以下代码是用神经网络做拟合,运行第一次没有问题,运行第二次在result = sess.run(merged , feed_dict = {xs:x_data,ys:y_data}) 报错??


import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

def add_layer(inputs , in_size,out_size, n_layer , activation_function = None):


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)
tf.summary.histogram(layer_name+'/biases', biases)
with tf.name_scope('Wx_plus_b'):

Wx_plus_b = 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

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

fig = plt.figure()
ax = fig.add_subplot(1,1,1)

ax.scatter(x_data,y_data,s = 10,color = 'b')

plt.ion()

plt.show()

with tf.name_scope('inputs'):
xs = tf.placeholder(tf.float32,[None , 1],name = 'x_input') #None 表示无论给多少个粒子都可以 输出为1
ys = tf.placeholder(tf.float32,[None , 1],name = 'y_input')

从输入到隐层

l1 = add_layer(xs,1,10,n_layer=1,activation_function = tf.nn.tanh)

prediction = add_layer(l1 , 10,1,n_layer=2,activation_function = None)

with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
reduction_indices=[1]))
##tf.summary.scaler('loss',loss)
tf.summary.scalar('loss',loss)




with tf.name_scope('train'):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.global_variables_initializer()
sess = tf.Session()

merged = tf.summary.merge_all()

writer = tf.summary.FileWriter('logs/', sess.graph)

sess.run(init)

for i in range(2001):
sess.run(train_step , feed_dict = {xs:x_data,ys:y_data})
if i%50==0:

result = sess.run(merged , feed_dict = {xs:x_data,ys:y_data})
writer.add_summary(result,i)
print(sess.run(loss, feed_dict = {xs:x_data,ys:y_data}))
try:
ax.lines.remove(lines[0])
except Exception:
pass
prediction_value = sess.run(prediction,feed_dict = {xs:x_data})
lines = plt.plot(x_data,prediction_value,'',lw = 5)
#plt.show()
plt.pause(0.1)

sess.close()

第二次运行出现报错:
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'inputs/x_input' with dtype float
[[Node: inputs/x_input = Placeholderdtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/gpu:0"]]
[[Node: layer2_3/biases/Variable/read/_15 = _Recvclient_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_197_layer2_3/biases/Variable/read", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]]
已邀请:

寒老师

赞同来自: 七月在线


既然重新connect to kernel就不会报错,感觉是环境配置问题。。。

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