CODEWITHSUNDEEP
tensorflow
juanmanpr
juanmanpr

Reputation: 21

InvalidArgumentError when running tf.global_variables_initializer()

Basically, I have a function that expects a tensor x and two placeholders z and c.

def error_robust(x,z,c):
  zz = tf.reshape(z, [-1, 28, 28, 1])
  var = tf.reduce_mean(x-zz)
  out = tf.cond( tf.abs(var) <= c, lambda: (c*c/6.0)*(1 - tf.pow(1-tf.pow(var/c,2),3)), lambda: tf.Variable(c*c/6.0) )
  return out

I define the placeholders and tensors that I am gonna use:

# TENSORFLOW PLACEHOLDERS
sess  = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
flat_mnist_data = tf.placeholder(tf.float32, [None, 28*28])
dropout_keep_prob = tf.placeholder(tf.float32)
param_robust = tf.placeholder(tf.float32, shape=())

Calling the defined function does not generate any errors:

error_r = error_robust(layer1_b.reconstruction, flat_mnist_data, param_robust)

This generates an error:

sess.run(tf.global_variables_initializer())

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype float [[Node: Placeholder = Placeholderdtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/gpu:0"]]

I don't really understand why it happens. Any ideas on how to solve this one?

Upvotes: 1

Views: 311

Answers (1)

juanmanpr
juanmanpr

Reputation: 21

Ok, I got it. I was first expecting c to be a simple scalar. So I was using tf.Variable as the second argument of the tf.cond. Updating the error_robust function solves it:

def error_robust(x,z,c):
  zz = tf.reshape(z, [-1, 28, 28, 1])
  var = tf.reduce_mean(x-zz)
  out = tf.cond( tf.abs(var) <= c, lambda: (c*c/6.0)*(1 - tf.pow(1-tf.pow(var/c,2),3)), lambda: c*c/6.0 )
  return out

Upvotes: 0

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