf.summary()，你能回答我吗，我应该在代码中插入代码tf summary merge_all()吗？在哪一段代码中？

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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
  input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])

  # Convolutional Layer #1
  conv1 = tf.layers.conv2d(
      inputs=input_layer,
      filters=32,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)

  # Pooling Layer #1
  pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

  # Convolutional Layer #2
  conv2 = tf.layers.conv2d(
      inputs=pool1,
      filters=64,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)

  # Pooling Layer #2
  pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

  # Flatten tensor into a batch of vectors
  pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])

  # 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
  # Input Tensor Shape: [batch_size, 1024]
  # Output Tensor Shape: [batch_size, 10]
  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
  mnist = tf.contrib.learn.datasets.load_dataset("mnist")
  train_data = mnist.train.images  # Returns np.array
  train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
  eval_data = mnist.test.images  # Returns np.array
  eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

  # 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.compat.v1.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=20000,
      hooks=[logging_hook])

  # Evaluate the model and print results
  eval_input_fn = tf.compat.v1.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()

【参考方案1】：

通常，您只需在代码中的任意位置指定tf.summary.scalar()、tf.summary.histogram() 或tf.summary.image()。您可以通过以下方式使用直方图摘要来捕获所有权重和偏差

for value in tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES):
    tf.summary.histogram(value.name, value)

至于可更新的指标摘要，例如f1分数的准确性，你需要将其包裹在eval_metric_ops中并传递给tf.estimator.EstimatorSpec

accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions)
    eval_metric_ops = 'accuracy': accuracy

您可以使用您在训练期间指定的相同目录调用 tensorboard。 你不需要使用tf.summary.merge_all()