灵感来自Andrej Karpathy Char-RNN，有一个关于char-rnn sherjilozair/char-rnn-tensorflow: Multi-layer Recurrent Neural Networks (LSTM, RNN) for character-level language models in Python using Tensorflow的Tensorflow实现 . 我想从这段代码实现双向字符级语言模型 . 我更改model.py并编写了一个简单的代码：
class Model:
def __init__(self, input_data, targets, seq_length=Config.max_seq_length, training=True):
if Config.model == 'rnn':
cell_fn = rnn.BasicRNNCell
elif Config.model == 'gru':
cell_fn = rnn.GRUCell
elif Config.model == 'lstm':
cell_fn = rnn.BasicLSTMCell
elif Config.model == 'nas':
cell_fn = rnn.NASCell
else:
raise Exception("model type not supported: {}".format(Config.model))
fw_cells = []
bw_cells = []
for _ in range(Config.num_layers):
fw_cell = cell_fn(Config.rnn_size)
bw_cell = cell_fn(Config.rnn_size)
fw_cells.append(fw_cell)
bw_cells.append(bw_cell)
self.fw_cell = rnn.MultiRNNCell(fw_cells, state_is_tuple=True)
self.bw_cell = rnn.MultiRNNCell(bw_cells, state_is_tuple=True)
self.input_data, self.targets = input_data, targets
with tf.variable_scope('rnnlm'):
softmax_w = tf.get_variable("softmax_w", [Config.rnn_size*2, Config.vocab_size])
softmax_b = tf.get_variable("softmax_b", [Config.vocab_size])
embedding = tf.get_variable("embedding", [Config.vocab_size, Config.rnn_size])
inputs = tf.nn.embedding_lookup(embedding, self.input_data)
inputs = tf.unstack(inputs, num=seq_length, axis=1)
outputs, _, _ = tf.nn.static_bidirectional_rnn(self.fw_cell, self.bw_cell, inputs,
dtype=tf.float32, scope='rnnlm')
output = tf.reshape(tf.concat(outputs, 1), [-1, Config.rnn_size*2])
self.logits = tf.matmul(output, softmax_w) + softmax_b
self.probs = tf.nn.softmax(self.logits)
self.lr = tf.Variable(0.0, trainable=False)
if training:
loss = legacy_seq2seq.sequence_loss_by_example(
[self.logits],
[tf.reshape(self.targets, [-1])],
[tf.sign(tf.cast(tf.reshape(self.targets, [-1]), dtype=tf.float32))])
with tf.name_scope('cost'):
self.cost = tf.reduce_mean(loss)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars), Config.grad_clip)
with tf.name_scope('optimizer'):
optimizer = tf.train.AdamOptimizer(self.lr)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
在训练阶段，我看到快速收敛 . 在3000次迭代后，损失达到0.003 . 在测试阶段，所有角色的概率为1.0 . 我认为有一个错误 . 我很乐意帮助找到我的错误 .
2 回答
看起来你设置
self.lr = tf.Variable(0.0, trainable=False). 尝试将其更改为非零值 . 如果您在测试阶段从self.probs读取概率，则应对其进行适当的归一化，使用前后输出来预测当前单词的概率 . 在您的情况下，您使用当前的rnn输出来预测当前单词的概率 .