acc_meter = metrics.Accuracy()

loss_meter = metrics.Mean()

loss_meter.update_state(loss)

acc_meter.update_state(y,pred)

print(step,'loss:',loss_meter.result().numpy())

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

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()

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())