def batch_set_value(tuples):
 if tuples:
  assign_ops = []
  feed_dict = {}
  for (x, value) in tuples:
   value = np.asarray(value)
   tf_dtype = _convert_string_dtype(x.dtype.name.split('_')[0])
   if hasattr(x, '_assign_placeholder'):
    assign_placeholder = x._assign_placeholder
    assign_op = x._assign_op
   else:
    assign_placeholder = tf.placeholder(tf_dtype, shape=value.shape)
    assign_op = x.assign(assign_placeholder)
    x._assign_placeholder = assign_placeholder
    x._assign_op = assign_op
   assign_ops.append(assign_op)
   feed_dict[assign_placeholder] = value
  get_session().run(assign_ops, feed_dict=feed_dict)

def batch_set_value ( tuples ) : if tuples : assign_ops = [ ] feed_dict = { } for ( x , value ) in tuples : value = np . asarray ( value ) tf_dtype = _convert_string_dtype ( x . dtype . name . split ( '_' ) [ 0 ] ) if hasattr ( x , '_assign_placeholder' ) : assign_placeholder = x . _assign_placeholder assign_op = x . _assign_op else : assign_placeholder = tf . placeholder ( tf_dtype , shape = value . shape ) assign_op = x . assign ( assign_placeholder ) x . _assign_placeholder = assign_placeholder x . _assign_op = assign_op assign_ops . append ( assign_op ) feed_dict [ assign_placeholder ] = value get_session ( ) . run ( assign_ops , feed_dict = feed_dict )
sets the values of many tensor variables at once .