张量板中直方图的可视化

Question

在执行以下操作时

# Tensorflow board
log_dir="logs" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir,histogram_freq=1)

我得到以下信息

TypeError: Value passed to parameter 'values' has DataType bool not in list of allowed values: float32, float64, int32, uint8, int16, int8, int64, bfloat16, uint16, float16, uint32, uint64

当我删除histogram_freq=1时，它修复了这个问题。 有没有办法将histogram_freq=1可视化？没有抛出那个错误

Answer 1

histogram_freq = 1在每个历元启用Visualization个Histogram计算

由于问题中没有完整的代码，请提及完整的示例代码，其中Weights和Biases用histogram_freq = 1可视化

# Load the TensorBoard notebook extension
%load_ext tensorboard

import tensorflow as tf
import datetime

# Clear any logs from previous runs
!rm -rf ./logs/ 

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

def create_model():
  return tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
  ])

model = create_model()
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")

tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

model.fit(x=x_train, 
          y=y_train, 
          epochs=5, 
          validation_data=(x_test, y_test), 
          callbacks=[tensorboard_callback])

%tensorboard  logdir logs/fit

带有histogram_freq = 1的权重和偏差直方图如下所示：

有关更多信息，请参阅此Tutorial on Tensorboard

请让我知道，如果你面临任何其他错误，连同完整的可复制代码，我会很高兴地帮助你

希望这有帮助。学习愉快