深度学习分类fashion_mnist数据集

随便搭建了一个网络，分类fashion_mnist数据集

import  os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets,layers,optimizers,Sequential,metrics


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

    return x,y


#加载数据集
(x,y),(x_test,y_test) = datasets.fashion_mnist.load_data()
print(x.shape,y.shape)
print(x_test.shape,y_test.shape)

batchsz = 128
db = tf.data.Dataset.from_tensor_slices((x,y))
db = db.map(preprocess).shuffle(10000).batch(batchsz)



db_test = tf.data.Dataset.from_tensor_slices((x_test,y_test))
db_test = db_test.map(preprocess).shuffle(10000).batch(batchsz)





db_iter = iter(db)
sample = next(db_iter)

print(sample[0].shape,sample[1].shape)

#构建网路
model = Sequential([
    layers.Dense(256,activation=tf.nn.relu),
    layers.Dense(128,activation=tf.nn.relu),
    layers.Dense(64,activation=tf.nn.relu),
    layers.Dense(32,activation=tf.nn.relu),
    layers.Dense(10)
])
model.build(input_shape=[None,28*28])
model.summary()

#优化器
optimizer = optimizers.Adam(lr=1e-3)




def main():

    for epoch in range(30):
        for step ,(x,y) in enumerate(db):
            x = tf.reshape(x,[-1,28*28])
            with tf.GradientTape() as tape:
                logits = model(x)
                y_onehot = tf.one_hot(y,depth=10)
                loss_mse = tf.reduce_mean(tf.losses.MSE(y_onehot,logits))
                loss_ce = tf.losses.categorical_crossentropy(y_onehot,logits,from_logits=True)
                loss_ce = tf.reduce_mean(loss_ce)

            grads = tape.gradient(loss_ce,model.trainable_variables)
            optimizer.apply_gradients(zip(grads,model.trainable_variables))

            if(step%100==0):
                print(epoch,step,'loss',float(loss_ce),float(loss_mse))

        # test
        total_correct = 0
        total_num = 0
        for x,y in db_test:
            x = tf.reshape(x, [-1, 28 * 28])
            logits = model(x)
            prob = tf.nn.softmax(logits,axis=1)

            pred = tf.argmax(prob,axis=1)

            pred = tf.cast(pred,dtype=tf.int32)

            correct = tf.equal(pred,y)

            correct = tf.reduce_sum(tf.cast(correct,dtype=tf.int32))

            total_correct += int(correct)
            total_num += x.shape[0]

        acc = total_correct / total_num
        print('acc: ',acc)

    pass









if __name__ == '__main__':
    main()

完结撒花！！！