讲解几个重点知识
1、对于tf.get_variable()中的reuse，意思是，如果有名字一模一样的变量，则对这个变量继续使用，如果没有名字一模一样的变量，则创建这个变量
2、options=run_options, run_metadata=run_metadata这玩意不好使
3、记住accuracy的argmax()
4、求accuracy三步：（1）argmax() (2)cast() (3)reduce_mean()
以下是mnist_inference的内容
import tensorflow as tf
INPUT_NODE = 784
OUTPUT_NODE = 10
LAYER1_NODE = 500
def get_weight_variable(shape, regularizer):
weights = tf.get_variable("weights", shape, initializer=tf.truncated_normal_initializer(stddev=0.1))
if regularizer != None: tf.add_to_collection('losses', regularizer(weights))
return weights
def inference(input_tensor, regularizer):
with tf.variable_scope('layer1'):
weights = get_weight_variable([INPUT_NODE, LAYER1_NODE], regularizer)
biases = tf.get_variable("biases", [LAYER1_NODE], initializer=tf.constant_initializer(0.0))
layer1 = tf.nn.relu(tf.matmul(input_tensor, weights) + biases)
with tf.variable_scope('layer2'):
weights = get_weight_variable([LAYER1_NODE, OUTPUT_NODE], regularizer)
biases = tf.get_variable("biases", [OUTPUT_NODE], initializer=tf.constant_initializer(0.0))
layer2 = tf.matmul(layer1, weights) + biases
return layer2以下是train的内容
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_inference
from mnist_inference import inference
BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 30000
MOVING_AVERAGE_DECAY = 0.99
def train(mnist):
# 输入数据的命名空间。
with tf.name_scope('input'):
x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')
y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')
with tf.variable_scope("layer"):
regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
y = mnist_inference.inference(x, regularizer)
global_step = tf.Variable(0, trainable=False)
# 处理滑动平均的命名空间。
with tf.name_scope("moving_average"):
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
# 计算损失函数的命名空间。
with tf.name_scope("loss_function"):
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
cross_entropy_mean = tf.reduce_mean(cross_entropy)
loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
with tf.name_scope("layer"):
logits = inference(x, None)
accuracy_op = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)), tf.float32))
# 定义学习率、优化方法及每一轮执行训练的操作的命名空间。
with tf.name_scope("train_step"):
learning_rate = tf.train.exponential_decay(
LEARNING_RATE_BASE,
global_step,
mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY,
staircase=True)
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
with tf.control_dependencies([train_step, variables_averages_op]):
train_op = tf.no_op(name='train')
writer = tf.summary.FileWriter("log", tf.get_default_graph()) # 注意这个是写在前面的
# 训练模型。
with tf.Session() as sess:
tf.global_variables_initializer().run()
for i in range(TRAINING_STEPS):
xs, ys = mnist.train.next_batch(BATCH_SIZE)
if i % 1000 == 0:
# 配置运行时需要记录的信息。
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
# 运行时记录运行信息的proto。
run_metadata = tf.RunMetadata()
_, loss_value, step = sess.run(
[train_op, loss, global_step], feed_dict={x: xs, y_: ys})
# options=run_options, run_metadata=run_metadata) # 看这里，在运行[train_op, loss, global_step]的时候,后边配置
# options = run_options, run_metadata = run_metadata
accuracy = sess.run(accuracy_op, feed_dict={x: mnist.validation.images, y_: mnist.validation.labels})
writer.add_run_metadata(run_metadata=run_metadata, tag=("tag%d" % i), global_step=i)
print("After %d training step(s), loss on training batch is %g." % (step, loss_value))
print("After %d training step(s), accuracy on validation batch is %g." % (step, accuracy))
else:
_, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})
writer.close()
def main(argv=None):
mnist = input_data.read_data_sets("./MNIST_data", one_hot=True)
train(mnist)
if __name__ == '__main__':
main()