11 Tensorflow2.x 自定义损失函数

自定义损失函数

构建模型分为以下几步：

1. 导入数据集，将数据集进行分类、归一化等
2. 构建模型
3. 模型编译
4. 模型训练
5. 绘制曲线图
6. 在测试集上进行评估

自定义损失函数在第2、3步中

'''2.构建模型'''
# 自定义损失函数
def customized_mse(y_true, y_pred):
    return tf.reduce_mean(tf.square(y_pred - y_true))

model = keras.models.Sequential([
    keras.layers.Dense(30, activation='relu',
                       input_shape=x_train.shape[1:]),
    keras.layers.Dense(1),
])
model.summary()
model.compile(loss=customized_mse, 
              optimizer="sgd",
              metrics=["mean_squared_error"])
callbacks = [keras.callbacks.EarlyStopping(
    patience=5, min_delta=1e-2)]
    
'''3.模型编译'''
model.compile(loss=customized_mse, 
              optimizer="sgd",
              metrics=["mean_squared_error"])
callbacks = [keras.callbacks.EarlyStopping(
    patience=5, min_delta=1e-2)]

所有的完成程序如下：

'''1. 导入数据集及数据集分类、归一化'''
from sklearn.datasets import fetch_california_housing

housing = fetch_california_housing()
# print(housing.DESCR)
# print(housing.data.shape)
# print(housing.target.shape)

from sklearn.model_selection import train_test_split

x_train_all, x_test, y_train_all, y_test = train_test_split(
    housing.data, housing.target, random_state = 7)
x_train, x_valid, y_train, y_valid = train_test_split(
    x_train_all, y_train_all, random_state = 11)
print(x_train.shape, y_train.shape)
print(x_valid.shape, y_valid.shape)
print(x_test.shape, y_test.shape)

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)


'''2.构建模型'''
# 自定义损失函数
def customized_mse(y_true, y_pred):
    return tf.reduce_mean(tf.square(y_pred - y_true))

model = keras.models.Sequential([
    keras.layers.Dense(30, activation='relu',
                       input_shape=x_train.shape[1:]),
    keras.layers.Dense(1),
])
model.summary()

'''3.模型编译'''
model.compile(loss=customized_mse, 
              optimizer="sgd",
              metrics=["mean_squared_error"])
callbacks = [keras.callbacks.EarlyStopping(
    patience=5, min_delta=1e-2)]

'''4.模型训练'''
history = model.fit(x_train_scaled, y_train,
                    validation_data = (x_valid_scaled, y_valid),
                    epochs = 100,
                    callbacks = callbacks)

'''5.绘制变化曲线'''
def plot_learning_curves(history):
    pd.DataFrame(history.history).plot(figsize=(8, 5))
    plt.grid(True)
    plt.gca().set_ylim(0, 1)
    plt.show()
plot_learning_curves(history)

'''6.在测试集上进行评估'''
model.evaluate(x_test_scaled, y_test)