在我的代码中添加一些tf.summary之后，我再次运行代码时出现以下错误：
tensorflow.python.framework.errors_impl.__InvalidArgumentError__:
You must feed a value for placeholder tensor 'Valid/Model/input/input_data_pl' with dtype int32 and shape [?,?,?]
关于我的摘要有什么问题的任何想法？
/Users/wuguohua/Envs/py3tf/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
return f(*args, **kwds)
Tensor("Train/Model/input/input_data_pl:0", shape=(?, ?, ?), dtype=int32)
Tensor("Train/Model/input/output_data_pl:0", shape=(?, ?), dtype=int32)
[TL] EmbeddingInputlayer Model/input/token_emb: (1178, 100)
/Users/wuguohua/Envs/py3tf/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:96: 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. "
Tensor("Valid/Model/input/input_data_pl:0", shape=(?, ?, ?), dtype=int32)
Tensor("Valid/Model/input/output_data_pl:0", shape=(?, ?), dtype=int32)
[TL] EmbeddingInputlayer Model/input/token_emb: (1178, 100)
2018-02-09 21:31:08.723167: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
Traceback (most recent call last):
File "/Users/wuguohua/Envs/py3tf/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1323, in _do_call
return fn(*args)
File "/Users/wuguohua/Envs/py3tf/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1302, in _run_fn
status, run_metadata)
File "/Users/wuguohua/Envs/py3tf/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Valid/Model/input/input_data_pl' with dtype int32 and shape [?,?,?]
[[Node: Valid/Model/input/input_data_pl = Placeholder[dtype=DT_INT32, shape=[?,?,?], _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
During handling of the above exception, another exception occurred:
我的代码：
class SegmentModel(object):
def __init__(self, is_training, config):
self.is_training = is_training
self.config = config
embed_size, pre_win, sur_win, rnn_hidden = \
config.embed_size, config.pre_win, \
config.sur_win, config.rnn_hidden_size
vocab_size, tag_size = config.vocab_size, config.tag_size
with tf.variable_scope("input"):
self.input_data = tf.placeholder(tf.int32, shape=[None, None, None],
name="input_data_pl")
self.output_data = tf.placeholder(tf.int32, shape=[None, None],
name="output_data_pl")
print(self.input_data)
print(self.output_data)
seq_length = tf.count_nonzero(tf.reduce_max(self.input_data, axis=2),
axis=1)
# check if the seq length right
self.seq_length = seq_length
token_emb_net = tl.layers.EmbeddingInputlayer(
self.input_data, vocab_size, embed_size, name="token_emb")
tok_emb_shape = tf.shape(token_emb_net.outputs)
win_size = pre_win + sur_win + 1
win_emb_size = win_size * embed_size
batch_size = tok_emb_shape[0]
token_emb_net.outputs = tf.reshape(token_emb_net.outputs,
(tok_emb_shape[0],
tok_emb_shape[1],
win_emb_size))
with tf.variable_scope("sentmodel"):
input_keep_prob = 1.0 - config.dropout if is_training else 1.0
rnn_cell = self._get_rnn_cell(is_training, rnn_hidden,
config.rnn_hidden_layer,
input_keep_prob,
1.0)
rnn_outputs, _ = tf.nn.dynamic_rnn(
cell=rnn_cell,
inputs=token_emb_net.outputs,
sequence_length=seq_length,
dtype=tf.float32,
)
rnn_outputs = tf.reshape(rnn_outputs, [-1, rnn_hidden])
with tf.variable_scope("softmax"):
softmax_w = tf.get_variable("w", shape=[rnn_hidden, tag_size])
softmax_b = tf.get_variable("b", shape=[tag_size])
logits = tf.nn.xw_plus_b(rnn_outputs, softmax_w, softmax_b)
logits = tf.reshape(logits, [tok_emb_shape[0],
tok_emb_shape[1],
tag_size])
self.logits = logits
with tf.variable_scope("crf"):
log_likelihood, trans_params = tf.contrib.crf.crf_log_likelihood(
logits,
tag_indices=self.output_data,
sequence_lengths=seq_length)
cost = tf.reduce_mean(-log_likelihood)
self.cost = cost
self.trans_params = trans_params
if not is_training:
pass
# self.decode_sequence = SegmentModel._predict_op(
# logits, trans_params, seq_length)
else:
with tf.variable_scope("optim"):
optimizer = tf.train.GradientDescentOptimizer(0.2)
tvars = tf.trainable_variables()
grad = tf.gradients(self.cost, tvars)
self.train_op = optimizer.apply_gradients(
zip(grad, tvars),
global_step=tf.train.get_or_create_global_step()
)
self.global_step = tf.train.get_or_create_global_step()
def main(_):
train_reader, dev_reader = prepare()
config = ModelConfig()
config.vocab_size = train_reader.word_vocab.size()
config.tag_size = train_reader.tag_vocab.size()
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.Graph().as_default():
with tf.variable_scope("Model", reuse=None):
train_model = SegmentModel(is_training=True, config=config)
tf.summary.scalar("Training Loss", train_model.cost)
with tf.variable_scope("Model", reuse=True):
tl.layers.set_name_reuse(True)
dev_model = SegmentModel(is_training=False, config=config)
tf.summary.scalar("Validation Loss", dev_model.cost)
# MonitoredTrainingSession will auto save check point
# and save the checkpoint after all epoch finish
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
config=tf_config) as mon_sess:
for epoch in range(config.max_max_epoch):
train_model.train(mon_sess, train_reader, config.batch_size)
dev_model.eval(mon_sess, dev_reader, config.batch_size)
if __name__ == "__main__":
tf.app.run()
答案 0 :(得分：0)
错误与摘要无关。您提供的代码中遗漏了很多东西，所以它有点难以辨别，但是tf.app.run甚至没有调用main函数。 / p>
其次，没有调用MonitoredSession.run接受feed_dict参数，这是一个dict，它提供Valid/Model/input/input_data_pl张量（因此错误）。
最后，您应该在定义张量的位置定义摘要 - 在相同的图形和变量空间中，如：
with tf.variable_scope("crf"):
log_likelihood, trans_params = tf.contrib.crf.crf_log_likelihood(
logits,
tag_indices=self.output_data,
sequence_lengths=seq_length)
cost = tf.reduce_mean(-log_likelihood)
===
tf.summary.scalar(name="Training Loss", tensor=cost)
===
self.cost = cost
self.trans_params = trans_params