RuntimeError: all elements of input should be between 0 and 1
RuntimeError: all elements of input should be between 0 and 1
我想在蛋白质嵌入上使用带有 bilstm 层的 RNN，使用 pytorch。它适用于线性层，但当我使用 Bilstm 时出现运行时错误。对不起，如果它不清楚这是我的第一篇文章，如果有人能帮助我，我将不胜感激。
from collections import Counter, OrderedDict
from typing import Optional
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F # noqa
from deepchain import log
from sklearn.model_selection import train_test_split
from sklearn.utils.class_weight import compute_class_weight
from torch import Tensor, nn
num_layers=2
hidden_size=256
from torch.utils.data import DataLoader, TensorDataset
def classification_dataloader_from_numpy(
x: np.ndarray, y: np.array, batch_size: int = 32
) -> DataLoader:
"""Build a dataloader from numpy for classification problem
This dataloader is use only for classification. It detects automatically the class of
the problem (binary or multiclass classification)
Args:
x (np.ndarray): [description]
y (np.array): [description]
batch_size (int, optional): [description]. Defaults to None.
Returns:
DataLoader: [description]
"""
n_class: int = len(np.unique(y))
if n_class > 2:
log.info("This is a classification problem with %s classes", n_class)
else:
log.info("This is a binary classification problem")
# y is float for binary classification, int for multiclass
y_tensor = torch.tensor(y).long() if len(np.unique(y)) > 2 else torch.tensor(y).float()
tensor_set = TensorDataset(torch.tensor(x).float(), y_tensor)
loader = DataLoader(tensor_set, batch_size=batch_size)
return loader
class RNN(pl.LightningModule):
"""A `pytorch` based deep learning model"""
def __init__(self, input_shape: int, n_class: int, num_layers, n_neurons: int = 128, lr: float = 1e-3):
super(RNN,self).__init__()
self.lr = lr
self.n_neurons=n_neurons
self.num_layers=num_layers
self.input_shape = input_shape
self.output_shape = 1 if n_class <= 2 else n_class
self.activation = nn.Sigmoid() if n_class <= 2 else nn.Softmax(dim=-1)
self.lstm = nn.LSTM(self.input_shape, self.n_neurons, num_layers, batch_first=True, bidirectional=True)
self.fc= nn.Linear(self.n_neurons, self.output_shape)
def forward(self, x):
h0=torch.zeros(self.num_layers, x_size(0), self.n_neurons).to(device)
c0=torch.zeros(self.num_layers, x_size(0), self.n_neurons).to(device)
out, _=self.lstm(x,(h0, c0))
out=self.fc(out[:, -1, :])
return self.fc(x)
def training_step(self, batch, batch_idx):
"""training_step defined the train loop. It is independent of forward"""
x, y = batch
y_hat = self.fc(x).squeeze()
y = y.squeeze()
if self.output_shape > 1:
y_hat = torch.log(y_hat)
loss = self.loss(y_hat, y)
self.log("train_loss", loss, on_epoch=True, on_step=False)
return {"loss": loss}
def validation_step(self, batch, batch_idx):
"""training_step defined the train loop. It is independent of forward"""
x, y = batch
y_hat = self.fc(x).squeeze()
y = y.squeeze()
if self.output_shape > 1:
y_hat = torch.log(y_hat)
loss = self.loss(y_hat, y)
self.log("val_loss", loss, on_epoch=True, on_step=False)
return {"val_loss": loss}
def configure_optimizers(self):
"""(Optional) Configure training optimizers."""
return torch.optim.Adam(self.parameters(),lr=self.lr)
def compute_class_weight(self, y: np.array, n_class: int):
"""Compute class weight for binary/multiple classification
If n_class=2, only compute weights for the positve class.
If n>2, compute for all classes.
