model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)# 定义损失函数和优化器criterion = nn.CrossEntropyLoss()optimizer = optim.Adam(model.parameters(), lr=1e-5)# 定义训练循环def train(model, data_loader, criterion, optimizer): model.train() total_loss = 0.0 for batch in data_loader: input_ids = batch['input_ids'].to(device) att...
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])# 模型训练model.fit(train_data, train_labels, epochs=10, batch_size=32, validation_data=(val_data, val_labels))```### 模型评估与优化- **评估指标**在推荐系统中,常用的评估指标包括准确率、召回率、F1分数等。我们使用这些指标来评估模型的性能。- **模型优化**通过调整模型的超参数、增加数据样本量以及引入...
import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Embedding, LSTM, GRU, RNN from tensorflow.keras.preprocessing.text import Toke... optimizer='adam', metrics=['accuracy']) # 训练模型 model.fit(padded_sequences, np.array([1]*len(sequences)), epochs=10, batch_size=32) # 生成诗歌 input_seq = tokenizer.texts_to_sequen...
activation=tf.nn.relu), keras.layers.Dense(10, activation=tf.nn.softmax)])model.compile(optimizer=tf.train.AdamOptimizer(), loss='sparse_categorical_crossentropy', metrics=['accuracy'])model.fit(train_images, train_labels, epochs=5)test_loss, test_acc = model.evaluate(test_images, test_labels)print('Test accuracy:', test_acc)predictions = model.predic...
同时尽量保证模型的精度不受影响。我们的主要实现方式是利用OpenVINO工具套件的模型剪枝和量化功能,有选择性地减小模型的规模,去除冗余参数,以适应端侧设备的资源限制。然后,借助 OpenVINO 的量化功能,将模型参数... pruned_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Load existing weights to pruned model (assuming the model is already trained) pr...
self.embedding = tf.keras.layers.Embedding(input_dim=500, output_dim=16) self.gru = tf.keras.layers.GRU(64) self.Dense = tf.keras.layers.Dense(1) def call(self, inputs): x = self.embedding(inputs) x = self.gru(x) return self.Dense(x) model = RecSysModel() model.compile(optimizer='adam', loss='mean_squared_error') model.fit(...
import tensorflow as tf data_dir = pathlib.Path(os.path.dirname(__file__) + '/../train_data')train_ds = tf.keras.preprocessing.image_dataset_from_directory( data_dir, ... Adam 是一种使用过去梯度计算当前梯度的方法,他的优点有:计算效率高,内存需求小。即使很少调整超参数,通常也能很好地工作。``` model.compile(optimizer='adam', loss=tf.keras.losses.Sparse...
name="SparseFeatFactors", initializer=paddle.nn.initializer.Uniform())) #使用循环的方式创建全连接层,可以在超参数中通过一个数组确定使用几个全连接层以及每个全连... optimizer: class: Adam learning_rate: 0.001 # user-defined pairs sparse_feature_number: 600000 sparse_feature_dim: 9 fc_sizes: [512, 256, 128, 32]```在简单了解召回模型和其组网实...
在project级别的build.gradle文件的dependencies中,添加以下代码,接入插件组件。 Java classpath "com.volcengine:apm_insight_plugin:1.4.2" 在app module的build.gradle文件的dependencies中,添加以下代码,完成... 默认false// .enableOptimizer(true) //可选,是否开启崩溃优化方案,默认false .autoStart(false) // 可选,是否在初始化时自动开启监控,默认为true// .debugMode(true) // 可...