TF之DNN:对DNN神经网络进行Tensorboard可视化(得到events.out.tfevents本地服务器输出到网页可视化)
目录
- import tensorflow as tf
- import numpy as np
-
-
- def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):
- add one more layer and return the output of this layer
- layer_name = 'layer%s' % n_layer
- with tf.name_scope(layer_name):
- with tf.name_scope('Jason_niu_weights'):
- Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')
- tf.summary.histogram(layer_name + '/weights', Weights)
- with tf.name_scope('Jason_niu_biases'):
- biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
- tf.summary.histogram(layer_name + '/biases', biases)
- with tf.name_scope('Jason_niu_Wx_plus_b'):
- Wx_plus_b = tf.add(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
-
-
- 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
-
- define placeholder for inputs to network
- with tf.name_scope('Jason_niu_inputs'):
- xs = tf.placeholder(tf.float32, [None, 1], name='x_input')
- ys = tf.placeholder(tf.float32, [None, 1], name='y_input')
-
- add hidden layer
- l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)
- add output layer
- prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None)
-
- the error between prediciton and real data
- with tf.name_scope('Jason_niu_loss'):
- loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
- reduction_indices=[1]))
- tf.summary.scalar('Jason_niu_loss', loss)
-
- with tf.name_scope('Jason_niu_train'):
- train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
-
- sess = tf.Session()
- merged = tf.summary.merge_all()
- writer = tf.summary.FileWriter("logs3/", sess.graph)
- important step
- sess.run(tf.global_variables_initializer())
-
- 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)
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TF:TF之Tensorboard实践:将神经网络Tensorboard形式得到events.out.tfevents文件+dos内运行该文件本地服务器输出到网页可视化
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