keras使用回调函数 Tesorboard可视化

首先创建log的目录my_dir，在cmd下执行 tensorboard --logdir=my_dir,对模型进行训练，同时打开浏览器，输入 localhost:6006 ,就可以看到模型训练过程的一些实时信息。

代码如下：

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

max_features = 2000
max_len = 500
(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)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)
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.GlobalMaxPool1D())
model.add(layers.Dense(1))
print(model.summary())
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['acc'])
tcallbacks = [
        keras.callbacks.TensorBoard(
            log_dir='./logs_dir',
            histogram_freq=1,
            embeddings_freq=1,
            embeddings_data=x_train[:1000].astype("float32")
        )
]
history = model.fit(
        x_train, y_train,
        epochs=20,
        batch_size=128,
        validation_split=0.2,
        callbacks=tcallbacks
)

tensorboard界面展示如下：