基于tensorflow搭建神经网络
一、tf.keras搭建神经网络步骤
六步法
importtrain，testmodel = tf.keras.models.Sequentialmodel.compilemodel.fitmodel.summary
models.Sequential()
model = tf.keras.models.Sequential ([ 网络结构 ]) #描述各层网络 网络结构举例：
拉直层：
tf.keras.layers.Flatten( )
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()
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”] )
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
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