尝试在张量流中实现最小玩具RNN示例 . 目标是学习从输入数据到目标数据的映射，类似于这个精彩的简洁example in theanets .
更新：我们到了那里 . 剩下的唯一部分是使它收敛（并且不那么复杂） . 有人可以帮助将以下内容转换为运行代码或提供一个简单的示例吗？
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!?
1 回答
我认为您的代码存在一些问题，但这个想法是正确的 .
主要问题是您使用单张量输入和输出，如：
inputs = tf.placeholder(tf.int32, [batch_size, num_steps]).在TensorFlow中，RNN函数采用张量列表（因为num_steps在某些模型中可能会有所不同） . 所以你应该构建这样的输入：
inputs = [tf.placeholder(tf.int32, [batch_size, 1]) for _ in xrange(num_steps)]然后你需要注意你的输入是int32s的事实，但RNN单元在float向量上运行 - 这就是embedding_lookup的用途 .
最后，您需要调整您的Feed以输入输入列表 .
我认为ptb教程是一个合理的观察点，但是如果你想要一个开箱即用的RNN的更简单的例子，你可以看看一些rnn单元测试，例如，这里 . https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/kernel_tests/rnn_test.py#L164