tensorboardScript.py

"""
    定义Tensorboard数据。
"""
import tensorflow as tf
import tensorboard


def input_visiable():
    with tf.name_scope('input'):
        x = tf.placeholder(tf.float32, [None, 784], name='x-input')
        y_ = tf.placeholder(tf.float32, [None, 10], name='y-input')

    with tf.name_scope('input_reshape'):
        image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
        tf.summary.image('input', image_shaped_input, 10)

def weight_variable(shape):
    """Create a weight variable with appropriate initialization."""
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    """Create a bias variable with appropriate initialization."""
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)
def variable_summaries(var):
    """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""

    with tf.name_scope('summaries'):
        # 计算参数的均值，并使用tf.summary.scaler记录
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)

        # 计算参数的标准差
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        # 使用tf.summary.scaler记录记录下标准差，最大值，最小值
        tf.summary.scalar('stddev', stddev)
        tf.summary.scalar('max', tf.reduce_max(var))
        tf.summary.scalar('min', tf.reduce_min(var))
        # 用直方图记录参数的分布
        tf.summary.histogram('histogram', var)

def accuracy_visiable(accuracy):
    with tf.name_scope('accuracy'):
        with tf.name_scope('correct_prediction'):
        # 分别将预测和真实的标签中取出最大值的索引，弱相同则返回1(true),不同则返回0(false)
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        with tf.name_scope('accuracy'):
        # 求均值即为准确率
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    tf.summary.scalar('accuracy', accuracy)

def summaries(session, log_dir = "./logs"):
    # summaries合并
    merged = tf.summary.merge_all()
    # 写到指定的磁盘路径中
    train_writer = tf.summary.FileWriter(log_dir + '/train', session.graph)
    test_writer = tf.summary.FileWriter(log_dir + '/test')

    # 运行初始化所有变量
    tf.global_variables_initializer().run()