除了导入导入Keras库来运行上述代码,还需要导入具体keras库的那些:def createModel(): input_shape=(1, 22, 59, 114) model = Sequential() #C1 model.add(Conv3D(16, (22, 5, 5), strides=(1, 2, 2), padding='valid',activation='relu',data_format= "channels_first", input_shape=input_shape)) model.add(keras.layers.MaxPooling3D(pool_size=(1, 2, 2),data_format= "channels_first", padding='same')) model.add(BatchNormalization()) #C2 model.add(Conv3D(32, (1, 3, 3), strides=(1, 1,1), padding='valid',data_format= "channels_first", activation='relu'))#incertezza se togliere padding model.add(keras.layers.MaxPooling3D(pool_size=(1,2, 2),data_format= "channels_first", )) model.add(BatchNormalization()) #C3 model.add(Conv3D(64, (1,3, 3), strides=(1, 1,1), padding='valid',data_format= "channels_first", activation='relu'))#incertezza se togliere padding model.add(keras.layers.MaxPooling3D(pool_size=(1,2, 2),data_format= "channels_first", )) model.add(BatchNormalization()) model.add(Flatten()) model.add(Dropout(0.5)) model.add(Dense(256, activation='sigmoid')) model.add(Dropout(0.5)) model.add(Dense(2, activation='softmax')) opt_adam = keras.optimizers.Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0) model.compile(loss='categorical_crossentropy', optimizer=opt_adam, metrics=['accuracy']) return model
时间: 2023-06-06 22:04:38 浏览: 41
除了导入Keras库,还需要导入Sequential、Conv3D、MaxPooling3D、Dropout、Flatten和Dense等具体的Keras库。这些库是搭建模型所必须的,分别实现了3D卷积、池化、Dropout、扁平化和全连接等操作。
相关问题
# 编码器 def encoder(input_shape, vocab_size, latent_dim): model = tf.keras.models.Sequential([ tf.keras.layers.Embedding(vocab_size, 256, input_shape=input_shape, name="encoder_embedding"), tf.keras.layers.LSTM(latent_dim, name="encode_lstm"), ],name="encoder") return model # 解码器 def decoder(output_shape, vocab_size, latent_dim): model = tf.keras.models.Sequential([ tf.keras.layers.RepeatVector(output_shape[0], input_shape=output_shape, name="decoder_repeatvector"), tf.keras.layers.LSTM(latent_dim, return_sequences=True,name="decode_lstm"), tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(vocab_size, activation='softmax'), name="decoder_td"), ], name="decoder") return model # expected shape=(None, None, 12), found shape=(None, 12, 256) # 定义模型 def build_model(input_shape, output_shape, vocab_size, latent_dim): encoder_model = encoder(input_shape, vocab_size, latent_dim) decoder_model = decoder(output_shape, vocab_size, latent_dim) model = tf.keras.models.Sequential([encoder_model, decoder_model]) return model改正一下模型
根据您提供的代码,您需要将编码器模型的LSTM层的return_sequences参数设置为True,以便正确地将输出形状传递给解码器模型。同时,您还需要将解码器模型的RepeatVector层的输入形状设置为output_shape[1],而不是output_shape[0]。修改后的代码如下所示:
```
# 编码器
def encoder(input_shape, vocab_size, latent_dim):
model = tf.keras.models.Sequential([
tf.keras.layers.Embedding(vocab_size, 256, input_shape=input_shape, name="encoder_embedding"),
tf.keras.layers.LSTM(latent_dim, return_sequences=True, name="encode_lstm"),
], name="encoder")
return model
# 解码器
def decoder(output_shape, vocab_size, latent_dim):
model = tf.keras.models.Sequential([
tf.keras.layers.RepeatVector(output_shape[1], input_shape=(latent_dim,), name="decoder_repeatvector"),
tf.keras.layers.LSTM(latent_dim, return_sequences=True, name="decode_lstm"),
tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(vocab_size, activation='softmax'), name="decoder_td"),
], name="decoder")
return model
# 定义模型
def build_model(input_shape, output_shape, vocab_size, latent_dim):
encoder_model = encoder(input_shape, vocab_size, latent_dim)
decoder_model = decoder(output_shape, vocab_size, latent_dim)
model = tf.keras.models.Sequential([encoder_model, decoder_model])
return model
```
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))
```