这里是第5课的代码
5-2tensorboard网络结构
# coding: utf-8
# In[1]:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# In[2]:
# 载入数据集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
# 每个批次的大小
batch_size = 100
# 计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size
# 命名空间
with tf.name_scope('input'):
# 定义两个placeholder
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'):
# 创建一个简单的神经网络
with tf.name_scope('wights'):
W = tf.Variable(tf.zeros([784, 10]), name='W')
with tf.name_scope('biases'):
b = tf.Variable(tf.zeros([10]), name='b')
with tf.name_scope('wx_plus_b'):
wx_plus_b = tf.matmul(x, W) + b
with tf.name_scope('softmax'):
prediction = tf.nn.softmax(wx_plus_b)
# 二次代价函数
# loss = tf.reduce_mean(tf.square(y-prediction))
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
with tf.name_scope('train'):
# 使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
# 初始化变量
init = tf.global_variables_initializer()
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
# 结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # argmax返回一维张量中最大的值所在的位置
with tf.name_scope('accuracy'):
# 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(init)
writer = tf.summary.FileWriter('logs/', sess.graph)
for epoch in range(1):
for batch in range(n_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys})
acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))
# In[ ]:
5-3tensorboard网络运行
# coding: utf-8
# In[1]:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# In[2]:
# 载入数据集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
# 每个批次的大小
batch_size = 100
# 计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size
# 参数概要
def variable_summaries(var):
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean) # 平均值
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)) # 最大值
tf.summary.scalar('min', tf.reduce_min(var)) # 最小值
tf.summary.histogram('histogram', var) # 直方图
# 命名空间
with tf.name_scope('input'):
# 定义两个placeholder
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'):
# 创建一个简单的神经网络
with tf.name_scope('wights'):
W = tf.Variable(tf.zeros([784, 10]), name='W')
variable_summaries(W)
with tf.name_scope('biases'):
b = tf.Variable(tf.zeros([10]), name='b')
variable_summaries(b)
with tf.name_scope('wx_plus_b'):
wx_plus_b = tf.matmul(x, W) + b
with tf.name_scope('softmax'):
prediction = tf.nn.softmax(wx_plus_b)
# 二次代价函数
# loss = tf.reduce_mean(tf.square(y-prediction))
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
tf.summary.scalar('loss', loss)
with tf.name_scope('train'):
# 使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
# 初始化变量
init = tf.global_variables_initializer()
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
# 结果存放在一个布尔型列表中
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # argmax返回一维张量中最大的值所在的位置
with tf.name_scope('accuracy'):
# 求准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
# 合并所有的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))
# In[ ]:
# for i in range(2001):
# #m每个批次100个样本
# batch_xs,batch_ys = mnist.train.next_batch(100)
# summary,_ = sess.run([merged,train_step],feed_dict={x:batch_xs,y:batch_ys})
# writer.add_summary(summary,i)
# if i%500 == 0:
# print(sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}))
5-4tensorboard可视化
# coding: utf-8 # In[1]: import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow.contrib.tensorboard.plugins import projector # In[2]: # 载入数据集 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) # 运行次数 max_steps = 1001 # 图片数量 image_num = 3000 # 文件路径 DIR = "D:/Tensorflow/" # 定义会话 sess = tf.Session() # 载入图片 embedding = tf.Variable(tf.stack(mnist.test.images[:image_num]), trainable=False, name='embedding') # 参数概要 def variable_summaries(var): with tf.name_scope('summaries'): mean = tf.reduce_mean(var) tf.summary.scalar('mean', mean) # 平均值 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)) # 最大值 tf.summary.scalar('min', tf.reduce_min(var)) # 最小值 tf.summary.histogram('histogram', var) # 直方图 # 命名空间 with tf.name_scope('input'): # 这里的none表示第一个维度可以是任意的长度 x = tf.placeholder(tf.float32, [None, 784], name='x-input') # 正确的标签 y = tf.placeholder(tf.float32, [None, 10], name='y-input') # 显示图片 with tf.name_scope('input_reshape'): image_shaped_input = tf.reshape(x, [-1, 28, 28, 1]) tf.summary.image('input', image_shaped_input, 10) with tf.name_scope('layer'): # 创建一个简单神经网络 with tf.name_scope('weights'): W = tf.Variable(tf.zeros([784, 10]), name='W') variable_summaries(W) with tf.name_scope('biases'): b = tf.Variable(tf.zeros([10]), name='b') variable_summaries(b) with tf.name_scope('wx_plus_b'): wx_plus_b = tf.matmul(x, W) + b with tf.name_scope('softmax'): prediction = tf.nn.softmax(wx_plus_b) with tf.name_scope('loss'): # 交叉熵代价函数 loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction)) tf.summary.scalar('loss', loss) with tf.name_scope('train'): # 使用梯度下降法 train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss) # 初始化变量 sess.run(tf.global_variables_initializer()) with tf.name_scope('accuracy'): with tf.name_scope('correct_prediction'): # 结果存放在一个布尔型列表中 correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) # argmax返回一维张量中最大的值所在的位置 with tf.name_scope('accuracy'): # 求准确率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 把correct_prediction变为float32类型 tf.summary.scalar('accuracy', accuracy) # 产生metadata文件 if tf.gfile.Exists(DIR + 'projector/projector/metadata.tsv'): tf.gfile.DeleteRecursively(DIR + 'projector/projector/metadata.tsv') with open(DIR + 'projector/projector/metadata.tsv', 'w') as f: labels = sess.run(tf.argmax(mnist.test.labels[:], 1)) for i in range(image_num): f.write(str(labels[i]) + '\n') # 合并所有的summary merged = tf.summary.merge_all() projector_writer = tf.summary.FileWriter(DIR + 'projector/projector', sess.graph) saver = tf.train.Saver() config = projector.ProjectorConfig() embed = config.embeddings.add() embed.tensor_name = embedding.name embed.metadata_path = DIR + 'projector/projector/metadata.tsv' embed.sprite.image_path = DIR + 'projector/data/mnist_10k_sprite.png' embed.sprite.single_image_dim.extend([28, 28]) projector.visualize_embeddings(projector_writer, config) for i in range(max_steps): # 每个批次100个样本 batch_xs, batch_ys = mnist.train.next_batch(100) run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_metadata = tf.RunMetadata() summary, _ = sess.run([merged, train_step], feed_dict={x: batch_xs, y: batch_ys}, options=run_options, run_metadata=run_metadata) projector_writer.add_run_metadata(run_metadata, 'step%03d' % i) projector_writer.add_summary(summary, i) if i % 100 == 0: acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels}) print("Iter " + str(i) + ", Testing Accuracy= " + str(acc)) saver.save(sess, DIR + 'projector/projector/a_model.ckpt', global_step=max_steps) projector_writer.close() sess.close() # In[ ]: