Visualizing histogram_freq in Tensorboard

Question

When executing the following

# 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)

I get the following

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

When I remove histogram_freq=1, it fixes the issue. Is there way to visualize histogram_freq=1 ? without that error being thrown ?

Answer 1

histogram_freq = 1 enables Visualization of Histogram computation every epoch.

Since complete code is not present in the question, mentioning the Complete Sample Code in which Weights and Biases are Visualized with histogram_freq = 1.

# 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

The Histogram of Weights and Biases with histogram_freq = 1 is shown below:

For more information, please refer this Tutorial on Tensorboard.

Please let me know if you face any other error, along with complete reproducible code and I will be Happy to help you.

Hope this helps. Happy Learning!