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我正在尝试使用张量流来训练序列模型。我在教程中看到，桶有助于加速培训。到目前为止，我只能使用一个桶进行训练，并且只使用一个gpu和多个桶来使用或多或少的开箱即用代码，但是当我尝试使用具有多个gpus的多个桶时，出现错误，指出 参数无效：您必须养活占位符张量“gpu_scope_0/encoder50_gpu0”与D型的值INT32带桶的Tensorflow错误
从错误中，我可以告诉大家，我不是正确声明input_feed，所以它期待的输入是的每次最大桶的大小。不过，我很困惑为什么会出现这种情况，因为在我正在调整的示例中，它在初始化input_feed的占位符时会执行相同的操作。据我所知，教程也初始化为最大容量的桶，但是当我使用教程的代码时，这个错误不会发生。
以下是我认为是相关的初始化代码：
self.encoder_inputs = [[] for _ in xrange(self.num_gpus)]
self.decoder_inputs = [[] for _ in xrange(self.num_gpus)]
self.target_weights = [[] for _ in xrange(self.num_gpus)]
self.scope_prefix = "gpu_scope"
for j in xrange(self.num_gpus):
with tf.device("/gpu:%d" % (self.gpu_offset + j)):
with tf.name_scope('%s_%d' % (self.scope_prefix, j)) as scope:
for i in xrange(buckets[-1][0]): # Last bucket is the biggest one.
self.encoder_inputs[j].append(tf.placeholder(tf.int32, shape=[None],
name="encoder{0}_gpu{1}".format(i,j)))
for i in xrange(buckets[-1][1] + 1):
self.decoder_inputs[j].append(tf.placeholder(tf.int32, shape=[None],
name="decoder{0}_gpu{1}".format(i,j)))
self.target_weights[j].append(tf.placeholder(tf.float32, shape=[None],
name="weight{0}_gpu{1}".format(i,j)))
# Our targets are decoder inputs shifted by one.
self.losses = []
self.outputs = []
# The following loss computation creates the neural network. The specified
# device hosts the trainable tf parameters.
bucket = buckets[0]
i = 0
with tf.device(param_device):
output, loss = tf.nn.seq2seq.model_with_buckets(self.encoder_inputs[i], self.decoder_inputs[i],
[self.decoder_inputs[i][k + 1] for k in
xrange(len(self.decoder_inputs[i]) - 1)],
self.target_weights[0], buckets,
lambda x, y: seq2seq_f(x, y, True),
softmax_loss_function=self.softmax_loss_function)
bucket = buckets[0]
self.encoder_states = []
with tf.device('/gpu:%d' % self.gpu_offset):
with variable_scope.variable_scope(variable_scope.get_variable_scope(),
reuse=True):
self.encoder_outputs, self.encoder_states = get_encoder_outputs(self,
self.encoder_inputs[0])
if not forward_only:
self.grads = []
print ("past line 297")
done_once = False
for i in xrange(self.num_gpus):
with tf.device("/gpu:%d" % (self.gpu_offset + i)):
with tf.name_scope("%s_%d" % (self.scope_prefix, i)) as scope:
with variable_scope.variable_scope(variable_scope.get_variable_scope(), reuse=True):
#for j, bucket in enumerate(buckets):
output, loss = tf.nn.seq2seq.model_with_buckets(self.encoder_inputs[i],
self.decoder_inputs[i],
[self.decoder_inputs[i][k + 1] for k in
xrange(len(self.decoder_inputs[i]) - 1)],
self.target_weights[i], buckets,
lambda x, y: seq2seq_f(x, y, True),
softmax_loss_function=self.softmax_loss_function)
self.losses.append(loss)
self.outputs.append(output)
# Training outputs and losses.
if forward_only:
self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(
self.encoder_inputs, self.decoder_inputs,
[self.decoder_inputs[0][k + 1] for k in xrange(buckets[0][1])],
self.target_weights, buckets, lambda x, y: seq2seq_f(x, y, True),
softmax_loss_function=self.softmax_loss_function)
# If we use output projection, we need to project outputs for decoding.
if self.output_projection is not None:
for b in xrange(len(buckets)):
self.outputs[b] = [
tf.matmul(output, self.output_projection[0]) + self.output_projection[1]
for output in self.outputs[b]
]
else:
self.bucket_grads = []
self.gradient_norms = []
params = tf.trainable_variables()
opt = tf.train.GradientDescentOptimizer(self.learning_rate)
self.updates = []
with tf.device(aggregation_device):
for g in xrange(self.num_gpus):
for b in xrange(len(buckets)):
gradients = tf.gradients(self.losses[g][b], params)
clipped_grads, norm = tf.clip_by_global_norm(gradients, max_gradient_norm)
self.gradient_norms.append(norm)
self.updates.append(
opt.apply_gradients(zip(clipped_grads, params), global_step=self.global_step))
，并在数据喂奶时，以下是相关代码：眼下
input_feed = {}
for i in xrange(self.num_gpus):
for l in xrange(encoder_size):
input_feed[self.encoder_inputs[i][l].name] = encoder_inputs[i][l]
for l in xrange(decoder_size):
input_feed[self.decoder_inputs[i][l].name] = decoder_inputs[i][l]
input_feed[self.target_weights[i][l].name] = target_weights[i][l]
# Since our targets are decoder inputs shifted by one, we need one more.
last_target = self.decoder_inputs[i][decoder_size].name
input_feed[last_target] = np.zeros([self.batch_size], dtype=np.int32)
last_weight = self.target_weights[i][decoder_size].name
input_feed[last_weight] = np.zeros([self.batch_size], dtype=np.float32)
# Output feed: depends on whether we do a backward step or not.
if not forward_only:
output_feed = [self.updates[bucket_id], self.gradient_norms[bucket_id], self.losses[bucket_id]]
else:
output_feed = [self.losses[bucket_id]] # Loss for this batch.
for l in xrange(decoder_size): # Output logits.
output_feed.append(self.outputs[0][l])
我只考虑填充每个输入达到桶的大小，但我认为这会失去一些桶装的优点