关于此问题：
: You must feed a value for placeholder tensor 'inputs/y_input' with dtype float and shape [?,1]
参考：https://blog.csdn.net/m0_37870649/article/details/79428960
https://blog.csdn.net/sinat_20729643/article/details/78683677
import tensorflow as tf
import numpy as np
with tf.name_scope('inputs'):
xs = tf.placeholder(tf.float32, [None, 1], name = 'x_input')
ys = tf.placeholder(tf.float32, [None, 1], name = 'y_input')
def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):
# add one more layer and return the output of this layer
layer_name = 'layer%s' % n_layer
with tf.name_scope(layer_name):
with tf.name_scope('weights'):
Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')
tf.summary.histogram(layer_name + '/weights', Weights)
with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
tf.summary.histogram(layer_name + '/biases', biases)
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b, )
tf.summary.histogram(layer_name + '/outputs', outputs)
return outputs
l1 = add_layer(xs, 1, 10, 1, tf.nn.relu)
prediction = add_layer(l1, 10, 1, n_layer=2)
with tf.name_scope('loss'):
loss = tf.reduce_mean( tf.square(ys - prediction) )
tf.summary.scalar('loss', loss)
optimize = tf.train.GradientDescentOptimizer(0.1)
with tf.name_scope('train'):
train = optimize.minimize(loss)
init = tf.global_variables_initializer()
x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = (np.square(x_data) - 0.5 + noise)
sess = tf.Session()
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter('D:/logs/', sess.graph)
sess.run(init)
tf.reset_default_graph()
for i in range(1000):
sess.run(train, feed_dict={xs:x_data, ys:y_data})
if i % 50 == 0:
#rs = sess.run(merged, feed_dict={xs:x_data, ys: y_data})
#writer.add_summary(rs)
result = sess.run(merged, feed_dict={xs:x_data, ys:y_data})
writer.add_summary(result, i)
writer.close()
然后：tensorboard --logdir=logfile 注意：log的路径上不加引号
此外还有一点:jupyter notebook中编辑完代码注意保存。