人工智能实践：全连接网络基础

array([0.,0.,0.,0.,0.,0.,1.,0.,0.,0]) 

mnist.train.images[0]

x=tf.constant([[1,2],[1,2]])    y=tf.constant([[1,1],[1,2]])  z=tf.add(x,y) print z

[[2,3],[2,4]] 

A = tf.convert_to_tensor(np.array([[1,1,2,4], [3,4,8,5]]))  print A.dtype   b = tf.cast(A, tf.float32)   print b.dtype

A = [[1,3,4,5,6]]   B =[[1,3,4,3,2]]   with tf.Session( ) as sess:       print(sess.run(tf.equal(A, B))) 

[[True  True  True False False]]

x = [[1., 1.][2., 2.]]  print(tf.reduce_mean(x))

print(tf.reduce_mean(x, 0))

print(tf.reduce_mean(x, 1))

correct_prediction= tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))  accuracy =tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

def forward(x, regularizer):

def===  return y  def get_weight(shape, regularizer):  def

learning_rate = tf.train.exponential_decay( LEARNING_RATE_BASE,  global_step,  LEARNING_RATE_STEP, LEARNING_RATE_DECAY,  staircase=True)

原文：https://www.cnblogs.com/xiaojianliu/p/9363305.html