tensorboard的可视化实例

最新推荐文章于 2025-02-01 13:06:06 发布

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最新推荐文章于 2025-02-01 13:06:06 发布

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分类专栏： Tensorflow教程

本文链接：https://blog.youkuaiyun.com/LYKymy/article/details/90290329

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代码

import tensorflow as tf
import numpy as np

def add_layer(inputs, in_size, out_size, activation_function=None):
    with tf.name_scope("layer"):
        with tf.name_scope("weight"):
            Weights = tf.Variable(tf.random_normal([in_size, out_size]))
        with tf.name_scope("biases"):
            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
        with tf.name_scope("Wx_plus_b"):
            Wx_plus_b = tf.matmul(inputs, Weights) + biases
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        return outputs
        


# Make up some real data
x_data = np.linspace(-1,1,300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = np.square(x_data) - 0.5 + noise

with tf.name_scope("input"):
    xs = tf.placeholder(tf.float32, [None, 1], name='x_input')
    ys = tf.placeholder(tf.float32, [None, 1], name='y_input')

layer1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
prediction = add_layer(layer1, 10, 1, activation_function = None)
with tf.name_scope("loss"):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))
with tf.name_scope("train"):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess = tf.Session()
writer = tf.summary.FileWriter("logs/", sess.graph)

init = tf.global_variables_initializer()
sess.run(init)

命令

tensorboard --logdir=logs