The tensorboard of python study notes draws the structural curve and analyzes the parameters

# coding: utf-8

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

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# load dataset
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)



# size of each batch
batch_size = 100
# Calculate how many batches there are
n_batch = mnist.train.num_examples // batch_size

#Parameter summary
def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean=tf.reduce_mean(var)
        tf.summary.scalar('mean',mean)
        with tf.name_scope('stddev'):
            stddev=tf.sqrt(tf.reduce_mean(tf.square(var-mean)))
        tf.summary.scalar('stddev',stddev)#标准差
        tf.summary.scalar('max', tf.reduce_max(var))
        tf.summary.scalar('min',tf.reduce_min(var))#minimum
        tf.summary.histogram('histogram',var)#histogram
#define a namespace
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("layer"):
    with tf.name_scope('weights'):
        W = tf.Variable(tf.zeros([784, 10]))
        variable_summaries(W)
    with tf.name_scope('biases'):
        b = tf.Variable(tf.zeros([10]))
        variable_summaries(b)
    with tf.name_scope('wx_plus_b'):
        wx_plus_b=tf.matmul(x, W) + b
# Create a simple neural network
    with tf.name_scope('prediction'):
        prediction = tf.nn.softmax(wx_plus_b)

with tf.name_scope('loss'):
    # Quadratic cost function
    loss = tf.reduce_mean(tf.square(y - prediction))
    tf.summary.scalar('loss',loss)#
with tf.name_scope("train"):
    # use gradient descent
    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

# Initialize variables
init = tf.global_variables_initializer()

with tf.name_scope('accuracy'):
    with tf.name_scope("correct_prediction"):
# The result is stored in a boolean list
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # argmax returns the position of the largest value in the one-dimensional tensor
# find the accuracy
    with tf.name_scope("accuracy"):
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        tf.summary.scalar('accuracy',accuracy)
#Merge all summary
merged=tf.summary.merge_all()

with tf.Session() as sess:
    sess.run(init)
    writer=tf.summary.FileWriter('logs/',sess.graph)
    for epoch in range(51):
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            summary,_=sess.run([merged,train_step], feed_dict={x: batch_xs, y: batch_ys})

        writer.add_summary(summary,epoch)
        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
        print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))

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