import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# 載入資料集
mnist = input_data.read_data_sets("mnist_data", one_hot=true)
# 每個批次的大小
batch_size = 100
# 計算一共有多少個批次
n_batch = mnist.train.num_examples // batch_size
defvariable_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) # 標準差
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) # 直方圖
# 定義命名空間
with tf.name_scope('input'):
# 定義兩個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'):
with tf.name_scope('wights'):
# 建立乙個簡單的神經網路
w = tf.variable(tf.zeros([784, 10]), name='w')
variable_summaries(w)
with tf.name_scope('biases'):
b = tf.variable(tf.zeros([1, 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'):
# 定義二次代價函式
loss = tf.reduce_mean(tf.square(y - prediction))
tf.summary.scalar('loss', loss)
with tf.name_scope('train'):
# 交叉熵
# loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels= y, logits= prediction))
# 定義梯度下降法
train_step = tf.train.gradientdescentoptimizer(0.2).minimize(loss)
# 初始化變數
init = tf.global_variables_initializer()
with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'):
# 結果儲存在乙個布林型列表中
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(prediction, 1))
with tf.name_scope('accuracy'):
# 求準確率
accuracy= tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
# 合併所有的summary
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