我升级到Tensorflow 2.0,没有
tf.summary.FileWriter("tf_graphs", sess.graph)
。我正在查看有关此问题的其他一些 StackOverflow 问题,他们说使用 tf.compat.v1.summary etc
。当然,一定有一种方法可以在 Tensorflow 版本 2 中对 tf.keras 模型进行图形化和可视化。它是什么?我正在寻找如下所示的张量板输出。谢谢!
您可以可视化任何
tf.function
修饰函数的图形,但首先,您必须跟踪其执行情况。
可视化 Keras 模型的图形意味着可视化它的
call
方法。
默认情况下,此方法未经过
tf.function
修饰,因此您必须将模型调用包装在正确修饰的函数中并执行它。
import tensorflow as tf
model = tf.keras.Sequential(
[
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(32, activation="relu"),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation="softmax"),
]
)
@tf.function
def traceme(x):
return model(x)
logdir = "log"
writer = tf.summary.create_file_writer(logdir)
tf.summary.trace_on(graph=True, profiler=True)
# Forward pass
traceme(tf.zeros((1, 28, 28, 1)))
with writer.as_default():
tf.summary.trace_export(name="model_trace", step=0, profiler_outdir=logdir)
根据 docs,一旦模型训练完成,您就可以使用 Tensorboard 来可视化图形。
首先,定义模型并运行它。然后,打开 Tensorboard 并切换到 Graph 选项卡。
最小可编译示例
这个例子取自文档。首先,定义您的模型和数据。
# Relevant imports.
%load_ext tensorboard
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
from packaging import version
import tensorflow as tf
from tensorflow import keras
# Define the model.
model = keras.models.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(32, activation='relu'),
keras.layers.Dropout(0.2),
keras.layers.Dense(10, activation='softmax')
])
model.compile(
optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
(train_images, train_labels), _ = keras.datasets.fashion_mnist.load_data()
train_images = train_images / 255.0
接下来,训练你的模型。在这里,您需要为 Tensorboard 定义回调以用于可视化统计数据和图表。
# Define the Keras TensorBoard callback.
logdir="logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
# Train the model.
model.fit(
train_images,
train_labels,
batch_size=64,
epochs=5,
callbacks=[tensorboard_callback])
训练后,在笔记本中运行
%tensorboard --logdir logs
并切换到导航栏中的“图表”选项卡:
您将看到一个看起来很像这样的图表:
这是tf2.x的解决方案,具有子类模型/层的图形可视化
import tensorflow as tf
print("TensorFlow version:", tf.__version__)
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model,Input
class MyModel(Model):
def __init__(self, dim):
super(MyModel, self).__init__()
self.conv1 = Conv2D(16, 3, activation='relu')
self.conv2 = Conv2D(32, 3, activation='relu')
self.conv3 = Conv2D(8, 3, activation='relu')
self.flatten = Flatten()
self.d1 = Dense(128, activation='relu')
self.d2 = Dense(1)
def call(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.flatten(x)
x = self.d1(x)
return self.d2(x)
def build_graph(self):
x = Input(shape=(dim))
return Model(inputs=[x], outputs=self.call(x))
dim = (28, 28, 1)
# Create an instance of the model
model = MyModel((dim))
model.build((None, *dim))
model.build_graph().summary()
tf.keras.utils.plot_model(model.build_graph(), to_file="model.png",
expand_nested=True, show_shapes=True)
输出是
TensorFlow version: 2.5.0
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
conv2d (Conv2D) (None, 26, 26, 16) 160
_________________________________________________________________
conv2d_1 (Conv2D) (None, 24, 24, 32) 4640
_________________________________________________________________
conv2d_2 (Conv2D) (None, 22, 22, 8) 2312
_________________________________________________________________
flatten (Flatten) (None, 3872) 0
_________________________________________________________________
dense (Dense) (None, 128) 495744
_________________________________________________________________
dense_1 (Dense) (None, 1) 129
=================================================================
Total params: 502,985
Trainable params: 502,985
Non-trainable params: 0
这也是一个图形可视化
tf.keras.callbacks.TensorBoard 代码:
# After model has been compiled
from tensorflow.python.ops import summary_ops_v2
from tensorflow.python.keras.backend import get_graph
tb_path = '/tmp/tensorboard/'
tb_writer = tf.summary.create_file_writer(tb_path)
with tb_writer.as_default():
if not model.run_eagerly:
summary_ops_v2.graph(get_graph(), step=0)