Args:
y ([np.array]):vector of int represented the class
n_class (int) : number fo class to use
"""
if n_class == 2:
class_count: typing.Counter = Counter(y)
cond_binary = (0 in class_count) and (1 in class_count)
assert cond_binary, "Must have O and 1 class for binary classification"
weight = class_count[0] / class_count[1]
else:
weight = compute_class_weight(class_weight="balanced", classes=np.unique(y), y=y)
return torch.tensor(weight).float()
def fit(
self,
x: np.ndarray,
y: np.array,
epochs: int = 10,
batch_size: int = 32,
class_weight: Optional[str] = None,
validation_data: bool = True,
**kwargs
):
assert isinstance(x, np.ndarray), "X should be a numpy array"
assert isinstance(y, np.ndarray), "y should be a numpy array"
assert class_weight in (
None,
"balanced",
), "the only choice available for class_weight is 'balanced'"
n_class = len(np.unique(y))
weight = None
self.input_shape = x.shape[1]
self.output_shape = 1 if n_class <= 2 else n_class
self.activation = nn.Sigmoid() if n_class <= 2 else nn.Softmax(dim=-1)
if class_weight == "balanced":
weight = self.compute_class_weight(y, n_class)
self.loss = nn.NLLLoss(weight) if self.output_shape > 1 else nn.BCELoss(weight)
if validation_data:
x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.2)
train_loader = classification_dataloader_from_numpy(
x_train, y_train, batch_size=batch_size
)
val_loader = classification_dataloader_from_numpy(x_val, y_val, batch_size=batch_size)
else:
train_loader = classification_dataloader_from_numpy(x, y, batch_size=batch_size)
val_loader = None
self.trainer = pl.Trainer(max_epochs=epochs, **kwargs)
self.trainer.fit(self, train_loader, val_loader)
def predict(self, x):
"""Run inference on data."""
if self.output_shape is None:
log.warning("Model is not fitted. Can't do predict")
return
return self.forward(x).detach().numpy()
def save(self, path: str):
"""Save the state dict model with torch"""
torch.save(self.fc.state_dict(), path)
log.info("Save state_dict parameters in model.pt")
def load_state_dict(self, state_dict: "OrderedDict[str, Tensor]", strict: bool = False):
"""Load state_dict saved parameters
Args:
state_dict (OrderedDict[str, Tensor]): state_dict tensor
strict (bool, optional): [description]. Defaults to False.
"""
self.fc.load_state_dict(state_dict, strict=strict)
self.fc.eval()
mlp = RNN(input_shape=1024, n_neurons=1024, num_layers=2, n_class=2)
mlp.fit(embeddings_train, np.array(y_train),validation_data=(embeddings_test, np.array(y_test)), epochs=30)
mlp.save("model.pt")
这些是发生的错误。我真的需要帮助，我会随时为您提供更多信息。
错误 1
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-154-e5fde11a675c> in <module>
1 # init MLP model, train it on the data, then save model
2 mlp = RNN(input_shape=1024, n_neurons=1024, num_layers=2, n_class=2)
----> 3 mlp.fit(embeddings_train, np.array(y_train),validation_data=(embeddings_test, np.array(y_test)), epochs=30)
4 mlp.save("model.pt")
<ipython-input-153-a8d51af53bb5> in fit(self, x, y, epochs, batch_size, class_weight, validation_data, **kwargs)
134 val_loader = None
135 self.trainer = pl.Trainer(max_epochs=epochs, **kwargs)
--> 136 self.trainer.fit(self, train_loader, val_loader)
137 def predict(self, x):
138 """Run inference on data."""
