在Jupyter中可视化TensorFlow图的简单方法? [英] Simple way to visualize a TensorFlow graph in Jupyter?

问题描述

可视化TensorFlow图的官方方法是使用TensorBoard，但有时我只是想在Jupyter中工作时快速浏览一下图.

The official way to visualize a TensorFlow graph is with TensorBoard, but sometimes I just want a quick look at the graph when I'm working in Jupyter.

是否有一个快速的解决方案，理想情况下基于TensorFlow工具或标准SciPy软件包(如matplotlib)，但必要时基于第三方库?

Is there a quick solution, ideally based on TensorFlow tools, or standard SciPy packages (like matplotlib), but if necessary based on 3rd party libraries?

推荐答案

TensorFlow 2.0现在通过魔术命令(例如%tensorboard --logdir logs/train)支持TensorBoard in Jupyter.这是链接到教程和示例.

TensorFlow 2.0 now supportsTensorBoardinJupytervia magic commands (e.g %tensorboard --logdir logs/train). Here's a link to tutorials and examples.

正如@MiniQuark在评论中提到的，我们需要先加载扩展名(%load_ext tensorboard.notebook).

As @MiniQuark mentioned in a comment, we need to load the extension first(%load_ext tensorboard.notebook).

以下是使用图形模式， @tf.function 和 tf.keras (在tensorflow==2.0.0-alpha0中)的用法示例:

Below are usage examples for using graph mode, @tf.function and tf.keras (in tensorflow==2.0.0-alpha0):

%load_ext tensorboard.notebook
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
from tensorflow.python.ops.array_ops import placeholder
from tensorflow.python.training.gradient_descent import GradientDescentOptimizer
from tensorflow.python.summary.writer.writer import FileWriter

with tf.name_scope('inputs'):
   x = placeholder(tf.float32, shape=[None, 2], name='x')
   y = placeholder(tf.int32, shape=[None], name='y')

with tf.name_scope('logits'):
   layer = tf.keras.layers.Dense(units=2)
   logits = layer(x)

with tf.name_scope('loss'):
   xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
   loss_op = tf.reduce_mean(xentropy)

with tf.name_scope('optimizer'):
   optimizer = GradientDescentOptimizer(0.01)
   train_op = optimizer.minimize(loss_op)

FileWriter('logs/train', graph=train_op.graph).close()
%tensorboard --logdir logs/train

2.与上述示例相同，但现在使用 @tf.function 装饰器进行前后传递，并且不禁用急切执行:

%load_ext tensorboard.notebook
import tensorflow as tf
import numpy as np

logdir = 'logs/'
writer = tf.summary.create_file_writer(logdir)
tf.summary.trace_on(graph=True, profiler=True)

@tf.function
def forward_and_backward(x, y, w, b, lr=tf.constant(0.01)):

    with tf.name_scope('logits'):
        logits = tf.matmul(x, w) + b

    with tf.name_scope('loss'):
        loss_fn = tf.nn.sparse_softmax_cross_entropy_with_logits(
            labels=y, logits=logits)
        reduced = tf.reduce_sum(loss_fn)

    with tf.name_scope('optimizer'):
        grads = tf.gradients(reduced, [w, b])
        _ = [x.assign(x - g*lr) for g, x in zip(grads, [w, b])]
    return reduced

# inputs
x = tf.convert_to_tensor(np.ones([1, 2]), dtype=tf.float32)
y = tf.convert_to_tensor(np.array([1]))
# params
w = tf.Variable(tf.random.normal([2, 2]), dtype=tf.float32)
b = tf.Variable(tf.zeros([1, 2]), dtype=tf.float32)

loss_val = forward_and_backward(x, y, w, b)

with writer.as_default():
    tf.summary.trace_export(
        name='NN',
        step=0,
        profiler_outdir=logdir)

%tensorboard --logdir logs/

3.使用tf.keras API:

3. Using tf.keras API:

%load_ext tensorboard.notebook
import tensorflow as tf
import numpy as np
x_train = [np.ones((1, 2))]
y_train = [np.ones(1)]

model = tf.keras.models.Sequential([tf.keras.layers.Dense(2, input_shape=(2, ))])

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

logdir = "logs/"

tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)

model.fit(x_train,
          y_train,
          batch_size=1,
          epochs=1,
          callbacks=[tensorboard_callback])

%tensorboard --logdir logs/

这些示例将在单元格下方生成如下内容:

These examples will produce something like this below the cell:

这篇关于在Jupyter中可视化TensorFlow图的简单方法?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！