本文共 8779 字，大约阅读时间需要 29 分钟。
本文将介绍如下内容：
在TF中实现的demo如下：
import tensorflow as tf import numpy as np y_pred = np.array([1,4,3,2,6,5,9])y_true = np.array([1,4,3,2,1,4,9])print(tf.reduce_mean(tf.square(y_pred - y_true)))# ---output------tf.Tensor(3, shape=(), dtype=int64)
结合之前的实例如下：
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)]
#!/usr/bin/env python3# -*- coding: utf-8 -*-import matplotlib as mplimport matplotlib.pyplot as pltimport numpy as npimport sklearnimport pandas as pdimport osimport sysimport timeimport tensorflow as tffrom tensorflow import keras# 1,打印使用的python库的版本信息print(tf.__version__)print(sys.version_info)for module in mpl, np, pd, sklearn, tf, keras: print(module.__name__, module.__version__) # 2,下载并使用sklearn中的“fetch_california_housing”数据集from sklearn.datasets import fetch_california_housinghousing = fetch_california_housing()print(housing.DESCR)print(housing.data.shape)print(housing.target.shape)# 3,拆分数据集中的数据为 训练数据、验证数据、测试数据from sklearn.model_selection import train_test_splitx_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)# 4,在将数据带入到模型之前，先进行预处理-训练、验证、测试数据标准化from sklearn.preprocessing import StandardScalerscaler = StandardScaler()x_train_scaled = scaler.fit_transform(x_train)x_valid_scaled = scaler.transform(x_valid)x_test_scaled = scaler.transform(x_test)# 5,构建模型、模型层级图、编译模型（添加损失函数、优化器）、添加回调函数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)]# 6,训练构建的模型history = model.fit(x_train_scaled, y_train, validation_data = (x_valid_scaled, y_valid), epochs = 100, callbacks = callbacks, verbose=0)# 7,得到训练曲线图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)# 8,调用估计器-估计测试数据集print(model.evaluate(x_test_scaled, y_test, verbose=0))#---output------_________________________________________________________________Layer (type) Output Shape Param # =================================================================dense_591 (Dense) (None, 30) 270 _________________________________________________________________dense_592 (Dense) (None, 1) 31 =================================================================Total params: 301Trainable params: 301Non-trainable params: 0_________________________________________________________________[0.3701427737871806, 0.3701428]
layer = tf.keras.layers.Dense(100)layer = tf.keras.layers.Dense(100, input_shape=(None, 5))temp = layer(tf.zeros([10, 5]))print(temp.shape)# ---output-----(10, 100)
a）这里介绍tf.keras.layers.Dense的两个属性：
tf.keras.layers中的variables和trainable_variables
variables和trainable_variables的数值如下：
print(layer.variables)print(layer.trainable_variables)
b）tf.keras.layers的计算公式如下:
x * w + b
w 等同于"kernel";b等同于"bias"
c）如果想了解其他的参数详情,可以参看如下：
help(layer)
# 5,自定义激活函数# tf.nn.softplus : log(1+e^x)customized_softplus = keras.layers.Lambda(lambda x : tf.nn.softplus(x))# 输入测试数据测试自定义的激活函数print(customized_softplus([-10., -5., 0., 5., 10.]))# 6,自定义DenseLayer函数# customized 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)
#!/usr/bin/env python3# -*- coding: utf-8 -*-import matplotlib as mplimport matplotlib.pyplot as pltimport numpy as npimport sklearnimport pandas as pdimport osimport sysimport timeimport tensorflow as tffrom tensorflow import keras# 1,打印使用的python库的版本信息print(tf.__version__)print(sys.version_info)for module in mpl, np, pd, sklearn, tf, keras: print(module.__name__, module.__version__) # 2,下载并使用sklearn中的“fetch_california_housing”数据集# tf.keras.layers传入,输出的参数from sklearn.datasets import fetch_california_housinghousing = fetch_california_housing()print(housing.DESCR)print(housing.data.shape)print(housing.target.shape)# 3,拆分数据集中的数据为 训练数据、验证数据、测试数据from sklearn.model_selection import train_test_splitx_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)# 4,在将数据带入到模型之前，先进行预处理-训练、验证、测试数据标准化from sklearn.preprocessing import StandardScalerscaler = StandardScaler()x_train_scaled = scaler.fit_transform(x_train)x_valid_scaled = scaler.transform(x_valid)x_test_scaled = scaler.transform(x_test)# 5,自定义激活函数# 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.]))# 6,自定义DenseLayer函数# customized 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)# 7,构建模型、自定义模型层级、自定义激活函数、编译模型（添加损失函数、优化器）、添加回调函数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)]# 8,训练构建的模型history = model.fit(x_train_scaled, y_train, validation_data = (x_valid_scaled, y_valid), epochs = 100, callbacks = callbacks)# 9,得到训练曲线图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)# 10,调用估计器-估计测试数据集model.evaluate(x_test_scaled, y_test, verbose=0)# ---output------0.38935184751370155 转载地址：http://kvili.baihongyu.com/