我有以下代码
flags = tf.flags
logging = tf.logging
flags.DEFINE_string('model', 'small',
'A type of model. Possible options are: small, medium, large.'
)
flags.DEFINE_string('data_path', None, 'data_path')
flags.DEFINE_string('checkpoint_dir', 'ckpt', 'checkpoint_dir')
flags.DEFINE_bool('use_fp16', False,
'Train using 16-bit floats instead of 32bit floats')
flags.DEFINE_bool('train', False, 'should we train or test')
FLAGS = flags.FLAGS
def data_type():
return tf.float16 if FLAGS.use_fp16 else tf.float32
class PTBModel(object):
"""The PTB model."""
def __init__(self, is_training, config):
self.batch_size = batch_size = config.batch_size
self.num_steps = num_steps = config.num_steps
size = config.hidden_size
vocab_size = config.vocab_size
self._input_data = tf.placeholder(tf.float32, [batch_size,
num_steps])
self._targets = tf.placeholder(tf.int32, [batch_size,
num_steps])
# Slightly better results can be obtained with forget gate biases
# initialized to 1 but the hyperparameters of the model would need to be
# different than reported in the paper.
lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(size, forget_bias=0.0,
state_is_tuple=True)
if is_training and config.keep_prob < 1:
lstm_cell = tf.nn.rnn_cell.DropoutWrapper(lstm_cell,
output_keep_prob=config.keep_prob)
cell = tf.nn.rnn_cell.MultiRNNCell([lstm_cell]
* config.num_layers, state_is_tuple=True)
self._initial_state = cell.zero_state(batch_size, data_type())
with tf.device('/cpu:0'):
embedding = tf.get_variable('embedding', [vocab_size,
size], dtype=data_type())
inputs = tf.nn.embedding_lookup(embedding, self._input_data)
if is_training and config.keep_prob < 1:
inputs = tf.nn.dropout(inputs, config.keep_prob)
# Simplified version of tensorflow.models.rnn.rnn.py's rnn().
# This builds an unrolled LSTM for tutorial purposes only.
# In general, use the rnn() or state_saving_rnn() from rnn.py.
#
# The alternative version of the code below is:
#
# from tensorflow.models.rnn import rnn
inputs = [tf.squeeze(input_, [1]) for input_ in tf.split(inputs, num_steps, axis=1)]
(outputs, state) = tf.nn.rnn(cell, inputs, initial_state=self._initial_state)
# outputs = []
# state = self._initial_state
# with tf.variable_scope("RNN"):
# for time_step in range(num_steps):
# if time_step > 0: tf.get_variable_scope().reuse_variables()
# (cell_output, state) = cell(inputs[:, time_step, :], state)
# outputs.append(cell_output)
output = tf.reshape(tf.concat(outputs, axis=1), [-1, size])
softmax_w = tf.get_variable('softmax_w', [size, vocab_size],
dtype=data_type())
softmax_b = tf.get_variable('softmax_b', [vocab_size],
dtype=data_type())
logits = tf.matmul(output, softmax_w) + softmax_b
loss = tf.nn.seq2seq.sequence_loss_by_example([logits],
[tf.reshape(self._targets, [-1])], [tf.ones([batch_size
* num_steps],
dtype=data_type())])
self._cost = cost = tf.reduce_sum(loss) / batch_size
self._final_state = state
# RANI
self.logits = logits
if not is_training:
return
self._lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
(grads, _) = tf.clip_by_global_norm(tf.gradients(cost, tvars),
config.max_grad_norm)
optimizer = tf.train.GradientDescentOptimizer(self._lr)
self._train_op = optimizer.apply_gradients(zip(grads, tvars))
self._new_lr = tf.placeholder(tf.float32, shape=[],
name='new_learning_rate')
self._lr_update = tf.assign(self._lr, self._new_lr)
def assign_lr(self, session, lr_value):
session.run(self._lr_update, feed_dict={self._new_lr: lr_value})
...
但是，当我运行它时，我会收到以下错误
File "ptb_word_lm.py", line 349, in <module>
tf.app.run()
File "C:\Users\Josh Goldman\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\platform\app.py", line 48, in run
_sys.exit(main(_sys.argv[:1] + flags_passthrough))
File "ptb_word_lm.py", line 299, in main
m = PTBModel(is_training=True, config=config)
File "ptb_word_lm.py", line 60, in __init__
inputs = tf.nn.embedding_lookup(embedding, self._input_data)
File "C:\Users\Josh Goldman\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\embedding_ops.py", line 122, in embedding_lookup
return maybe_normalize(_do_gather(params[0], ids, name=name))
File "C:\Users\Josh Goldman\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\embedding_ops.py", line 42, in _do_gather
return array_ops.gather(params, ids, name=name)
File "C:\Users\Josh Goldman\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 1179, in gather
validate_indices=validate_indices, name=name)
File "C:\Users\Josh Goldman\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 589, in apply_op
param_name=input_name)
File "C:\Users\Josh Goldman\AppData\Local\Programs\Python\Python35\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 60, in _SatisfiesTypeConstraint
", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
TypeError: Value passed to parameter 'indices' has DataType float32 not in list of allowed values: int32, int64
有人，请帮助我。我将所有软件包升级到最新版本。我正在使用正确的翻译。如果错误非常简单，我很抱歉。我只有13岁，对编程很新。顺便说一句，这段代码不是我的;我是从Github那里得到的。
答案 0 :(得分：1)
错误是由tensorflow版本引起的，tf.split的语法在较新版本中已更改。 tf.concat
# replace this line with the following one
inputs = [tf.squeeze(input_, [1]) for input_ in tf.split(1, num_steps, inputs)]
# this support `tensorflow >= 1.0.0`
inputs = [tf.squeeze(input_, [1]) for input_ in tf.split(inputs, num_steps, axis=1)]
# Also use dtype float32 for inputs
self._input_data = tf.placeholder(tf.float32, [batch_size,
num_steps])
# replace this line
output = tf.reshape(tf.concat(1, outputs), [-1, size])
# with this one
output = tf.reshape(tf.concat(outputs, axis=1), [-1, size])