自定义layer层
构建模型分为以下几步:
- 导入数据集,将数据集进行分类、归一化等
- 构建模型
- 模型编译
- 模型训练
- 绘制曲线图
- 在测试集上进行评估
自定义损失函数在第2步中
方法一:使用子类class 方式自定义dense layer
class CustomizedDenseLayer(keras.layers.Layer):
def __init__(self, units, activation=None, **kwargs):
self.units = units
self.activation = keras.layers.Activation(activation)
super(CustomizedDenseLayer, self).__init__(**kwargs)
def build(self,input_shape):
'''构建参数 w b'''
# x * w + b
#input_shape:[None,a] * w:[a,b] --> output_shape:[None,b]
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[1],self.units),
initializer='uniform',
trainable=True)
self.bias = self.add_weight(name='bias',
shape=(self.units,),
initializer = 'uniform',
trainable = True)
super(CustomizedDenseLayer,self).build(input_shape)
def call(self, x):
'''完成正向计算'''
return self.activation(x @ self.kernel + self.bias)
model = keras.models.Sequential([
CustomizedDenseLayer(30,
activation='relu',
input_shape=x_train.shape[1:]),
CustomizedDenseLayer(1),
])
model.summary()
方法2: 使用lambda方式自定义 dense layer
# 例: tf.nn.softplus : log(1+e^x)
customized_softplus = keras.layers.Lambda(lambda x : tf.nn.softplus(x))
print(customized_softplus([-10.,-5.,0.,5.,10.]))
'''
customized_softplus -->
keras.layers.Dense(1,activation='softplus')
'''
完整的代码如下:(以子类方式为例)
'''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
class CustomizedDenseLayer(keras.layers.Layer):
def __init__(self, units, activation=None, **kwargs):
self.units = units
self.activation = keras.layers.Activation(activation)
super(CustomizedDenseLayer, self).__init__(**kwargs)
def build(self,input_shape):
'''构建参数 w b'''
# x * w + b
#input_shape:[None,a] * w:[a,b] --> output_shape:[None,b]
self.kernel = self.add_weight(name='kernel',
shape=(input_shape[1],self.units),
initializer='uniform',
trainable=True)
self.bias = self.add_weight(name='bias',
shape=(self.units,),
initializer = 'uniform',
trainable = True)
super(CustomizedDenseLayer,self).build(input_shape)
def call(self, x):
'''完成正向计算'''
return self.activation(x @ self.kernel + self.bias)
model = keras.models.Sequential([
CustomizedDenseLayer(30,
activation='relu',
input_shape=x_train.shape[1:]),
CustomizedDenseLayer(1),
])
'''3.模型编译'''
model.compile(loss='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)