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
456 )
457
--> 458 self._run(model)
459
460 assert self.state.stopped
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in _run(self, model)
754
755 # dispatch `start_training` or `start_evaluating` or `start_predicting`
--> 756 self.dispatch()
757
758 # plugin will finalized fitting (e.g. ddp_spawn will load trained model)
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in dispatch(self)
795 self.accelerator.start_predicting(self)
796 else:
--> 797 self.accelerator.start_training(self)
798
799 def run_stage(self):
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in start_training(self, trainer)
94
95 def start_training(self, trainer: 'pl.Trainer') -> None:
---> 96 self.training_type_plugin.start_training(trainer)
97
98 def start_evaluating(self, trainer: 'pl.Trainer') -> None:
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in start_training(self, trainer)
142 def start_training(self, trainer: 'pl.Trainer') -> None:
143 # double dispatch to initiate the training loop
--> 144 self._results = trainer.run_stage()
145
146 def start_evaluating(self, trainer: 'pl.Trainer') -> None:
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_stage(self)
805 if self.predicting:
806 return self.run_predict()
--> 807 return self.run_train()
808
809 def _pre_training_routine(self):
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_train(self)
840 self.progress_bar_callback.disable()
841
--> 842 self.run_sanity_check(self.lightning_module)
843
844 self.checkpoint_connector.has_trained = False
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_sanity_check(self, ref_model)
1105
1106 # run eval step
-> 1107 self.run_evaluation()
1108
1109 self.on_sanity_check_end()
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_evaluation(self, on_epoch)
960 # lightning module methods
961 with self.profiler.profile("evaluation_step_and_end"):
--> 962 output = self.evaluation_loop.evaluation_step(batch, batch_idx, dataloader_idx)
963 output = self.evaluation_loop.evaluation_step_end(output)
964
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/evaluation_loop.py in evaluation_step(self, batch, batch_idx, dataloader_idx)
172 model_ref._current_fx_name = "validation_step"
173 with self.trainer.profiler.profile("validation_step"):
--> 174 output = self.trainer.accelerator.validation_step(args)
175
176 # capture any logged information
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in validation_step(self, args)
224
225 with self.precision_plugin.val_step_context(), self.training_type_plugin.val_step_context():
--> 226 return self.training_type_plugin.validation_step(*args)
227
228 def test_step(self, args: List[Union[Any, int]]) -> Optional[STEP_OUTPUT]:
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in validation_step(self, *args, **kwargs)
159
160 def validation_step(self, *args, **kwargs):
--> 161 return self.lightning_module.validation_step(*args, **kwargs)
162
163 def test_step(self, *args, **kwargs):
<ipython-input-153-a8d51af53bb5> in validation_step(self, batch, batch_idx)
78 if self.output_shape > 1:
79 y_hat = torch.log(y_hat)
---> 80 loss = self.loss(y_hat, y)
81 self.log("val_loss", loss, on_epoch=True, on_step=False)
82 return {"val_loss": loss}
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
611 def forward(self, input: Tensor, target: Tensor) -> Tensor:
612 assert self.weight is None or isinstance(self.weight, Tensor)
--> 613 return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)
614
615
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/torch/nn/functional.py in binary_cross_entropy(input, target, weight, size_average, reduce, reduction)
2760 weight = weight.expand(new_size)
2761
-> 2762 return torch._C._nn.binary_cross_entropy(input, target, weight, reduction_enum)
2763
2764
RuntimeError: all elements of input should be between 0 and 1
错误 2
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-139-b7e8b13763ef> in <module>
1 # Model evaluation
----> 2 y_pred = mlp(embeddings_val).squeeze().detach().numpy()
3 model_evaluation_accuracy(np.array(y_val), y_pred)
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
<ipython-input-136-e2fc535640ab> in forward(self, x)
55 self.fc= nn.Linear(self.hidden_size, self.output_shape)
56 def forward(self, x):
---> 57 h0=torch.zeros(self.num_layers, x_size(0), self.hidden_size).to(device)
58 c0=torch.zeros(self.num_layers, x_size(0), self.hidden_size).to(device)
59 out, _=self.lstm(x,(h0, c0))
NameError: name 'x_size' is not defined
我将此添加为答案，因为很难发表评论。
您遇到的主要问题是 BCE 丢失。 IIRC BCE 损失期望 p(y=1)，所以你的输出应该在 0 和 1 之间。如果你想使用 logits（它在数值上也更稳定），你应该使用 BinaryCrossEntropyWithLogits.
