The following network code, which should be your classic simple LSTM language model, starts outputting nan loss after a while... on my training set it takes a couple of hours and I couldn't replicate it easily on smaller datasets. But it always happens in serious training.
Sparse_softmax_with_cross_entropy should be numerically stable, so it can't be the cause... but other than that, I don't see any other node that could cause an issue in the graph. What could be the problem?
class MyLM():
def __init__(self, batch_size, embedding_size, hidden_size, vocab_size):
self.x = tf.placeholder(tf.int32, [batch_size, None]) # [batch_size, seq-len]
self.lengths = tf.placeholder(tf.int32, [batch_size]) # [batch_size]
# remove padding. [batch_size * seq_len] -> [batch_size * sum(lengths)]
mask = tf.sequence_mask(self.lengths) # [batch_size, seq_len]
mask = tf.cast(mask, tf.int32) # [batch_size, seq_len]
mask = tf.reshape(mask, [-1]) # [batch_size * seq_len]
# remove padding + last token. [batch_size * seq_len] -> [batch_size * sum(lengths-1)]
mask_m1 = tf.cast(tf.sequence_mask(self.lengths - 1, maxlen=tf.reduce_max(self.lengths)), tf.int32) # [batch_size, seq_len]
mask_m1 = tf.reshape(mask_m1, [-1]) # [batch_size * seq_len]
# remove padding + first token. [batch_size * seq_len] -> [batch_size * sum(lengths-1)]
m1_mask = tf.cast(tf.sequence_mask(self.lengths - 1), tf.int32) # [batch_size, seq_len-1]
m1_mask = tf.concat([tf.cast(tf.zeros([batch_size, 1]), tf.int32), m1_mask], axis=1) # [batch_size, seq_len]
m1_mask = tf.reshape(m1_mask, [-1]) # [batch_size * seq_len]
embedding = tf.get_variable("TokenEmbedding", shape=[vocab_size, embedding_size])
x_embed = tf.nn.embedding_lookup(embedding, self.x) # [batch_size, seq_len, embedding_size]
lstm = tf.nn.rnn_cell.LSTMCell(hidden_size, use_peepholes=True)
# outputs shape: [batch_size, seq_len, hidden_size]
outputs, final_state = tf.nn.dynamic_rnn(lstm, x_embed, dtype=tf.float32,
sequence_length=self.lengths)
outputs = tf.reshape(outputs, [-1, hidden_size]) # [batch_size * seq_len, hidden_size]
w = tf.get_variable("w_out", shape=[hidden_size, vocab_size])
b = tf.get_variable("b_out", shape=[vocab_size])
logits_padded = tf.matmul(outputs, w) + b # [batch_size * seq_len, vocab_size]
self.logits = tf.dynamic_partition(logits_padded, mask_m1, 2)[1] # [batch_size * sum(lengths-1), vocab_size]
predict = tf.argmax(logits_padded, axis=1) # [batch_size * seq_len]
self.predict = tf.dynamic_partition(predict, mask, 2)[1] # [batch_size * sum(lengths)]
flat_y = tf.dynamic_partition(tf.reshape(self.x, [-1]), m1_mask, 2)[1] # [batch_size * sum(lengths-1)]
self.cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits, labels=flat_y)
self.cost = tf.reduce_mean(self.cross_entropy)
self.train_step = tf.train.AdamOptimizer(learning_rate=0.01).minimize(self.cost)
