基于tensorflow搭建神经网络

一、tf.keras搭建神经网络步骤

六步法

1. import
2. train，test
3. model = tf.keras.models.Sequential
4. model.compile
5. model.fit
6. model.summary

models.Sequential()

model = tf.keras.models.Sequential ([ 网络结构 ]) #描述各层网络
网络结构举例：

1. 拉直层：

tf.keras.layers.Flatten( )

2. 全连接层：

tf.keras.layers.Dense(神经元个数, activation= “激活函数”,kernel_regularizer=哪种正则化)

activation（字符串给出）可选: relu、 softmax、 sigmoid 、 tanh
kernel_regularizer可选: tf.keras.regularizers.l1()、tf.keras.regularizers.l2()

3. 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”)

4. LSTM层： tf.keras.layers.LSTM()

model.compile()

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

Optimizer可选:
‘sgd’ or tf.keras.optimizers.SGD (lr=学习率,momentum=动量参数)
‘adagrad’ or tf.keras.optimizers.Adagrad (lr=学习率)
‘adadelta’ or tf.keras.optimizers.Adadelta (lr=学习率)
‘adam’ or tf.keras.optimizers.Adam (lr=学习率, beta_1=0.9, beta_2=0.999)

loss可选:
‘mse’ or tf.keras.losses.MeanSquaredError()
‘sparse_categorical_crossentropy’ or tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)

Metrics可选:
‘accuracy’ ：y_和y都是数值，如y_=[1] y=[1]
‘categorical_accuracy’ ：y_和y都是独热码(概率分布)，如y_=[0,1,0] y=[0.256,0.695,0.048]
‘sparse_categorical_accuracy’ ：y_是数值，y是独热码(概率分布),如y_=[1] y=[0.256,0.695,0.048]

model.fit()

model.fit (训练集的输入特征, 训练集的标签,
batch_size= , epochs= ,
validation_data=(验证集的输入特征，验证集的标签),
validation_split=从训练集划分多少比例给验证集，
validation_freq = 多少次epoch验证一次)

model.summary()

二、自定义Model

class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        定义网络结构块
    def call(self, x):
        调用网络结构块，实现前向传播
        return y
model = MyModel()

例：

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()

三、tf.keras实现手写数字分类

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()

运行结果如下：

60000/60000 [==============================] - 5s 89us/sample - loss: 0.0455 - sparse_categorical_accuracy: 0.9861 - val_loss: 0.0806 - val_sparse_categorical_accuracy: 0.9752
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param # 
=================================================================
flatten (Flatten)            multiple                  0         
_________________________________________________________________
dense (Dense)                multiple                  100480    
_________________________________________________________________
dense_1 (Dense)              multiple                  1290      
=================================================================
Total params: 101,770
Trainable params: 101,770
Non-trainable params: 0