正如您在其中一条评论中提到的，您使用的是 sigmoid 激活函数，但我不喜欢您的前向函数。主要是你forward函数的最后一行是
return self.fc(x)
这不使用 sigmoid 激活。此外，您仅使用输入 x 来生成输出。 LSTM 输出只是被丢弃了？我认为，添加一些打印语句或断点以确保中间输出符合您的预期是个好主意。
我收到错误 RuntimeError: all elements of input should be between 0 and 1 因为我的 x 数据有 NaN 项。
我想在蛋白质嵌入上使用带有 bilstm 层的 RNN，使用 pytorch。它适用于线性层，但当我使用 Bilstm 时出现运行时错误。对不起，如果它不清楚这是我的第一篇文章，如果有人能帮助我，我将不胜感激。
from collections import Counter, OrderedDict
from typing import Optional
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F # noqa
from deepchain import log
from sklearn.model_selection import train_test_split
from sklearn.utils.class_weight import compute_class_weight
from torch import Tensor, nn
num_layers=2
hidden_size=256
from torch.utils.data import DataLoader, TensorDataset
def classification_dataloader_from_numpy(
x: np.ndarray, y: np.array, batch_size: int = 32
) -> DataLoader:
"""Build a dataloader from numpy for classification problem
This dataloader is use only for classification. It detects automatically the class of
the problem (binary or multiclass classification)
Args:
x (np.ndarray): [description]
y (np.array): [description]
batch_size (int, optional): [description]. Defaults to None.
Returns:
DataLoader: [description]
"""
n_class: int = len(np.unique(y))
if n_class > 2:
log.info("This is a classification problem with %s classes", n_class)
else:
log.info("This is a binary classification problem")
# y is float for binary classification, int for multiclass
y_tensor = torch.tensor(y).long() if len(np.unique(y)) > 2 else torch.tensor(y).float()
tensor_set = TensorDataset(torch.tensor(x).float(), y_tensor)
loader = DataLoader(tensor_set, batch_size=batch_size)
return loader
class RNN(pl.LightningModule):
"""A `pytorch` based deep learning model"""
def __init__(self, input_shape: int, n_class: int, num_layers, n_neurons: int = 128, lr: float = 1e-3):
super(RNN,self).__init__()
self.lr = lr
self.n_neurons=n_neurons
self.num_layers=num_layers
self.input_shape = input_shape
self.output_shape = 1 if n_class <= 2 else n_class
self.activation = nn.Sigmoid() if n_class <= 2 else nn.Softmax(dim=-1)
self.lstm = nn.LSTM(self.input_shape, self.n_neurons, num_layers, batch_first=True, bidirectional=True)
self.fc= nn.Linear(self.n_neurons, self.output_shape)
def forward(self, x):
h0=torch.zeros(self.num_layers, x_size(0), self.n_neurons).to(device)
c0=torch.zeros(self.num_layers, x_size(0), self.n_neurons).to(device)
out, _=self.lstm(x,(h0, c0))
out=self.fc(out[:, -1, :])
return self.fc(x)
def training_step(self, batch, batch_idx):
"""training_step defined the train loop. It is independent of forward"""
x, y = batch
y_hat = self.fc(x).squeeze()
y = y.squeeze()
if self.output_shape > 1:
y_hat = torch.log(y_hat)
loss = self.loss(y_hat, y)
self.log("train_loss", loss, on_epoch=True, on_step=False)
return {"loss": loss}
def validation_step(self, batch, batch_idx):
"""training_step defined the train loop. It is independent of forward"""
x, y = batch
y_hat = self.fc(x).squeeze()
y = y.squeeze()
if self.output_shape > 1:
y_hat = torch.log(y_hat)
loss = self.loss(y_hat, y)
self.log("val_loss", loss, on_epoch=True, on_step=False)
return {"val_loss": loss}
def configure_optimizers(self):
"""(Optional) Configure training optimizers."""
return torch.optim.Adam(self.parameters(),lr=self.lr)
def compute_class_weight(self, y: np.array, n_class: int):
"""Compute class weight for binary/multiple classification
If n_class=2, only compute weights for the positve class.
If n>2, compute for all classes.
