神经网络搭建八股

使用tf.keras

六步法搭建模型

1.import

2.train, test 指定输入特征/标签

3.model = tf.keras.model.Sequential

在Squential,搭建神经网络

4.model.compile

配置训练方法，选择哪种优化器、损失函数、评测指标

5.model.fit 执行训练过程，告知训练集输入特征，batch,epoch

6.model.summary打印网络结构和参数统计

model = tf.keras.model.Sequential

Sequential是个容器，封装了网络结构

网络结构例子：

拉直层：tf.keras.layers.Flatten()

全连接层：tf.keras.layers.Dense(神经元个数，activetion="激活函数",kernel_regularizer=那种正则化)

卷积层:

tf.keras.layers.Conv2D(filters= 卷积核个数，kernel_size=卷积核尺寸，strides=卷积步长，padding="valid"or"same"

LSTM层：

tf.keras.layers.LSTM()

model.compile

model.compile(optimizer=优化器，loss=损失函数，metrics=["准确率"]

后期可通过tensorflow官网查询函数的具体用法，调节超参数

有些网络输出经过softmax概率分布输出，有些不经过概率分布输出 

当网络评测指标和蒙的概率一样，例如十分类概率为.1/10.可能概率分布错了

独热码y_和y是[010]网络输出则为[0.xx, 0.xx, 0.xx]

 第三种方法 y_= [1] y =[0.2xx,0xx,0xx]

model.fit

model.fit(训练集的输入特征，训练集的标签，batch_size, epochs=, 

validation_data=(测试集的输入特征，标签）,

validation_split=从训练集划分多少比例给测试集，

validation_freq=多少次epoch测试一次）

model.summary

重构Iris分类

import tensorflow as tf
from sklearn import datasets
import numpy as np

x_train = datasets.load_iris().data
y_train = datasets.load_iris().target

np.random.seed(116)
np.random.shuffle(x_train)
np.random.seed(116)
np.random.shuffle(y_train)
tf.random.set_seed(116)

model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(3, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2())
])

model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.1),
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

model.fit(x_train, y_train, batch_size=32, epochs=500, validation_split=0.2, validation_freq=20)

model.summary()

自定义搭建模型

swquential可以搭建上层输出就是下层输入的网络结构，但是无法搭建带有跳连特征的非顺序网络结构

class MyModel(Model)

        def __init__(self):

                super(MyModel, self) __init()

                定义网络结构块

        def call(self, x): #写出前向传播

               调用网络结构块，实现前向传播

        return y     

model = MyModel

__init__定义出积木

call调用积木，实现前向传播

import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras import Model
from sklearn import datasets
import numpy as np

x_train = datasets.load_iris().data
y_train = datasets.load_iris().target

np.random.seed(116)
np.random.shuffle(x_train)
np.random.seed(116)
np.random.shuffle(y_train)
tf.random.set_seed(116)

class IrisModel(Model):
    def __init__(self):
        super(IrisModel, self).__init__()
        self.d1 = Dense(3, activation='softmax', kernel_regularizer=tf.keras.regularizers.l2())

    def call(self, x):
        y = self.d1(x)
        return y

model = IrisModel()

model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.1),
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

model.fit(x_train, y_train, batch_size=32, epochs=500, validation_split=0.2, validation_freq=20)
model.summary()

每循环一次train，计算一次test的测试指标

MNIST数据集

1.导入MNIST数据集

mnist=tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) =  mnist.load_data(

2.作为输入特征，输入神经网络时，将数据拉伸成一维数组：

tf.keras.layers.Flatten()

把784个像素点的灰度值作为输入特征放入神经网络

plt.imshow(x_train[0], cmap='gray')#绘制灰度图

plt.show()

0表示纯黑色255表示纯白色

需要对测试集和数据集进行归一化处理，把数值变小，更适合神经网络吸收，使用sequental训练模型，由于输入特征为数组，输出为概率分布，所以我们选择sparse_categorical_accuracy

import tensorflow as tf

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
model.summary()

准确率是测试集的准确率

自定义Model实现 __init__中定义cell函数中用到的层

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras import Model

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0


class MnistModel(Model):
    def __init__(self):
        super(MnistModel, self).__init__()
        self.flatten = Flatten()
        self.d1 = Dense(128, activation='relu')
        self.d2 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.flatten(x)
        x = self.d1(x)
        y = self.d2(x)
        return y


model = MnistModel()

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
model.summary()

FASHION数据集

import tensorflow as tf

fashion = tf.keras.datasets.fashion_mnist
(x_train, y_train),(x_test, y_test) = fashion.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
model.summary()