"Python Deep Learning" 7.2.2 Introduction to TensorBoard (code)

1. Using TensorBoard's text classification model

import keras
from keras import layers
from keras.datasets import imdb
from keras.preprocessing import sequence

max_features = 2000 #作为特征的单词个数
max_len = 500 #在这么多单词之后截断文本

(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
x_train = sequence.pad_sequences(x_train, maxlen=max_len)
print(x_train.shape)    #(25000, 500)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)
print(x_test.shape) 

(25000, 500)

(25000, 500)

model = keras.models.Sequential()
model.add(layers.Embedding(max_features, 128, input_length=max_len, name='embed'))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.MaxPooling1D(5))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(1))
model.summary()
#由于电脑内存过小，无法写入25000行的数据，因此取前1000行
print(x_train[:1000].shape)   #（1000， 500）

model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])

 2. Before using TensorBoard, you first need to create a directory to save the generated log files, which can be created manually or using terminal commands.

$ mkdir my_log_dir

3. Use a TensorBoard callback function to train the model

callbacks = [
    keras.callbacks.TensorBoard(
        log_dir='my_log_dir',  #将日志写入这个位置
        histogram_freq=1,  #每一轮之后记录激活的直方图
        embeddings_freq=1,  #每一轮之后记录嵌入数据
        embeddings_data=x_train[:1000].astype('float32')  #指定embeddings_data的值
    )
]
history = model.fit(x_train, y_train,
                    epochs=20,
                    batch_size=128,
                    validation_split=0.2,
                    callbacks=callbacks
                   )

4. Start the TensorBoard server on the command line cmd , instructing it to read the log currently being written by the callback function

tensorboard --logdir "D:\Deep Learning with Python\Code\my_log_dir"

 Open the given localhost address in the browser, you can view:

 

 5. Use the keras.utils.plot_model function to draw the model as a graph composed of layers 

Note that Keras also provides another more concise method - the keras.utils.plot_model function, which can draw the model as a graph composed of layers instead of a graph composed of Tensorflow operations. To use this function, you need to install Python's pydot library and pydot-ng library, and you also need to install the graphviz library. (Installation tutorial: keras visualization pydot and graphviz installation )

Let's take a quick look:

from keras.utils import plot_model
plot_model(model,to_file='model.png')

from keras.utils import plot_model
plot_model(model,show_shapes=True,to_file='model.png')

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2021-11-23 Tues. 15：50 p.m.