tensorflow 报错内容如下 这是什么问题
class Encoder(tf.keras.Model) :
def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz) :
super(Encoder, self).__init__()
self.batch_sz = batch_sz
self.enc_units = enc_units
self.embedding = keras.layers.Embedding(vocab_size, embedding_dim)
self.gru = keras.layers.GRU(self.enc_units, return_sequences=True, return_state=True, recurrent_initializer="glorot_uniform")
@tf.function
def call(self, x, hidden) :
x = self.embedding(x)
output, state = self.gru(x, initial_state=hidden)
return output, state
def initialize_hidden_state(self) :
return tf.zeros((self.batch_sz, self.enc_units))
class BahdanauAttentionMechanism(tf.keras.layers.Layer) :
def __init__(self, units) :
super(BahdanauAttentionMechanism, self).__init__()
self.W1 = layers.Dense(units)
self.W2 = layers.Dense(units)
self.V = layers.Dense(1)
@tf.function
def call(self, query, values) :
hidden_with_time_axis = tf.expand_dims(query, 1)
score = self.V(tf.nn.tanh(self.W1(values) + self.W2(hidden_with_time_axis)))
attention_weights = tf.nn.softmax(score, axis=1)
context_vector = attention_weights * values
context_vector = tf.math.reduce_sum(context_vector, axis=1)
return context_vector, attention_weights
class Decoder(tf.keras.Model) :
def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz) :
super(Decoder, self).__init__()
self.batch_sz = batch_sz
self.dec_units = dec_units
self.embedding = layers.Embedding(vocab_size, embedding_dim)
self.gru = layers.GRU(self.dec_units, return_sequences=True, return_state=True, recurrent_initializer="glorot_uniform")
self.fc = layers.Dense(vocab_size)
self.attention = BahdanauAttentionMechanism(self.dec_units)
@tf.function
def call(self, x, hidden, enc_output) :
context_vector, attention_weights = self.attention(hidden, enc_output)
x = self.embedding(x)
x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)
output, state = self.gru(x)
output = tf.reshape(output, (-1, output.shape[2]))
x = self.fc(output)
return x, state, attention_weights
Current allocation summary follows.
Current allocation summary follows.
2023-04-13 17:02:03.448253: W tensorflow/core/framework/op_kernel.cc:1780] OP_REQUIRES failed at matmul_op_impl.h:728 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[64,1024] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
2023-04-13 17:02:03.449923: W tensorflow/core/common_runtime/bfc_allocator.cc:491] ***************************************************************************************************x
2023-04-13 17:02:03.450152: W tensorflow/core/framework/op_kernel.cc:1780] OP_REQUIRES failed at resource_variable_ops.cc:725 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[64,1,256] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Traceback (most recent call last):
File "C:\Users\DELL\Desktop\h\p1.py", line 326, in
train_model(q_hidden, encoder, decoder, q_index, BATCH_SIZE, dataset, steps_per_epoch, optimizer, checkpoint, checkpoint_prefix, summary_writer)
File "C:\Users\DELL\Desktop\h\p1.py", line 208, in train_model
batch_loss = optimizer_loss(q, a, q_hidden, encoder, decoder, q_index, BATCH_SIZE, optimizer)
File "C:\Users\DELL\Desktop\h\p1.py", line 189, in optimizer_loss
batch_loss, grads = grad_loss(q, a, q_hidden, encoder, decoder, q_index, BATCH_SIZE)
File "C:\Users\DELL\Desktop\h\p1.py", line 175, in grad_loss
predictions, a_hidden, _ = decoder(a_input, a_hidden, q_output)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\tensorflow\python\eager\execute.py", line 54, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.ResourceExhaustedError: Exception encountered when calling layer "decoder" " f"(type Decoder).
Graph execution error:
Detected at node 'dense_5/Tensordot/MatMul' defined at (most recent call last):
File "C:\Users\DELL\Desktop\h\p1.py", line 319, in
a_output, _, _ = decoder(tf.random.uniform((64, 1)), q_hidden, q_output)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\engine\training.py", line 557, in __call__
return super().__call__(*args, **kwargs)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\engine\base_layer.py", line 1097, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "C:\Users\DELL\Desktop\h\p1.py", line 138, in call
context_vector, attention_weights = self.attention(hidden, enc_output)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\engine\base_layer.py", line 1097, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "C:\Users\DELL\Desktop\h\p1.py", line 109, in call
score = self.V(tf.nn.tanh(self.W1(values) + self.W2(hidden_with_time_axis)))
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\engine\base_layer.py", line 1097, in __call__
outputs = call_fn(inputs, *args, **kwargs)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\utils\traceback_utils.py", line 96, in error_handler
return fn(*args, **kwargs)
File "C:\Users\DELL\AppData\Roaming\Python\Python310\site-packages\keras\layers\core\dense.py", line 244, in call
outputs = tf.tensordot(inputs, self.kernel, [[rank - 1], [0]])
Node: 'dense_5/Tensordot/MatMul'
OOM when allocating tensor with shape[64,1024] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node dense_5/Tensordot/MatMul}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info. This isn't available when running in Eager mode.
