目标
本文旨在介绍 tensorflow 入门知识点及实战示例，希望各位新手同学能在学习之后熟练 tensorflow 相关操作
手动调整学习率代码
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST", one_hot=True)
batch_size = 64
n_batches = mnist.train.num_examples // batch_size
def variable_info(var): # 将所有变量都存起来，一会可以在 tensorboard 的图中观察其动态
with tf.name_scope('summaries'):
mean_value = tf.reduce_mean(var)
tf.summary.scalar('mean', mean_value)
with tf.name_scope('stddev'):
stddev_value = tf.sqrt(tf.reduce_mean(tf.square(var - mean_value)))
tf.summary.scalar('stddev', stddev_value)
tf.summary.scalar('max', tf.reduce_max(var))
tf.summary.scalar('min', tf.reduce_min(var))
tf.summary.histogram('histogram',var)
with tf.name_scope("input_layer"):
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
lr = tf.Variable(0.01,tf.float32) # 这里的学习率设置为变量
tf.summary.scalar('learning_rate',lr) # 将 lr 存起来，一会可以在 tensorboard 的图中观察其动态
with tf.name_scope('network'): # 搭建网络结构
with tf.name_scope("weights"):
w = tf.Variable(tf.truncated_normal([784,10], stddev=0.1), name='w')
variable_info(w)
with tf.name_scope('baises'):
b = tf.Variable(tf.zeros([10]) + 0.1, name="b")
variable_info(b)
with tf.name_scope('xw_plus_b'):
a = tf.matmul(x,w) + b
with tf.name_scope('softmax'):
out = tf.nn.softmax(a)
with tf.name_scope("loss_train"): # 计算损失值，定义优化器
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out, labels=y))
train_step = tf.train.AdamOptimizer(lr).minimize(loss)
tf.summary.scalar("loss", loss) # 将 loss 存起来，一会可以在 tensorboard 的图中观察其动态
with tf.name_scope("eval"): # 计算准确率，评估模型效果
with tf.name_scope("correct"):
correct = tf.equal(tf.argmax(out, 1), tf.argmax(y, 1))
with tf.name_scope("accuracy"):
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
tf.summary.scalar('accuracy',accuracy)
init = tf.global_variables_initializer()
merged = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(init)
writer = tf.summary.FileWriter('tflogs/', sess.graph) # 将结果文件都存入目标目录中，使用 tensorboard 工具可以查看
for epoch in range(20):
sess.run(tf.assign(lr, 0.001 * (0.95 ** epoch)))
for batch in range(n_batches):
batch_x, batch_y = mnist.train.next_batch(batch_size)
summary, _ = sess.run([merged, train_step], feed_dict = {x:batch_x, y:batch_y, keep_prob:0.5})
writer.add_summary(summary, epoch * n_batches + batch)
loss_value, acc, lr_value = sess.run([loss, accuracy, lr], feed_dict = {x:mnist.test.images, y:mnist.test.labels, keep_prob:1.0})
print("Iter: ", epoch, "Loss: ", loss_value, "Acc: ", acc, "lr: ", lr_value)
复制代码结果输出
Iter: 0 Loss: 1.6054871 Acc: 0.895 lr: 0.001
Iter: 1 Loss: 1.5699576 Acc: 0.9148 lr: 0.00095
Iter: 2 Loss: 1.5598879 Acc: 0.9195 lr: 0.0009025
Iter: 3 Loss: 1.5546178 Acc: 0.9215 lr: 0.000857375
Iter: 4 Loss: 1.5502373 Acc: 0.9254 lr: 0.00081450626
Iter: 5 Loss: 1.5473799 Acc: 0.9269 lr: 0.0007737809
Iter: 6 Loss: 1.5452079 Acc: 0.9277 lr: 0.0007350919
Iter: 7 Loss: 1.5434842 Acc: 0.9294 lr: 0.0006983373
Iter: 8 Loss: 1.5427189 Acc: 0.9278 lr: 0.0006634204
Iter: 9 Loss: 1.5417348 Acc: 0.9293 lr: 0.0006302494
Iter: 10 Loss: 1.540729 Acc: 0.9293 lr: 0.0005987369
Iter: 11 Loss: 1.5403976 Acc: 0.9298 lr: 0.0005688001
Iter: 12 Loss: 1.5395288 Acc: 0.9301 lr: 0.0005403601
Iter: 13 Loss: 1.5395651 Acc: 0.9298 lr: 0.0005133421
Iter: 14 Loss: 1.5387015 Acc: 0.9307 lr: 0.000487675
Iter: 15 Loss: 1.5383359 Acc: 0.9308 lr: 0.00046329122
Iter: 16 Loss: 1.5379355 Acc: 0.931 lr: 0.00044012666
Iter: 17 Loss: 1.5374689 Acc: 0.9314 lr: 0.00041812033
Iter: 18 Loss: 1.5376941 Acc: 0.9305 lr: 0.00039721432
Iter: 19 Loss: 1.5371386 Acc: 0.9308 lr: 0.0003773536
复制代码要点一
上述过程执行完如果想看保存的数据在图中的变化，可以按照下面的步骤来
- pip install tensorboard==1.10.0
- 然后命令行运行 ./tensorboard --logdir=保存的绝对位置
- 浏览器中打开 http://localhost:6006/ 就能看到相应的参数变化，很方便
要点二
调整学习率有助于训练模型，开始的时候使用较大点的学习率来保证模型效果快速收敛，在收敛到最优点附近时要尽量缩小学习率避免来回震荡。这里使用的就是最常见的一种学习率衰减方式，也就是指数衰减。