Args:
y ([np.array]):vector of int represented the class
n_class (int) : number fo class to use
"""
if n_class == 2:
class_count: typing.Counter = Counter(y)
cond_binary = (0 in class_count) and (1 in class_count)
assert cond_binary, "Must have O and 1 class for binary classification"
weight = class_count[0] / class_count[1]
else:
weight = compute_class_weight(class_weight="balanced", classes=np.unique(y), y=y)
return torch.tensor(weight).float()
def fit(
self,
x: np.ndarray,
y: np.array,
epochs: int = 10,
batch_size: int = 32,
class_weight: Optional[str] = None,
validation_data: bool = True,
**kwargs
):
assert isinstance(x, np.ndarray), "X should be a numpy array"
assert isinstance(y, np.ndarray), "y should be a numpy array"
assert class_weight in (
None,
"balanced",
), "the only choice available for class_weight is 'balanced'"
n_class = len(np.unique(y))
weight = None
self.input_shape = x.shape[1]
self.output_shape = 1 if n_class <= 2 else n_class
self.activation = nn.Sigmoid() if n_class <= 2 else nn.Softmax(dim=-1)
if class_weight == "balanced":
weight = self.compute_class_weight(y, n_class)
self.loss = nn.NLLLoss(weight) if self.output_shape > 1 else nn.BCELoss(weight)
if validation_data:
x_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.2)
train_loader = classification_dataloader_from_numpy(
x_train, y_train, batch_size=batch_size
)
val_loader = classification_dataloader_from_numpy(x_val, y_val, batch_size=batch_size)
else:
train_loader = classification_dataloader_from_numpy(x, y, batch_size=batch_size)
val_loader = None
self.trainer = pl.Trainer(max_epochs=epochs, **kwargs)
self.trainer.fit(self, train_loader, val_loader)
def predict(self, x):
"""Run inference on data."""
if self.output_shape is None:
log.warning("Model is not fitted. Can't do predict")
return
return self.forward(x).detach().numpy()
def save(self, path: str):
"""Save the state dict model with torch"""
torch.save(self.fc.state_dict(), path)
log.info("Save state_dict parameters in model.pt")
def load_state_dict(self, state_dict: "OrderedDict[str, Tensor]", strict: bool = False):
"""Load state_dict saved parameters
Args:
state_dict (OrderedDict[str, Tensor]): state_dict tensor
strict (bool, optional): [description]. Defaults to False.
"""
self.fc.load_state_dict(state_dict, strict=strict)
self.fc.eval()
mlp = RNN(input_shape=1024, n_neurons=1024, num_layers=2, n_class=2)
mlp.fit(embeddings_train, np.array(y_train),validation_data=(embeddings_test, np.array(y_test)), epochs=30)
mlp.save("model.pt")
这些是发生的错误。我真的需要帮助，我会随时为您提供更多信息。
错误 1
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-154-e5fde11a675c> in <module>
1 # init MLP model, train it on the data, then save model
2 mlp = RNN(input_shape=1024, n_neurons=1024, num_layers=2, n_class=2)
----> 3 mlp.fit(embeddings_train, np.array(y_train),validation_data=(embeddings_test, np.array(y_test)), epochs=30)
4 mlp.save("model.pt")
<ipython-input-153-a8d51af53bb5> in fit(self, x, y, epochs, batch_size, class_weight, validation_data, **kwargs)
134 val_loader = None
135 self.trainer = pl.Trainer(max_epochs=epochs, **kwargs)
--> 136 self.trainer.fit(self, train_loader, val_loader)
137 def predict(self, x):
138 """Run inference on data."""
