10分钟搞懂TensorBoard用法

with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    file_writer = tf.summary.FileWriter('./logs/1', sess.graph)

tensorboard --logdir=./logs/1

 with tf.name_scope("RNN_layers"):        lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)        drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)        cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)with tf.name_scope("RNN_layers")

 with tf.name_scope('logits'):        softmax_w = tf.Variable(tf.truncated_normal((lstm_size, num_classes), stddev=0.1),                               name='softmax_w')        softmax_b = tf.Variable(tf.zeros(num_classes), name='softmax_b')        logits = tf.matmul(output, softmax_w) + softmax_b        tf.summary.histogram('softmax_w', softmax_w)        tf.summary.histogram('softmax_b', softmax_b) #以直方图采集权重 ....merged = tf.summary.merge_all() #收集全部采集点.....with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    train_writer = tf.summary.FileWriter('./logs/2/train', sess.graph)    ....    summary, batch_loss, new_state, _ = sess.run([model.merged, model.cost,                                                           model.final_state, model.optimizer],                                                           feed_dict=feed) #运行采集点的收集器merge    ....    train_writer.add_summary(summary, iteration) #采集到的信息写入文件

epochs = 20batch_size = 100num_steps = 100train_x, train_y, val_x, val_y = split_data(chars, batch_size, num_steps)for lstm_size in [128,256,512]:    for num_layers in [1, 2]:        for learning_rate in [0.002, 0.001]:            log_string = 'logs/4/lr={},rl={},ru={}'.format(learning_rate, num_layers, lstm_size) #每一对参数写入一个文件            writer = tf.summary.FileWriter(log_string)            model = build_rnn(len(vocab),                     batch_size=batch_size,                    num_steps=num_steps,                    learning_rate=learning_rate,                    lstm_size=lstm_size,                    num_layers=num_layers)            train(model, epochs, writer)#每个文件用采集器收集信息