tensorflow - テンソルフローでの最小限の RNN の例

Question

テンソルフローで最小限のおもちゃの RNN の例を実装しようとしています。目標は、入力データからターゲット データへのマッピングを学習することです。

更新：私たちはそこに着いています。残っている唯一の部分は、それを収束させることです (そして、より複雑になりません)。誰かが以下を実行中のコードに変換したり、簡単な例を提供したりできますか?

import tensorflow as tf
from tensorflow.python.ops import rnn_cell

init_scale = 0.1
num_steps = 7
num_units = 7
input_data = [1, 2, 3, 4, 5, 6, 7]
target = [2, 3, 4, 5, 6, 7, 7]
#target = [1,1,1,1,1,1,1] #converges, but not what we want


batch_size = 1

with tf.Graph().as_default(), tf.Session() as session:
  # Placeholder for the inputs and target of the net
  # inputs = tf.placeholder(tf.int32, [batch_size, num_steps])
  input1 = tf.placeholder(tf.float32, [batch_size, 1])
  inputs = [input1 for _ in range(num_steps)]
  outputs = tf.placeholder(tf.float32, [batch_size, num_steps])

  gru = rnn_cell.GRUCell(num_units)
  initial_state = state = tf.zeros([batch_size, num_units])
  loss = tf.constant(0.0)

  # setup model: unroll
  for time_step in range(num_steps):
    if time_step > 0: tf.get_variable_scope().reuse_variables()
    step_ = inputs[time_step]
    output, state = gru(step_, state)
    loss += tf.reduce_sum(abs(output - target))  # all norms work equally well? NO!
  final_state = state

  optimizer = tf.train.AdamOptimizer(0.1)  # CONVERGEs sooo much better
  train = optimizer.minimize(loss)  # let the optimizer train

  numpy_state = initial_state.eval()
  session.run(tf.initialize_all_variables())
  for epoch in range(10):  # now
    for i in range(7): # feed fake 2D matrix of 1 byte at a time ;)
      feed_dict = {initial_state: numpy_state, input1: [[input_data[i]]]} # no
      numpy_state, current_loss,_ = session.run([final_state, loss,train], feed_dict=feed_dict)
    print(current_loss)  # hopefully going down, always stuck at 189, why!?