id_seq = 100 seq = np.copy(sequence_target_drop_test[id_seq]) seq[seq == mask_value] = np.nan seq = scaler_target.inverse_transform(seq)
时间: 2024-05-19 16:14:02 浏览: 13
These lines of code are used to extract a sequence of values from the `sequence_target_drop_test` array with an index of `id_seq`. The `np.copy` function is used to make a copy of the sequence so that the original array is not modified. The value `mask_value` is replaced with `np.nan` to indicate missing values. Then, the `scaler_target` object is used to invert the scaling performed on the sequence during preprocessing. The resulting sequence is a set of actual values that can be compared to predicted values to evaluate the performance of a model.
相关问题
losses = tensorflow.leagcy_seq2seq.sequence_loss_by_example
抱歉,你的代码中存在一些错误。正确的函数名称是 `tf.contrib.legacy_seq2seq.sequence_loss_by_example` 而不是 `tensorflow.leagcy_seq2seq.sequence_loss_by_example`。此外,`tf.contrib` 模块已经被废弃,建议使用新的模块 `tf.compat.v1`。因此,正确的代码应该是这样的:
```
import tensorflow as tf
# Define your logits and targets
losses = tf.compat.v1.nn.seq2seq.sequence_loss_by_example(
logits=[logits],
targets=[targets],
weights=[weights]
)
# Compute the average loss
cost = tf.reduce_mean(losses)
```
请注意,你需要将 `logits`、`targets` 和 `weights` 替换为你实际使用的张量。
import tensorflow as tf import tensorflow_hub as hub from tensorflow.keras import layers import bert import numpy as np from transformers import BertTokenizer, BertModel # 设置BERT模型的路径和参数 bert_path = "E:\\AAA\\523\\BERT-pytorch-master\\bert1.ckpt" max_seq_length = 128 train_batch_size = 32 learning_rate = 2e-5 num_train_epochs = 3 # 加载BERT模型 def create_model(): input_word_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_word_ids") input_mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_mask") segment_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="segment_ids") bert_layer = hub.KerasLayer(bert_path, trainable=True) pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids]) output = layers.Dense(1, activation='sigmoid')(pooled_output) model = tf.keras.models.Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=output) return model # 准备数据 def create_input_data(sentences, labels): tokenizer = bert.tokenization.FullTokenizer(vocab_file=bert_path + "trainer/vocab.small", do_lower_case=True) # tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') input_ids = [] input_masks = [] segment_ids = [] for sentence in sentences: tokens = tokenizer.tokenize(sentence) tokens = ["[CLS]"] + tokens + ["[SEP]"] input_id = tokenizer.convert_tokens_to_ids(tokens) input_mask = [1] * len(input_id) segment_id = [0] * len(input_id) padding_length = max_seq_length - len(input_id) input_id += [0] * padding_length input_mask += [0] * padding_length segment_id += [0] * padding_length input_ids.append(input_id) input_masks.append(input_mask) segment_ids.append(segment_id) return np.array(input_ids), np.array(input_masks), np.array(segment_ids), np.array(labels) # 加载训练数据 train_sentences = ["Example sentence 1", "Example sentence 2", ...] train_labels = [0, 1, ...] train_input_ids, train_input_masks, train_segment_ids, train_labels = create_input_data(train_sentences, train_labels) # 构建模型 model = create_model() model.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate), loss='binary_crossentropy', metrics=['accuracy']) # 开始微调 model.fit([train_input_ids, train_input_masks, train_segment_ids], train_labels, batch_size=train_batch_size, epochs=num_train_epochs)这段代码有什么问题吗?
这段代码存在一些问题:
1. `bert_path` 的值不应该是 ".ckpt" 结尾的文件路径,而是BERT模型的目录路径,例如:`bert_path = "E:\\AAA\\523\\BERT-pytorch-master\\"`。
2. `vocab_file` 参数应该是BERT模型目录下的 "vocab.txt" 文件路径,而不是训练器目录下的 "vocab.small" 文件路径。例如:`tokenizer = bert.tokenization.FullTokenizer(vocab_file=bert_path + "vocab.txt", do_lower_case=True)`。
3. `create_input_data` 函数中的 `tokenizer` 对象应该是从 `BertTokenizer` 类中导入的。因此,需要将 `from transformers import BertTokenizer` 加入代码开头,并使用 `BertTokenizer.from_pretrained()` 函数来加载BERT的tokenizer。
4. `train_sentences` 和 `train_labels` 应该是训练数据集,但是在代码中缺失了这些数据的定义和加载。
5. `create_input_data` 函数返回的 `labels` 应该是一维的,而不是二维的。因此,需要修改函数 `return` 语句为:`return np.array(input_ids), np.array(input_masks), np.array(segment_ids), np.array(labels).reshape(-1)`。
6. 在 `model.fit` 中,需要指定验证集的参数 `validation_data`,以便在训练过程中评估模型的性能。
综上所述,以下是修改后的代码示例:
```
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras import layers
import bert
import numpy as np
from transformers import BertTokenizer, BertModel
# 设置BERT模型的路径和参数
bert_path = "E:\\AAA\\523\\BERT-pytorch-master\\"
max_seq_length = 128
train_batch_size = 32
learning_rate = 2e-5
num_train_epochs = 3
# 加载BERT模型
def create_model():
input_word_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_word_ids")
input_mask = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="input_mask")
segment_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype=tf.int32, name="segment_ids")
bert_layer = hub.KerasLayer(bert_path, trainable=True)
pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids])
output = layers.Dense(1, activation='sigmoid')(pooled_output)
model = tf.keras.models.Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=output)
return model
# 准备数据
def create_input_data(sentences, labels):
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
input_ids = []
input_masks = []
segment_ids = []
for sentence in sentences:
tokens = tokenizer.tokenize(sentence)
tokens = ["[CLS]"] + tokens + ["[SEP]"]
input_id = tokenizer.convert_tokens_to_ids(tokens)
input_mask = [1] * len(input_id)
segment_id = [0] * len(input_id)
padding_length = max_seq_length - len(input_id)
input_id += [0] * padding_length
input_mask += [0] * padding_length
segment_id += [0] * padding_length
input_ids.append(input_id)
input_masks.append(input_mask)
segment_ids.append(segment_id)
return np.array(input_ids), np.array(input_masks), np.array(segment_ids), np.array(labels).reshape(-1)
# 加载训练数据
train_sentences = ["Example sentence 1", "Example sentence 2", ...]
train_labels = [0, 1, ...]
train_input_ids, train_input_masks, train_segment_ids, train_labels = create_input_data(train_sentences, train_labels)
# 构建模型
model = create_model()
model.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate),
loss='binary_crossentropy',
metrics=['accuracy'])
# 开始微调
model.fit([train_input_ids, train_input_masks, train_segment_ids], train_labels,
batch_size=train_batch_size,
epochs=num_train_epochs,
validation_data=([val_input_ids, val_input_masks, val_segment_ids], val_labels))
```