DNN of TF: Tensorboard visualization of DNN neural network (get events.out.tfEvents local server output to web page visualization)

 

 

directory

The output

Code design

 

The output

 

 

Code design

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: Tensorboard practice: The neural network Tensorboard form to get events.out. tfEvents file + DOS running the file local server output to the web page visualization