model.add(layers.Dense(CAT_NUM,activation="softmax"))return(model)defcompile_model(model): model.compile(optimizer=optimizers.Nadam(), loss=losses.SparseCategoricalCrossentropy(), metrics=[metrics.SparseCategoricalAccuracy(),metrics.SparseTopKCategoricalAccuracy(5)])return(model) model=create_model() ...
defcreate_model(): model = models.Sequential([ layers.Dense(512, activation='relu', input_shape=(784,)), layers.Dropout(0.2), layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', metrics=['accuracy'], loss='sparse_categorical_crossentropy') ...
SparseTopKCategoricalAccuracy(5)]) return(model) model = create_model() model.summary() model = compile_model(model) 代码语言:javascript 代码运行次数:0 运行 AI代码解释 Model: "sequential" ___ Layer (type) Output Shape Param # === embedding (Embedding) (None, 300, 7) 216874 ___...
import numpy as np def create_model(): model = tf.keras.Sequential([tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)), tf.keras.layers.Dense(32, activation='relu'), tf.keras.layers.Dense(10, activation='softmax')]) return model model = create_model() x_train = np...
model=create_model() # step3 编译模型 主要是确定优化方法,损失函数等 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # step4 模型训练 训练一个epochs model.fit(x=x_train, y=y_train, epochs=1, ...
def create_model(bert_config, is_training, input_ids): # 通过传入的训练数据,进行representation model = modeling.BertModel(config=bert_config, is_training=is_training, input_ids=input_ids) # output = model.get_pooled_output() output = model.get_sequence_output() ...
# 保存权重model.save_weights('./checkpoints/my_checkpoint')# 创建模型实例model=create_model()# 加载权重model.load_weights('./checkpoints/my_checkpoint')# 评估模型loss,acc=model.evaluate(test_images,test_labels,verbose=2)print("Restored model, accuracy: {:5.2f}%".format(100*acc)) ...
model=create_model()model.fit(train_images,train_labels,epochs=5)# 以SavedModel格式保存整个模型 model.save("saved_model/my_model")new_model=tf.keras.models.load_model("saved_model/my_model")# 看到模型的结构 new_model.summary()# 评估模型 loss,acc=new_model.evaluate(test_images,test_labels...
1function createModel() 2{ 3var model = tf.sequential() 4model.add(tf.layers.dense({units:8, inputShape:2,activation: 'tanh'})) 5model.add(tf.layers.dense({units:1, activation: 'sigmoid'})) 6model.compile({optimizer: 'sgd', loss: 'binaryCrossentropy', lr:0.1}) ...
训练模型并将其传递给 ModelCheckpoint回调 checkpoint_path = "training_1/cp.ckpt" checkpoint_dir = os.path.dirname(checkpoint_path) # 创建一个检查点回调 cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path, save_weights_only=True, verbose=1) model = create_model() model.fit(train...