TensorFlow训练softmax回归(带tensorboard)

输入数据集：MNIST_data

模型：简单的softmax回归模型。除了应用到常用的tensorflow的api，还用到了tensorflow的TensorBoard

#encoding:utf-8

import tensorflow as tf
import numpy as np
import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels

log_dir = "mnist_logs"

def variable_summarys(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)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram', var)
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev = 0.01), name = "weight")
def model(X, w):
return tf.matmul(X, w)

graph = tf.Graph()
with graph.as_default():
input_X = tf.placeholder('float', [None, 784], name = "input_X")
input_Y = tf.placeholder('float', [None, 10], name = 'input_Y')

global_step = tf.Variable(0, trainable = False)

with tf.name_scope('input_reshape'):
image_shaped_input = tf.reshape(input_X, [-1,28, 28, 1])
tf.summary.image('input', image_shaped_input, 10)

w = init_weights([784, 10])
variable_summarys(w)
py_x = model(input_X, w)
tf.summary.tensor_summary('predict', py_x)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = py_x, labels = input_Y))
tf.summary.scalar("costsummary.tensor_summary", cost)

optimizer = tf.train.GradientDescentOptimizer(0.005).minimize(cost, global_step = global_step)
predict_op = tf.argmax(py_x, 1)
merged = tf.summary.merge_all()
with tf.device("/cpu:0"):
saver = tf.train.Saver(tf.global_variables(), max_to_keep = 2)

with tf.Session(graph = graph) as sess:
train_writer = tf.summary.FileWriter(log_dir + 'train', sess.graph)
tf.global_variables_initializer().run()

for i in range(100):
for start, end in zip(range(0, len(trX), 128), range(128, len(trX)+1, 128)):
summary, _, step_id = sess.run([merged, optimizer, global_step],
feed_dict={input_X: trX[start:end], input_Y: trY[start:end]})
train_writer.add_summary(summary, step_id)
predict_results = sess.run(predict_op, feed_dict = {input_X : teX})
accuracy = np.mean(np.argmax(teY, axis = 1) == predict_results)
print "accuracy", i+1, " ", accuracy
saver.save(sess, 'run/chekpoint', global_step  = n_step)

运行结果：



tensorboard部分展示：

summary.scalar部分