我收到以下警告：
/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gradients_impl.py:95: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
对于以下代码：
import random
import numpy as np
import os
from pathlib import Path
import tensorflow as tf
from tensorflow.contrib.rnn import HighwayWrapper, LSTMCell, DropoutWrapper
from tensorflow.python.ops import array_ops
from socket import gethostname
import os
from pathlib import Path
import numpy as np
from sklearn.metrics import f1_score, precision_score, recall_score
import json
import tensorflow as tf
import time
import argparse
from pathlib import Path
import socket
import os
import datetime
from google.colab import drive
print(print(tf.__version__))
def orthonorm(shape, dtype=tf.float32, # TODO only works for square (recurrent) weights
partition_info=None): # pylint: disable=unused-argument
"""Variable initializer that produces a random orthonormal matrix."""
if len(shape) != 2 or shape[0] != shape[1]:
raise ValueError("Expecting square shape, got %s" % shape)
_, u, _ = tf.svd(tf.random_normal(shape, dtype=dtype), full_matrices=True)
return u
def _reverse(input_, seq_lengths, seq_dim, batch_dim): # reverses sequences with right-padding correctly
return array_ops.reverse_sequence(
input=input_, seq_lengths=seq_lengths,
seq_dim=seq_dim, batch_dim=batch_dim)
def bilstms_interleaved(inputs, num_layers, size, keep_prob, lengths):
outputs = inputs
print('interleaved')
for layer in range(num_layers):
direction = 'backw.' if layer % 2 else 'forw.'
print('Layer {}: Creating {} LSTM'.format(layer, direction)) # backwards if layer odd
with tf.variable_scope('{}_lstm_{}'.format(direction, layer)):
# cell
cell = HighwayWrapper(DropoutWrapper(LSTMCell(size),
variational_recurrent=True,
dtype=tf.float32,
state_keep_prob=keep_prob))
# calc either bw or fw - interleaving is done at graph construction (not runtime)
if direction == 'backw.':
outputs_reverse = _reverse(outputs, seq_lengths=lengths, seq_dim=1, batch_dim=0)
tmp, _ = tf.nn.dynamic_rnn(cell=cell,
inputs=outputs_reverse,
sequence_length=lengths,
dtype=tf.float32)
outputs = _reverse(tmp, seq_lengths=lengths, seq_dim=1, batch_dim=0)
else:
outputs, _ = tf.nn.dynamic_rnn(cell=cell,
inputs=outputs,
sequence_length=lengths,
dtype=tf.float32)
return outputs
class Model():
def __init__(self, config, embeddings, num_labels, g):
# embedding
with tf.device('/cpu:0'):
self.word_ids = tf.placeholder(tf.int32, [None, None], name='word_ids')
embedded = tf.nn.embedding_lookup(embeddings, self.word_ids, name='embedded')
# stacked bilstm
with tf.device('/gpu:0'):
self.predicate_ids = tf.placeholder(tf.float32, [None, None], name='predicate_ids')
self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')
self.lengths = tf.placeholder(tf.int32, [None], name='lengths')
inputs = tf.concat([embedded, tf.expand_dims(self.predicate_ids, -1)], axis=2, name='lstm_inputs')
final_outputs = bilstms_interleaved(inputs,
config.num_layers,
config.cell_size,
self.keep_prob,
self.lengths)
# projection
shape0 = tf.shape(final_outputs)[0] * tf.shape(final_outputs)[1] # both batch_size and seq_len are dynamic
final_outputs_2d = tf.reshape(final_outputs, [shape0, config.cell_size], name='final_outputs_2d')
wy = tf.get_variable('Wy', [config.cell_size, num_labels])
by = tf.get_variable('by', [num_labels])
logits = tf.nn.xw_plus_b(final_outputs_2d, wy, by, name='logits') # need [shape0, num_labels]
# loss
self.label_ids = tf.placeholder(tf.int32, [None, None], name='label_ids') # [batch_size, max_seq_len]
label_ids_flat = tf.reshape(self.label_ids, [-1]) # need [shape0]
mask = tf.greater(label_ids_flat, 0, 'mask')
self.nonzero_label_ids_flat = tf.boolean_mask(label_ids_flat, mask,
name='nonzero_label_ids_flat') # removes elements
nonzero_logits = tf.boolean_mask(logits, mask, name='nonzero_logits')
nonzero_losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=nonzero_logits,
labels=self.nonzero_label_ids_flat,
name='nonzero_losses')
self.nonzero_mean_loss = tf.reduce_mean(nonzero_losses, name='nonzero_mean_loss')
# update
optimizer = tf.train.AdadeltaOptimizer(learning_rate=config.learning_rate, rho=0.95,epsilon=config.epsilon)
gradients, variables = zip(*optimizer.compute_gradients(self.nonzero_mean_loss))
gradients, _ = tf.clip_by_global_norm(gradients, config.max_grad_norm)
self.update = optimizer.apply_gradients(zip(gradients, variables), name='update')
# predictions
self.nonzero_predicted_label_ids = tf.cast(tf.argmax(tf.nn.softmax(nonzero_logits), axis=1), tf.int32,
name='nonzero_predicted_label_ids')
# tensorboard
tf.summary.scalar('nonzero_accuracy', tf.reduce_mean(tf.cast(tf.equal(self.nonzero_predicted_label_ids,
self.nonzero_label_ids_flat),
tf.float32)))
tf.summary.scalar('nonzero_mean_xe', self.nonzero_mean_loss)
self.scalar_summaries = tf.summary.merge_all()
p = Path("/content/drive/My Drive/DLSRL/Tensorboard log")
self.train_writer = tf.summary.FileWriter(str(p), g)
# confusion matrix
nonzero_cm = tf.confusion_matrix(self.nonzero_label_ids_flat, self.nonzero_predicted_label_ids)
size = tf.shape(nonzero_cm)[0]
self.cm_summary = tf.summary.image('nonzero_cm', tf.reshape(tf.cast(nonzero_cm, tf.float32),
[1, size, size, 1])) # needs 4d
我正在尝试使用tensorflow实现语义角色标签。在运行模型时，尽管没有必要调整超参数，但由于在论文中已进行了说明，并且在检查张量板图模型是否正确时，仍会出现此错误和性能下降。另一个问题是，加载检查点时出现错误，并且错误与无法找到/加载IndexedSlices有关。因此，我认为此警告正在给模型造成问题，但我无法弄清原因。我应该如何修改代码以使其起作用并删除该警告？