tensorflow基础--------实现手写数字识别

import  tensorflow as tf
from    tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
from 	tensorflow import keras

导入相关的包

进行数据读取和处理

def preprocess(x,y):
    x =tf.cast(x,dtype=tf.float32) / 255. - 1.
    y =tf.cast(y,dtype=tf.int32)
    return  x,y

batchsz = 128

(x, y), (x_val, y_val)  = datasets.fashion_mnist.load_data()
# 删除所有大小为1的维度 将y和y_val 的[50k,1,10]==>[50000, 10]
y = tf.squeeze(y)
y_val = tf.squeeze(y_val)
y = tf.one_hot(y,depth=10)# [50k,10]
y_val = tf.one_hot(y_val,depth=10) #[10k,10]
print("datasets:",x.shape,y.shape,x.min(),x.max())

#调整x,y值
train_db = tf.data.Dataset.from_tensor_slices((x,y))
#处理图片，打散并取前128张
train_db = train_db.map(preprocess).shuffle(100000).batch(batchsz)
test_db = tf.data.Dataset.from_tensor_slices((x_val,y_val))
test_db = test_db.map(preprocess).batch(batchsz)

sample = next(iter(train_db))
print('batch:',sample[0].shape,sample[1].shape)

开始进行梯度处理

将[b,28*28]==>[b,10]

class MyDense(layers.Layer):
    def __init__(self,inp_dim,outp_dim):
        super(MyDense, self).__init__()

        self.kernel = self.add_variable('w',[inp_dim,outp_dim])
        # self.bias   = self.add_variable('b',[outp_dim])


    def call(self, inputs,training=None):
        x = inputs @ self.kernel
        return x

class MyNetwork(keras.Model):

    def __init__(self):
        super(MyNetwork, self).__init__()

        self.fc1 = MyDense(28*28,256)
        self.fc2 = MyDense(256,128)
        self.fc3 = MyDense(128,64)
        self.fc4 = MyDense(64,32)
        self.fc5 = MyDense(32,10)

    def call(self, inputs, training=None):
        """
        :param inputs:[b,32,32,3]
        :param training:
        :return:
        """
        x = tf.reshape(inputs,[-1,28*28])
        #[b,28*28]==>[b,10]
        x = self.fc1(x)
        x = tf.nn.relu(x)
        x = self.fc2(x)
        x = tf.nn.relu(x)
        x = self.fc3(x)
        x = tf.nn.relu(x)
        x = self.fc4(x)
        x = tf.nn.relu(x)
        x = self.fc5(x)
        return x

开始训练

network = MyNetwork()
network.compile(optimizer=optimizers.Adam(lr=1e-3),
                loss = tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])

network.fit(train_db,epochs=5,validation_data=test_db,validation_freq=1)
network.evaluate(test_db)