with tf.name_scope('input'):
xs = tf.placeholder(tf.float32, [None, 1], name = 'x_input')
ys = tf.placeholder(tf.float32, [None, 1], name = 'y_input')
def add_layer(input, in_size, out_size, activation_function = None):
with tf.name_scope('layer'):
with tf.name_scope('weight'):
W = tf.Variable(tf.random_normal([in_size, out_size]), name = 'W')
with tf.name_scope('biases'):
b = tf.Variable(tf.zeros([1, out_size]) + 0.1, name = 'b')
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.add(tf.matmul(input, W), b)
if activation_function == None:
out = Wx_plus_b
else:
out = activation_function(Wx_plus_b)
return out
l1 = add_layer(xs, 1, 10, tf.nn.relu)
prediction = add_layer(l1, 10, 1)
with tf.name_scope('loss'):
loss = tf.reduce_mean( tf.square(ys - prediction) )
optimize = tf.train.GradientDescentOptimizer(0.1)
with tf.name_scope('train'):
train = optimize.minimize(loss)
sess = tf.Session()
writer = tf.summary.FileWriter('D:/log/', sess.graph)
tensorboard --logdir=./log --host=127.0.0.1
很棒的结果：