title: Tensorflow error: ValueError: None values not supported
body: <p>This is my test code. But it could not run. Terminal always gave me this error:</p>
  
  <blockquote>
    <p>Traceback (most recent call last):<br>
    File "desktop/test.py", line 28,
    in  loss =tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1])).<br>
    File "/Users/sumeixu/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py",
    line 898, in binary_op_wrapper
        y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name="y")<br>
    File
    "/Users/sumeixu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py",
    line 932, in convert_to_tensor
        as_ref=False)<br>
    File "/Users/sumeixu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py",
    line 1022, in internal_convert_to_tensor
        ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)<br>
    File
    "/Users/sumeixu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py",
    line 233, in _constant_tensor_conversion_function
        return constant(v, dtype=dtype, name=name)<br>
    File "/Users/sumeixu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py",
    line 212, in constant
        value, dtype=dtype, shape=shape, verify_shape=verify_shape))<br>
    File
    "/Users/sumeixu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py",
    line 401, in make_tensor_proto
        raise ValueError("None values not supported.").       </p>
  </blockquote>
  
  <p>ValueError: None values not supported.</p>
  
  <p>Help please. Blew is my code. Thank you so much!</p>
  
  <pre><code>import tensorflow as tf 
  import numpy as np 
  import matplotlib.pyplot as plt
  
  def add_layer(inputs,in_size,out_size,activation_function=None):
      Weights=tf.Variable(tf.random_normal([in_size,out_size]))
      biases=tf.Variable(tf.zeros([1,out_size])+0.1)
      Wx_Plus_b = tf.matmul(inputs,Weights)+biases
  
      if activation_function is None:
          outputs=Wx_Plus_b
      else:
              outputs=activation_function(Wx_Plus_b)
              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
  
  xs=tf.placeholder(tf.float32,[None,1])
  ys=tf.placeholder(tf.float32,[None,1])
  
  l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)
  
  prediction=add_layer(l1,10,1,activation_function=None)
  
  loss =tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))
  
  train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)
  
  init = tf.initialize_all_variables()
  sess = tf.Session()
  sess.run(init)
  
  for i in range(1000):
      sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
  
      if i%50==0:
          print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
  </code></pre>