TensorFlow TensorBoard 使用

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TensorBoard示例程序

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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

SUMMARY_DIR = "log"
BATCH_SIZE = 100
TRAIN_STEPS = 10000
HIDDEN1_SIZE = 500


def variable_summary(var, name):
    with tf.name_scope("summaries"):
        tf.summary.histogram(name, var)
        mean = tf.reduce_mean(var)
        tf.summary.scalar("mean/" + name, mean)
        stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar("stddev/" + name, stddev)


def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
    with tf.name_scope(layer_name):
        with tf.name_scope("weights"):
            weights = tf.Variable(tf.truncated_normal(
                [input_dim, output_dim], stddev=0.1))
            variable_summary(weights, layer_name + "/weights")

        with tf.name_scope("biases"):
            biases = tf.Variable(tf.constant(0.1, shape=[output_dim]))
            variable_summary(biases, layer_name + "/biases")

        with tf.name_scope("Wx_plus_b"):
            preactivate = tf.matmul(input_tensor, weights) + biases
            tf.summary.histogram(layer_name + "/pre_activation", preactivate)
            activate = act(preactivate, name="activation")
            tf.summary.histogram(layer_name + "/activation", activate)
    return activate


def main(_):
    mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
    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], name="x-input-reshpe")
        tf.summary.image("input", image_shaped_input)

    hidden1 = nn_layer(x, 784, HIDDEN1_SIZE, "layer1")
    y = nn_layer(hidden1, HIDDEN1_SIZE, 10, "layer2", act=tf.identity)

    with tf.name_scope("cross_entropy"):
        cross_entropy = tf.reduce_mean(
            tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
        tf.summary.scalar("cross entropy", cross_entropy)

    with tf.name_scope("train"):
        train_step = tf.train.AdamOptimizer().minimize(cross_entropy)

    with tf.name_scope("accuracy"):
        with tf.name_scope("correct_prediction"):
            correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
        with tf.name_scope("accuracy"):
            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        tf.summary.scalar("accuracy", accuracy)

    merged = tf.summary.merge_all()

    with tf.Session() as sess:
        summary_writer = tf.summary.FileWriter(SUMMARY_DIR, sess.graph)

        sess.run(tf.global_variables_initializer())

        for i in range(TRAIN_STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)
            summary, _ = sess.run([merged, train_step],
                                  feed_dict={x: xs, y_: ys})
            summary_writer.add_summary(summary, i)

    summary_writer.close()


if __name__ == '__main__':
    tf.app.run()