Keras != tf.keras
▪ datasets
▪ layers
▪ losses
▪ metrics(主要)
▪ optimizers

1 Keras.Metrics

1.Metrics(新建一个Matrics)
2.update_state(添加数据)
3.result().numpy()(得到结果)
4.reset_states(清0)

1.1 step1.Build meter

第一个数准确度的metrics,第二个是平均值的metrics。

1.2 step2.Update data

我们向那个里面添加数据，我们就调用那个名字.update_state()

1.3 step3.Get Average data

可以得到一个结果，就是将他转化成.result().numpy()

1.4 step4.clear buffer

就是如果我们向清理这个里面的缓存的话，我们调用.reset_states()

我们之前的代码用了那些API之后：

import tensorflow as tf
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


batchsz = 128
(x, y), (x_val, y_val) = datasets.mnist.load_data()
print('datasets:', x.shape, y.shape, x.min(), x.max())



db = tf.data.Dataset.from_tensor_slices((x,y))
db = db.map(preprocess).shuffle(60000).batch(batchsz).repeat(10)

ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.map(preprocess).batch(batchsz) 




network = Sequential([layers.Dense(256, activation='relu'),
                     layers.Dense(128, activation='relu'),
                     layers.Dense(64, activation='relu'),
                     layers.Dense(32, activation='relu'),
                     layers.Dense(10)])
network.build(input_shape=(None, 28*28))
network.summary()

optimizer = optimizers.Adam(lr=0.01)

acc_meter = metrics.Accuracy() #定义
loss_meter = metrics.Mean() #定义


for step, (x,y) in enumerate(db):

    with tf.GradientTape() as tape:
        # [b, 28, 28] => [b, 784]
        x = tf.reshape(x, (-1, 28*28))
        # [b, 784] => [b, 10]
        out = network(x)
        # [b] => [b, 10]
        y_onehot = tf.one_hot(y, depth=10) 
        # [b]
        loss = tf.reduce_mean(tf.losses.categorical_crossentropy(y_onehot, out, from_logits=True))

        loss_meter.update_state(loss) #更新

 

    grads = tape.gradient(loss, network.trainable_variables)
    optimizer.apply_gradients(zip(grads, network.trainable_variables))


    if step % 100 == 0:

        print(step, 'loss:', loss_meter.result().numpy()) #输出
        loss_meter.reset_states() #清空


    # evaluate
    if step % 500 == 0:
        total, total_correct = 0., 0
        acc_meter.reset_states() #清空

        for step, (x, y) in enumerate(ds_val): 
            # [b, 28, 28] => [b, 784]
            x = tf.reshape(x, (-1, 28*28))
            # [b, 784] => [b, 10]
            out = network(x) 


            # [b, 10] => [b] 
            pred = tf.argmax(out, axis=1) 
            pred = tf.cast(pred, dtype=tf.int32)
            # bool type 
            correct = tf.equal(pred, y)
            # bool tensor => int tensor => numpy
            total_correct += tf.reduce_sum(tf.cast(correct, dtype=tf.int32)).numpy()
            total += x.shape[0]

            acc_meter.update_state(y, pred)


        print(step, 'Evaluate Acc:', total_correct/total, acc_meter.result().numpy())