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in fit(self, model, train_dataloader, val_dataloaders, datamodule)
456 )
457
--> 458 self._run(model)
459
460 assert self.state.stopped
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in _run(self, model)
754
755 # dispatch `start_training` or `start_evaluating` or `start_predicting`
--> 756 self.dispatch()
757
758 # plugin will finalized fitting (e.g. ddp_spawn will load trained model)
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in dispatch(self)
795 self.accelerator.start_predicting(self)
796 else:
--> 797 self.accelerator.start_training(self)
798
799 def run_stage(self):
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in start_training(self, trainer)
94
95 def start_training(self, trainer: 'pl.Trainer') -> None:
---> 96 self.training_type_plugin.start_training(trainer)
97
98 def start_evaluating(self, trainer: 'pl.Trainer') -> None:
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in start_training(self, trainer)
142 def start_training(self, trainer: 'pl.Trainer') -> None:
143 # double dispatch to initiate the training loop
--> 144 self._results = trainer.run_stage()
145
146 def start_evaluating(self, trainer: 'pl.Trainer') -> None:
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_stage(self)
805 if self.predicting:
806 return self.run_predict()
--> 807 return self.run_train()
808
809 def _pre_training_routine(self):
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_train(self)
840 self.progress_bar_callback.disable()
841
--> 842 self.run_sanity_check(self.lightning_module)
843
844 self.checkpoint_connector.has_trained = False
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_sanity_check(self, ref_model)
1105
1106 # run eval step
-> 1107 self.run_evaluation()
1108
1109 self.on_sanity_check_end()
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py in run_evaluation(self, on_epoch)
960 # lightning module methods
961 with self.profiler.profile("evaluation_step_and_end"):
--> 962 output = self.evaluation_loop.evaluation_step(batch, batch_idx, dataloader_idx)
963 output = self.evaluation_loop.evaluation_step_end(output)
964
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/trainer/evaluation_loop.py in evaluation_step(self, batch, batch_idx, dataloader_idx)
172 model_ref._current_fx_name = "validation_step"
173 with self.trainer.profiler.profile("validation_step"):
--> 174 output = self.trainer.accelerator.validation_step(args)
175
176 # capture any logged information
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/accelerators/accelerator.py in validation_step(self, args)
224
225 with self.precision_plugin.val_step_context(), self.training_type_plugin.val_step_context():
--> 226 return self.training_type_plugin.validation_step(*args)
227
228 def test_step(self, args: List[Union[Any, int]]) -> Optional[STEP_OUTPUT]:
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py in validation_step(self, *args, **kwargs)
159
160 def validation_step(self, *args, **kwargs):
--> 161 return self.lightning_module.validation_step(*args, **kwargs)
162
163 def test_step(self, *args, **kwargs):
<ipython-input-153-a8d51af53bb5> in validation_step(self, batch, batch_idx)
78 if self.output_shape > 1:
79 y_hat = torch.log(y_hat)
---> 80 loss = self.loss(y_hat, y)
81 self.log("val_loss", loss, on_epoch=True, on_step=False)
82 return {"val_loss": loss}
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
611 def forward(self, input: Tensor, target: Tensor) -> Tensor:
612 assert self.weight is None or isinstance(self.weight, Tensor)
--> 613 return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)
614
615
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/torch/nn/functional.py in binary_cross_entropy(input, target, weight, size_average, reduce, reduction)
2760 weight = weight.expand(new_size)
2761
-> 2762 return torch._C._nn.binary_cross_entropy(input, target, weight, reduction_enum)
2763
2764
RuntimeError: all elements of input should be between 0 and 1
错误 2
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-139-b7e8b13763ef> in <module>
1 # Model evaluation
----> 2 y_pred = mlp(embeddings_val).squeeze().detach().numpy()
3 model_evaluation_accuracy(np.array(y_val), y_pred)
/opt/conda/envs/bio-transformers/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
887 result = self._slow_forward(*input, **kwargs)
888 else:
--> 889 result = self.forward(*input, **kwargs)
890 for hook in itertools.chain(
891 _global_forward_hooks.values(),
<ipython-input-136-e2fc535640ab> in forward(self, x)
55 self.fc= nn.Linear(self.hidden_size, self.output_shape)
56 def forward(self, x):
---> 57 h0=torch.zeros(self.num_layers, x_size(0), self.hidden_size).to(device)
58 c0=torch.zeros(self.num_layers, x_size(0), self.hidden_size).to(device)
59 out, _=self.lstm(x,(h0, c0))
NameError: name 'x_size' is not defined
我将此添加为答案，因为很难发表评论。
您遇到的主要问题是 BCE 丢失。 IIRC BCE 损失期望 p(y=1)，所以你的输出应该在 0 和 1 之间。如果你想使用 logits（它在数值上也更稳定），你应该使用 BinaryCrossEntropyWithLogits.
正如您在其中一条评论中提到的，您使用的是 sigmoid 激活函数，但我不喜欢您的前向函数。主要是你forward函数的最后一行是
return self.fc(x)
这不使用 sigmoid 激活。此外，您仅使用输入 x 来生成输出。 LSTM 输出只是被丢弃了？我认为，添加一些打印语句或断点以确保中间输出符合您的预期是个好主意。
我收到错误 RuntimeError: all elements of input should be between 0 and 1 因为我的 x 数据有 NaN 项。