CNN中tensorboard数据可视化

Posted 2021-02-04 ck85

1.CNN_my_test.py

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


mnist = input_data.read_data_sets(‘data/‘, one_hot=True)

print(‘数据ok‘)

print(mnist.train.images[0].shape)


def weight_initializer(shape):
    initializer = tf.truncated_normal(shape, stddev= 0.1)
    return tf.Variable(initializer)


def biases_initializer(shape):
    initializer = tf.constant(0.1, shape=shape)
    return tf.Variable(initializer)






x = tf.placeholder(tf.float32, shape=[None, 784])
y = tf.placeholder(tf.float32, shape=[None, 10])


x_image = tf.reshape(x, [-1, 28, 28, 1])
tf.summary.image(‘input‘, x_image, 1)

wc1 = weight_initializer([5, 5, 1, 32])
bc1 = biases_initializer([32])
hc1 = tf.nn.relu(tf.nn.conv2d(x_image, wc1, strides=[1, 1, 1, 1], padding=‘SAME‘) + bc1)
pool_hc1 = tf.nn.max_pool(hc1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘)

wc2 = weight_initializer([5, 5, 32, 64])
bc2 = biases_initializer([64])
hc2 = tf.nn.relu(tf.nn.conv2d(pool_hc1, wc2, strides=[1, 1, 1, 1], padding=‘SAME‘) + bc2)
pool_hc2 = tf.nn.max_pool(hc2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=‘SAME‘)

wd1 = weight_initializer([7*7*64, 1024])
bd1 = biases_initializer([1024])
hc2_flat = tf.reshape(pool_hc2, [-1, 7*7*64])
hd1 = tf.nn.relu(tf.matmul(hc2_flat, wd1) + bd1)
hd1_dp = tf.nn.dropout(hd1, keep_prob=0.7)



wd2 = weight_initializer([1024, 10])
bd2 = biases_initializer([10])
y_conv = tf.nn.softmax(tf.matmul(hd1_dp, wd2) + bd2)


cross_entropy = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(logits=y_conv, labels=y))
tf.summary.scalar(‘cross entropy‘, cross_entropy)

train_step = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cross_entropy)
corr = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y, 1))
acc = tf.reduce_mean(tf.cast(corr, tf.float32))

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



merged = tf.summary.merge_all()
log_dir = ‘./log‘
train_writer = tf.summary.FileWriter(log_dir + ‘/train‘, sess.graph)

for i in range(2000):
    if i % 100 != 0:
        batch = mnist.train.next_batch(50)
        train_step.run(session=sess, feed_dict={x: batch[0], y: batch[1]})
        summary, _ = sess.run([merged, train_step], feed_dict={x: batch[0], y: batch[1]})
        train_writer.add_summary(summary, i)

    else:
        batch = mnist.train.next_batch(50)
        train_accuracy = acc.eval(session=sess, feed_dict={x: batch[0], y: batch[1]})
        test_accuracy = acc.eval(session=sess, feed_dict={x: mnist.test.images[0:50], y: mnist.test.labels[0:50]})
        print(‘train_acc: %.5f, test_acc: %.5f‘ % (train_accuracy, test_accuracy))
        run_metadata = tf.RunMetadata()
        train_writer.add_run_metadata(run_metadata, ‘step%03d‘ % i)

        summary, _ = sess.run([merged, train_step], feed_dict={x: batch[0], y: batch[1]})
        train_writer.add_summary(summary, 1)


print(‘训练完成！！‘)
train_writer.close()

分3个部分

1.将需要记录的变量用一下函数记录

图像

tf.summary.image(‘input‘, x_image, 1)

散点图

tf.summary.scalar(‘cross entropy‘, cross_entropy)

 

2.生成实现变量记录的对象，和记录文件路径

merged = tf.summary.merge_all()
log_dir = ‘./log‘
train_writer = tf.summary.FileWriter(log_dir + ‘/train‘, sess.graph)

3.训练时进行记录

        summary, _ = sess.run([merged, train_step], feed_dict={x: batch[0], y: batch[1]})
        train_writer.add_summary(summary, 1)