metrics=['accuracy'])#compile:指定损失函数为categorical_crossentropy,优化器为rmsprop,评估标准为accuracy# 训练模型history = model.fit(X_train, y_train, epochs=100, batch_size=5, verbose=1)#fit:使用训练集数据和标签训练模型,指定训练的轮数(epochs) 3.5 模型评价与预测 #evaluate:在测试集上评估模型...
compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # 训练模型 model.fit(train_images, train_labels, epochs=5) # 在测试集上评估模型性能 test_loss, test_acc = model.evaluate(test_images, test_labels) print('\nTest accuracy:', test_acc) # 使用模型...
(8, ), kernel_initializer='uniform', activation='relu')) model.add(Dropout(0.05)) model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid')) # 编译模型 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) return model model = KerasClassifier(...
(1,activation='sigmoid'))# 编译模型 model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])# 训练模型 history=model.fit(x_train,y_train,epochs=5,batch_size=32,validation_split=0.2)# 评估模型 test_loss,test_acc=model.evaluate(x_test,y_test)print(f'Test Accuracy:...
(1, activation='sigmoid')) # 编译模型 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # 训练模型 history = model.fit(x_train, y_train, epochs=5, batch_size=32, validation_split=0.2) # 评估模型 test_loss, test_acc = model.evaluate(x_test, y_test...
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) 1. 2. 3. 4. 5. 6. 7. 使用示例:使用深度学习模型检测文本相似性 使用深度学习模型来检测文本相似性通常需要大规模的训练数据和计算资源。 以下是一个示例,演示了如何使用预训练的BERT模型来检测文本相似性。在这个示例中...
# 模型编译与训练model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])history = model.fit(train_images, train_labels, epochs=10,validation_data=(test_images, test_labels)) ...
model.add(Dense(1, activation='sigmoid'))# 编译模型model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])# 训练模型history = model.fit(x_train, y_train, epochs=5, batch_size=32, validation_split=0.2)# 评估模型test_loss, test_acc = model.evaluate(x_test, ...
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(partial_x_train, partial_y_train, epochs=30, batch_size=512, validation_data=(x_val, y_val)) results = model.evaluate(X_test, y_test) ...
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) 1. 2. 3. 4. 5. 6. 7. 8. 9. 4.进行训练和测试 4.1 留出验证集 代码实现: x_val = x_train[:1000] partial_x_train = x_train[1000:]