Python TensorFlow2.0 cifar100 cnn vgg13

cifar100,vgg13

import tensorflow as tf
from tensorflow.keras import layers, optimizers, datasets, Sequential
import os

os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
tf.random.set_seed(2345)

conv_layers = [# 5 units of conv  + max pooling
    # unit 1
    layers.Conv2D(64, kernel_size=[3, 3], padding = 'same',activation=tf.nn.relu),
    layers.Conv2D(64, kernel_size=[3, 3], padding = 'same',activation=tf.nn.relu),
    layers.MaxPool2D(pool_size=[2,2],strides=2,padding='same'),
    # unit 2
    layers.Conv2D(128, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
    layers.Conv2D(128, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
    layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
    # unit 3
    layers.Conv2D(256, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
    layers.Conv2D(256, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
    layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
    # unit 4
    layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
    layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
    layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
    # unit 5
    layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
    layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
    layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),

]



def preprocess(x,y):
    # [0~1]
    x = tf.cast(x, dtype=tf.float32) / 255.
    y = tf.cast(y, dtype=tf.int32)
    return x, y

(x,y), (x_test,y_test)= datasets.cifar100.load_data()

y = tf.squeeze(y,axis=1)
y_test= tf.squeeze(y_test,axis=1)
print (x.shape, y.shape, x_test.shape,y_test.shape)

batchsz=64
train_db = tf.data.Dataset.from_tensor_slices((x,y))
train_db = train_db.shuffle(1000).map(preprocess).batch(batchsz)

test_db = tf.data.Dataset.from_tensor_slices((x_test,y_test))
test_db = test_db.shuffle(1000).map(preprocess).batch(batchsz)

sample = next(iter(train_db))
print ('sample:',sample[0].shape, sample[1].shape,
       tf.reduce_min(sample[0]),tf.reduce_max(sample[0]))





def main():
    #[b, 32, 32, 3]
    conv_net= Sequential(conv_layers)
    conv_net.build(input_shape=[None, 32, 32, 3])
    # x=tf.random.normal([4, 32, 32, 3])
    # out = conv_net(x)
    # print (out.shape)

    fc_net =Sequential([
        layers.Dense(256,activation=tf.nn.relu),
        layers.Dense(128,activation=tf.nn.relu),
        layers.Dense(100,activation=None)

    ])

    conv_net.build(input_shape=[None, 32, 32, 3])
    fc_net.build(input_shape=[None, 512])
    optimizer = optimizers.Adam(lr=1e-4)

    #[1, 2] + [3, 4] =>[1, 2, 3, 4]
    variables = conv_net.trainable_variables + fc_net.trainable_variables

    for epoch in range(50):

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

            with tf.GradientTape() as tape:

                out = conv_net(x)

                out = tf.reshape(out, [-1, 512])

                logits = fc_net(out)

                y_onehot = tf.one_hot(y, depth=100)

                loss = tf.losses.categorical_crossentropy(y_onehot, logits, from_logits =True)
                loss = tf.reduce_mean(loss)

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

            if step %10 ==0:
                print ('epoch:',epoch,'step:',step, 'loss:',float(loss))


        total_num = 0
        total_correct=0

        for x,y in test_db:

            out = conv_net(x)
            out = tf.reshape(out, [-1, 512])
            logits = fc_net(out)
            prob = tf.nn.softmax(logits, axis=1)
            pred = tf.argmax(prob,axis=1)
            pred = tf.cast(pred, dtype=tf.int32)

            correct = tf.cast(tf.equal(pred, y),dtype=tf.int32)
            correct = tf.reduce_sum(correct)

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

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








if __name__ =='__main__':
    main()