1.自定义损失函数
2.自定义DenseLayer
2.1 不带参
2.2 带参
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pandas as pd
import os
import sklearn
import sys
import time
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl,np,pd,sklearn,tf,keras:
print(module.__name__, module.__version__)
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_valid.shape, y_valid.shape)
print(x_train.shape, y_train.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.fit_transform(x_valid)
x_test_scaled = scaler.fit_transform(x_test)
# 自定义损失函数
def customized_mse(y_true, y_pred):
return tf.reduce_mean(tf.squre(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)]
history = model.fit(x_train_scaled,y_train,
validation_data=(x_valid_scaled,y_valid),
epochs = 100,
callbacks = callbacks)
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)
model.evaluate(x_test_scaled, y_test)
# lambda 自定义layer
# tf.nn.softplus:log(1+e^x)
customized_softplus = keras.layers.Lambda(lambda x:tf.nn.softplus(x))
print(customized_softplus([-01.,-5.,0.,5.,10.]))
# 自定义DenseLyer
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 bulid(self,input_shape):
"""构建所需要的参数"""
# 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 = 'zeros',
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),
customized_softplus,
# keras.layers.Dense(1,activation = 'softplus'),
# keras.layers.Dense(1),keras.layers.Activation('softplus'),
])
model.summary()
model.compile(loss = "mean_squared_error",optimizer = "sgd")
callbacks = [keras.callbacks.EarlyStopping(
patience=5,min_delta=1e-2)]