TensorBoard的最全使用教程：看这篇就够了开发者社区

TensorBoard的最全使用教程：看这篇就够了

pip install tensorboard

tensorboard --logdir=<directory_name>

Serving TensorBoard on localhost; to expose to the network, use a proxy or pass –bind_allTensorBoard 2.2.0 at http://localhost:6006/ (Press CTRL+C to quit)

%load_ext tensorboard

%tensorboard --logdir logs

%load_ext tensorboard

import tensorflow as tf
# Load and normalize MNIST data
mnist_data = tf.keras.datasets.mnist
(X_train, y_train), (X_test, y_test) = mnist_data.load_data()
X_train, X_test = X_train / 255.0, X_test / 255.0
# Define the model
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation='softmax')
# Compile the model
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

tf_callback = tf.keras.callbacks.TensorBoard(log_dir="./logs")

model.fit(X_train, y_train, epochs=5, callbacks=[tf_callback])

ssh -L 6006:127.0.0.1:6006 username@server_ip

tensorboard --logdir=’logs’ --port=6006

# Specify a directory for logging data
logdir = "./logs"
# Create a file writer to write data to our logdir
file_writer = tf.summary.create_file_writer(logdir)
# Loop from 0 to 199 and get the sine value of each number
for i in range(200):
    with file_writer.as_default():
        tf.summary.scalar('sine wave', np.math.sin(i), step=i)

# Load and normalize MNIST data
mnist_data = tf.keras.datasets.mnist
(X_train, y_train), (X_test, y_test) = mnist_data.load_data()
X_train, X_test = X_train / 255.0, X_test / 255.0

# Reshape the first image
img = np.reshape(X_train[0], (-1, 28, 28, 1))
# Specify a directory for logging data
logdir = "./logs"# Create a file writer to write data to our logdir
file_writer = tf.summary.create_file_writer(logdir)
# With the file writer, log the image data
with file_writer.as_default():
    tf.summary.image("Training data", img, step=0)

# Define the model
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Create a callback
tf_callback = tf.keras.callbacks.TensorBoard(log_dir="./logs")
# Pass in the callback when fitting the model
model.fit(X_train, y_train, epochs=5, callbacks=[tf_callback])

# Our sample text
sample_text = "Hello world!"
# Specify a directory for logging data
logdir = "./logs/text/"
# Create a file writer to write data to our logdir
file_writer = tf.summary.create_file_writer(logdir)
# With the file writer, log the text data
with file_writer.as_default():
    tf.summary.text("sample_text", sample_text, step=0)

from tensorboard.plugins import projector

import os
import tensorflow as tf
import tensorflow_datasets as tfds
# Load the data
(train_data, test_data), info = tfds.load(
    "imdb_reviews/subwords8k",
    split=(tfds.Split.TRAIN, tfds.Split.TEST),
    with_info=True,
    as_supervised=True,)encoder = info.features["text"].encoder
# Create training batches
train_batches = train_data.shuffle(1000).padded_batch(
    10, padded_shapes=((None,), ())
# Create testing batches
test_batches = test_data.shuffle(1000).padded_batch(
    10, padded_shapes=((None,), ())
# Get the first batch
train_batch, train_labels = next(iter(train_batches))

# Create an embedding layer
embedding_dim = 16
embedding = tf.keras.layers.Embedding(
    encoder.vocab_size, 
    embedding_dim)
# Configure the embedding layer as part of a Keras model
model = tf.keras.Sequential([
    embedding,
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(16, activation="relu"),
    tf.keras.layers.Dense(1),])
# Compile the model
model.compile(
    optimizer="adam",
    loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
    metrics=["accuracy"],
# Train the model for a single epoch
history = model.fit(
    train_batches, epochs=1, validation_data=test_batches
)

# Set up a log dir, just like in previous sections
log_dir='/logs/imdb-example/'
if not os.path.exists(log_dir):
    os.makedirs(log_dir)
# Save labels separately in a line-by-line manner.
with open(os.path.join(log_dir, 'metadata.tsv'), "w") as f:
    for subwords in encoder.subwords:
        f.write("{}\n".format(subwords))
# Fill in the rest of the labels with "unknown"
for unknown in range(1, encoder.vocab_size - len(encoder.subwords)):
    f.write("unknown #{}\n".format(unknown))
# Save the weights we want to analyze as a variable
weights = tf.Variable(model.layers[0].get_weights()[0][1:])
# Create a checkpoint from embedding, the filename and key are the
# name of the tensor
checkpoint = tf.train.Checkpoint(embedding=weights)
checkpoint.save(os.path.join(log_dir, "embedding.ckpt"))
# Set up config
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
# The name of the tensor will be suffixed by
# `/.ATTRIBUTES/VARIABLE_VALUE`
embedding.tensor_name = "embedding/.ATTRIBUTES/VARIABLE_VALUE"
embedding.metadata_path = 'metadata.tsv'
projector.visualize_embeddings(log_dir, config)

pip install  tensorboard_plugin_profile

# Create a callback
tf_callback = tf.keras.callbacks.TensorBoard(log_dir="./logs")
# Pass in the callback when fitting the model
model.fit(X_train, y_train, epochs=5, callbacks=[tf_callback])

pip install fairness_indicators
pip install tensorboard-plugin-fairness-indicators

from tensorboard_plugin_fairness_indicators import summary_v2

# Create a file writer to write data to our logdir
file_writer = tf.summary.create_file_writer(logdir)
# Write data to our result directory
with file_writer.as_default():
    summary_v2.FairnessIndicators(result_dir, step=1)

# Change the experiment id to our own
experiment_id = "insert experiment ID here"
# Get the experiment using the id
experiment = tb.data.experimental.ExperimentFromDev(experiment_id)
# Save the scalars to a dataframe
df = experiment.get_scalars()

# Import the summary writer
from torch.utils.tensorboard import SummaryWriter# Create an instance of the object
writer = SummaryWriter()

import torch
import torchvision
from torchvision import datasets, transforms
# Compose a set of transforms to use later on
transform = transforms.Compose([
    transforms.ToTensor(), 
    transforms.Normalize((0.5,), (0.5,))
# Load in the MNIST dataset
trainset = datasets.MNIST(
    'mnist_train', 
    train=True, 
    download=True, 
    transform=transform
# Create a data loader
trainloader = torch.utils.data.DataLoader(
    trainset, 
    batch_size=64, 
    shuffle=True
# Get a pre-trained ResNet18 model
model = torchvision.models.resnet18(False)
# Change the first layer to accept grayscale images
model.conv1 = torch.nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
# Get the first batch from the data loader
images, labels = next(iter(trainloader))
# Write the data to TensorBoard
grid = torchvision.utils.make_grid(images)

TensorBoard的最全使用教程：看这篇就够了

TensorBoard的最全使用教程：看这篇就够了

什么是 TensorBoard？

如何安装 TensorBoard

启动 TensorBoard

使用TensorBoard

TensorBoard 仪表板

TensorBoard 插件

使用 What-If 工具了解模型

将 TensorBoard 数据作为 DataFrame进行访问

将 TensorBoard 与 PyTorch 结合使用