keras中的tensorboard可视化

import tensorflow as tf
from keras.callbacks import Callback

class Mylosscallback(Callback):
    def __init__(self, log_dir):
        super(Callback, self).__init__()
        self.writer = tf.summary.FileWriter(log_dir)
        self.num = 0
    def on_train_begin(self, logs={}):
        self.losses = {'batch': [], 'epoch': []}
        self.accuracy = {'batch': [], 'epoch': []}
        self.val_loss = {'batch': [], 'epoch': []}
        self.val_acc = {'batch': [], 'epoch': []}
    def on_batch_end(self, batch, logs={}):
        self.num = self.num + 1
        self.losses = logs.get('loss')
        self.accuracy = logs.get('acc')
        self.val_loss = logs.get('val_loss')
        self.val_acc = logs.get('val_acc')
        print('debug success!!!')
        summary = tf.Summary()
        summary.value.add(tag='losses', simple_value=self.losses)
        summary.value.add(tag='accuracy', simple_value=self.accuracy)
        summary.value.add(tag='val_loss', simple_value=self.val_loss)
        summary.value.add(tag='val_acc', simple_value=self.val_acc)
        self.writer.add_summary(summary, self.num)
        self.writer.flush()

最后使用这个命令就可以使用tensorboard可视化自己想要的东西了，需要一个回调

model.fit(X_train, y_train, epochs=50, callbacks=[Mylosscallback(log_dir='./log')])