Keras高层API之Metrics

Posted 2021-03-13 赵代码

在tf.keras中，metrics其实就是起到了一个测量表的作用，即测量损失或者模型精度的变化。metrics的使用分为以下四步：

step1:Build a meter

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

step2:Update data

loss_meter.update_state(loss)
acc_meter.update_state(y,pred)

step3:Get Average data

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

清除缓存：

if step % 100 == 0:
    print(step,\'loss:\',loss_meter.result().numpy())
    loss_meter.reset_states()

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

实战：

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()
loss_meter = metrics.Mean()


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)

 

    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()) 
        loss_meter.reset_states()


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

        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())