9月

tensorflow 可视化

from __future__ import print_function
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):

    layer_name = 'layer%s' % n_layer
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            Weights = tf.Variable(tf.random_normal([in_size, out_size]))
            tf.summary.histogram(layer_name + '/weights', Weights)
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
            tf.summary.histogram(layer_name + '/biases', biases)
        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)
        tf.summary.histogram(layer_name + '/outputs', outputs)
    return outputs

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

# plt.scatter(x_data, y_data)
# plt.show()

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

l1 = add_layer(xs, 1, 10,n_layer=1, activation_function=tf.nn.relu)

prediction = add_layer(l1, 10, 1,n_layer=2, activation_function=None)

with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction), reduction_indices=[1]))
    tf.summary.scalar('loss', loss)
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess = tf.Session()

#
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("logs/", sess.graph)
init = tf.global_variables_initializer()

sess.run(init)

fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.scatter(x_data, y_data)
plt.ion()
plt.show()

for i in range(1000):
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})

    if i % 50 == 0:
        result = sess.run(merged, feed_dict={xs: x_data, ys: y_data})
        writer.add_summary(result, i)

通过tf.summary.histogram(layer_name + ‘/weights’, Weights)，tf.summary.scalar(‘loss’, loss)， writer.add_summary(result, i)

来监控数据。

tensorboard –logdir logs 来启动