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 ___...
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') ...
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, ) # step5 模型测试 loss,acc=model.evaluate(x_t...
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.random.rand(1000, 10) y_train = np....
model = create_model() # 保存模型到HDF5文件 model.save('my_model.h5') # 读取模型 model = keras.models.load_model('my_model.h5') 1. 2. 3. 4. 5. 6. 二、自定义模型 如果是自定义模型使用上述方法保存会报错且保存失败,报错为:
# 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)) ...
losses.sparse_categorical_crossentropy, metrics=['accuracy']) return model model = create_model() model.summary() Model: "sequential_2" ___ Layer (type) Output Shape Param # === dense_4 (Dense) (None, 128) 100480 ___
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...
Dim)x=inception_module(x,176,160,chanDim)x=AveragePooling2D((7,7))(x)x=Dropout(0.5)(x)# softmax classifierx=Flatten()(x)x=Dense(classes)(x)x=Activation("softmax")(x)# create the modelmodel=Model(inputs,x,name="minigooglenet")# return the constructed network architecturereturnmodel...