tensorflow(二十六)：Keras计算准确率和损失

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)


# 第一步： 这里要对loss和accuracy做一个跟踪。所以这里建立了2个metrics
# 一个是accuracy的metrics，一个是求loss均值的metrics.
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))


        # 第二步： 每次loss计算完之后会更新一次metrics列表，这样loss会非常的准确。
        loss_meter.update_state(loss)

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

    if step % 100 == 0:

        # 第三步： 测试的时候把loss的result打印出来。
        print(step, ‘loss:‘, loss_meter.result().numpy())

        # 第四步： 把当前的loss buffer缓存清理掉。======这样每隔100次打印出来的loss是前100次的平均loss,而不是第100次了。
        # 数值会看起来非常的稳定。
        loss_meter.reset_states()

    # evaluate 测试的时候。我们来看acc metrics
    if step % 500 == 0:
        total, total_correct = 0., 0

        # 首先： acc_meter缓存清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]

            # 然后： acc_meter的值更新缓存到列表。
            acc_meter.update_state(y, pred)

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