我的graph看起来像这样
with graph.as_default():
train_inputs = tf.placeholder(tf.int32, shape=[None, None])
with tf.device('/cpu:0'):
embeddings = tf.Variable(tf.zeros([vocab_size, options.embed_size]))
restorer = tf.train.Saver({'embeddings': embeddings})
init = tf.variables_initializer([embeddings])
uninit = tf.report_uninitialized_variables()
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
# length() returns a [batch_szie,] tensor of true lengths of sentences (lengths before zero-padding)
sequence_length = length(embed)
lstm = tf.nn.rnn_cell.LSTMCell(options.rnn_size)
output, _ = tf.nn.dynamic_rnn(
lstm,
embed,
dtype=tf.float32,
swequence_length=sequence_length
)
我的session：
with tf.Session(graph=graph) as session:
restorer.restore(session, options.restore_path)
# tf.global_variables_initializer.run()
init.run()
print session.run([uninit])
while len(data.ids):
# data.generate_batch returns a list of size [batch_size, max_length], and zero-padding is used, when the sentences are shorter than max_length. For example, batch_inputs = [[1,2,3,4], [3,2,1,0], [1,2,0,0]]
batch_inputs, _ = data.generate_batch(options.batch_size)
feed_dict = {train_inputs: batch_inputs}
test = session.run([tf.shape(output)], feed_dict=feed_dict)
print test
功能length()：
def length(self, sequence):
length = tf.sign(sequence)
length = tf.reduce_sum(length, reduction_indices=1)
length = tf.cast(length, tf.int32)
return length
我得到的错误：
Traceback (most recent call last):
File "rnn.py", line 103, in <module>
test = session.run([tf.shape(output)], feed_dict=feed_dict)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 766, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 964, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1014, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1034, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value RNN/LSTMCell/W_0
[[Node: RNN/LSTMCell/W_0/read = Identity[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](RNN/LSTMCell/W_0)]]
Caused by op u'RNN/LSTMCell/W_0/read', defined at:
File "rnn.py", line 75, in <module>
sequence_length=sequence_length,
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn.py", line 845, in dynamic_rnn
dtype=dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn.py", line 1012, in _dynamic_rnn_loop
swap_memory=swap_memory)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 2636, in while_loop
result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 2469, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 2419, in _BuildLoop
body_result = body(*packed_vars_for_body)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn.py", line 995, in _time_step
skip_conditionals=True)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn.py", line 403, in _rnn_step
new_output, new_state = call_cell()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn.py", line 983, in <lambda>
call_cell = lambda: cell(input_t, state)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell.py", line 496, in __call__
dtype, self._num_unit_shards)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell.py", line 329, in _get_concat_variable
sharded_variable = _get_sharded_variable(name, shape, dtype, num_shards)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/rnn_cell.py", line 359, in _get_sharded_variable
dtype=dtype))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 1024, in get_variable
custom_getter=custom_getter)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 850, in get_variable
custom_getter=custom_getter)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 346, in get_variable
validate_shape=validate_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 331, in _true_getter
caching_device=caching_device, validate_shape=validate_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variable_scope.py", line 677, in _get_single_variable
expected_shape=shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 224, in __init__
expected_shape=expected_shape)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/variables.py", line 367, in _init_from_args
self._snapshot = array_ops.identity(self._variable, name="read")
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 1424, in identity
result = _op_def_lib.apply_op("Identity", input=input, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
self._traceback = _extract_stack()
FailedPreconditionError (see above for traceback): Attempting to use uninitialized value RNN/LSTMCell/W_0
[[Node: RNN/LSTMCell/W_0/read = Identity[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](RNN/LSTMCell/W_0)]]
然而，当我打印出未初始化的变量时，我得到了[array([], dtype=object)]
当我用init.run()替换tf.global_variables_initializer.run()时，它有效。
知道为什么init.run()不起作用？
答案 0 :(得分：0)
您定义init如下：
init = tf.variables_initializer([embeddings])
此定义表示init仅初始化embeddings变量。调用tf.nn.dynamic_rnn()函数会创建更多变量，表示LSTM中的各种内部权重，而这些变量不会由init初始化。
相比之下，tf.global_variables_initializer()返回一个操作，该操作在运行时将初始化模型中的所有（全局）变量，包括为LSTM创建的变量。