How to use tf.while_loop () for variable length inputs in a tensor stream?

Question

I am trying to use tf.while_loop () to handle variable length inputs. However, I can only use it for a fixed length. The code no longer works after I changed the shape = (4) to the shape = (No). tf.dynamic_rnn seems to handle variable-length inputs. I'm not sure how tf.dynamic_rnn accomplishes this with tf.while_loop ().

import tensorflow as tf import numpy as np from tensorflow.python.ops import tensor_array_ops from tensorflow.python.ops import array_ops with tf.Graph().as_default(), tf.Session() as sess: initial_m = tf.Variable(0.0, name='m') inputs = tf.placeholder(dtype='float32', shape=(4)) #The code no longer works after I change shape=(4) to shape=(None) #inputs = tf.placeholder(dtype='float32', shape=(None)) time_steps = tf.shape(inputs)[0] initial_outputs = tf.TensorArray(dtype=tf.float32, size=time_steps) initial_t = tf.constant(0, dtype='int32') def should_continue(t, *args): return t < time_steps def iteration(t, m, outputs_): cur = tf.gather(inputs, t) m = m * 0.5 + cur * 0.5 outputs_ = outputs_.write(t, m) return t + 1, m, outputs_ t, m, outputs = tf.while_loop( should_continue, iteration, [initial_t, initial_m, initial_outputs]) outputs = outputs.pack() init = tf.global_variables_initializer() sess.run([init]) print sess.run([outputs], feed_dict={inputs: np.asarray([1,1,1,1])}) 

(before change):

 [array([ 0.5 , 0.75 , 0.875 , 0.9375], dtype=float32)] 

output (after change):

 Traceback (most recent call last): File "simple.py", line 26, in <module> [initial_t, initial_m, initial_outputs]) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2636, in while_loop result = context.BuildLoop(cond, body, loop_vars, shape_invariants) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2469, in BuildLoop pred, body, original_loop_vars, loop_vars, shape_invariants) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2450, in _BuildLoop _EnforceShapeInvariant(m_var, n_var) File "/usr/local/lib/python2.7/site-packages/tensorflow/python/ops/control_flow_ops.py", line 586, in _EnforceShapeInvariant % (merge_var.name, m_shape, n_shape)) ValueError: The shape for while/Merge_1:0 is not an invariant for the loop. It enters the loop with shape (), but has shape <unknown> after one iteration. Provide shape invariants using either the `shape_invariants` argument of tf.while_loop or set_shape() on the loop variables. 

Answer 1

It works if you delete shapes from all variables:

 import tensorflow as tf import numpy as np config = tf.ConfigProto(graph_options=tf.GraphOptions( optimizer_options=tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0))) tf.reset_default_graph() sess = tf.Session("", config=config) #initial_m = tf.Variable(0.0, name='m') #The code no longer works after I change shape=(4) to shape=(None) inputs = tf.placeholder(dtype='float32', shape=(None)) time_steps = tf.shape(inputs)[0] initial_outputs = tf.TensorArray(dtype=tf.float32, size=time_steps) initial_t = tf.placeholder(dtype='int32') initial_m = tf.placeholder(dtype=tf.float32) def should_continue(t, *args): return t < time_steps def iteration(t, m, outputs_): cur = tf.gather(inputs, t) m = m * 0.5 + cur * 0.5 outputs_ = outputs_.write(t, m) return t + 1, m, outputs_ t, m, outputs = tf.while_loop(should_continue, iteration, [initial_t, initial_m, initial_outputs]) outputs = outputs.stack() init = tf.global_variables_initializer() sess.run([init]) print(sess.run([outputs], feed_dict={inputs: np.asarray([1, 1, 1, 1]), initial_t: 0, initial_m: 0.}))