[Op:__forward_call_3520]
Call arguments received by layer "decoder" " f"(type Decoder):
• x=tf.Tensor(shape=(64, 1), dtype=int32)
• hidden=tf.Tensor(shape=(64, 1024), dtype=float32)
• enc_output=tf.Tensor(shape=(64, 74, 1024), dtype=float32)
这个错误提示是说在计算过程中，尝试在GPU上分配一个shape为[64,1024]的float型tensor时内存不足，导致程序崩溃。同样的原因也导致了另一个位置出现了类似的错误提示。
解决这个问题的思路有以下几种：
1.减小batch_size
减小batch_size可以减少每次计算需要的内存，从而避免内存不足的情况。不过这样可能会降低训练效率。
2.减小模型参数量
减小模型参数量可以减少每次计算需要的内存，可以通过使用更小的embedding_dim或者减小模型层数等方式实现。但是这样会牺牲模型的表达能力。
3.将模型移动到CPU上计算
如果GPU内存真的非常有限，可以考虑将模型移动到CPU上计算。不过这样会大大降低训练速度，需谨慎考虑。
4.增加GPU显存
如果以上方法都不能解决问题，可以考虑增加GPU显存。这可以通过更换显卡或者在代码中调用tf.config.experimental.set_memory_growth方法来实现。
下面是增加显存的示例代码：
import tensorflow as tf
gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=0.7)
sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
tf.compat.v1.keras.backend.set_session(sess)
将这段代码加入到你的程序中，并尝试减小per_process_gpu_memory_fraction的值，看是否能解决问题。
应该是爆显存了，你检查下。
OOM（out of memory）错误,分配GPU内存问题，导致TensorFlow无法继续执行，最终导致内存不足
办法：
1、减小batch size的值，减少内存压力
2、减小模型大小（隐藏单元数）
3、禁用GPU并使用CPU运行代码，这会降低速度，但内存压力会减小
4、增加内存
5、重新启动Python内核和TensorFlow会话，看人品会内存泄漏导致内存不足
你的计算机内存不支持一次训练那么多图片，把batchsize调小一点吧，另外训练时你可以使用任务管理器看你电脑的内存占用，找一个合适你电脑的batchsize。
引用chatGPT作答，
这个错误提示表明，当您的 TensorFlow 代码尝试分配张量时，GPU 内存不足。具体地，错误信息显示 TensorFlow 尝试在 GPU:0 上分配大小为 [64,1024]、类型为 float 的张量，但没有足够的内存可用。同样，它还尝试在 GPU:0 上分配大小为 [64,1,256]、类型为 float 的张量，但是内存不足。
为了解决这个错误，您可以尝试以下一项或多项措施：
1.减小模型的大小：您可以通过使用更小的词汇表大小、嵌入维度或隐藏层大小来减少模型参数的数量。
2.减小批量大小：您可以尝试减小批量大小，以减少每个批次的内存使用量。
3.使用更大的 GPU：如果您可以使用具有更多内存的更大的 GPU，您可以尝试在该 GPU 上运行代码。
4.使用混合精度：您可以尝试使用混合精度训练，这允许您使用低精度的浮点数来减少模型的内存使用量。
5.使用梯度检查点：您可以尝试使用梯度检查点，通过在反向传播期间重新计算中间激活值，以换取计算时间来减少内存使用量。当您的大型模型无法放入内存时，这种技术可能非常有用。
6.减小序列长度：您可以尝试减小输入或输出的序列长度，以减少模型的内存使用量。
这个问题看起来是代码中断了，可以尝试检查代码中是否有拼写错误或者缺失的符号。具体来说，检查Encoder类的构造函数是否完整，包括embeddi后面是否缺失了ng_dim这个参数。另外，也可以检查一下是否正确导入了tensorflow和keras库。