tensorflow2.0——CIFAR100卷积+全连接实战

import tensorflow as tf
#   设置相关底层配置
physical_devices = tf.config.experimental.list_physical_devices('GPU')
assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
tf.config.experimental.set_memory_growth(physical_devices[0], True)

def preprocess(x,y):
    x = tf.cast(x,dtype=tf.float32) / 255
    y = tf.cast(y,dtype=tf.int32)
    return x,y

#   ###############数据加载以及处理#############
(x,y),(x_test,y_test) = tf.keras.datasets.cifar100.load_data()
#   将y的1维度去掉
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:')
print(x.shape,y.shape,x_test.shape,y_test.shape)
train_db = tf.data.Dataset.from_tensor_slices((x,y))
train_db = train_db.shuffle(1000).batch(64)
test_db = tf.data.Dataset.from_tensor_slices((x_test,y_test))
test_db = test_db.shuffle(1000).batch(200)
#   打印看下数据的形状
sample = next(iter(train_db))
print('sample:',sample[0].shape,sample[1].shape
      ,tf.reduce_min(sample[0]),tf.reduce_max(sample[0]))

if __name__ == '__main__':
    #   卷积网络结构
    conv_layers = [
        #   第一部分（两卷积一池化）
        tf.keras.layers.Conv2D(64, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
        tf.keras.layers.Conv2D(64, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
        tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
        #   第二部分（两卷积一池化）
        tf.keras.layers.Conv2D(128, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
        tf.keras.layers.Conv2D(128, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
        tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
        #   第三部分（两卷积一池化）
        tf.keras.layers.Conv2D(256, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
        tf.keras.layers.Conv2D(256, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
        tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
        #   第四部分（两卷积一池化）
        tf.keras.layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
        tf.keras.layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
        tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
        #   第五部分（两卷积一池化）
        tf.keras.layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
        tf.keras.layers.Conv2D(512, kernel_size=[3, 3], padding='same', activation=tf.nn.relu),
        tf.keras.layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='same'),
    ]
    #   [b,32,32,3] => [b,1,1,512]  卷积层操作
    conv_net = tf.keras.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)
    #   全连接层操作
    fc_net = tf.keras.Sequential([
        tf.keras.layers.Dense(256,activation=tf.nn.relu),
        tf.keras.layers.Dense(128, activation=tf.nn.relu),
        tf.keras.layers.Dense(100, activation=None)
    ])
    fc_net.build(input_shape=[None,512])
    #   把卷积和全连接层的参数合并 ‘+’可以把两个列表直接合并
    variables = conv_net.trainable_variables + fc_net.trainable_variables
    #   定义优化器
    optimizer = tf.optimizers.Adam(lr=1e-4)
    #   训练
    for epoch in range(50):
        for step,(x,y) in enumerate(train_db):
            with tf.GradientTape() as tape:
                #   [b,32,32,3] => [b,1,1,512]
                out = conv_net(x)
                #   flatten
                out = tf.reshape(out,[-1,512])
                #    [b,512] =>[b,100]
                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 % 100 == 0:
                print(epoch,step,'loss:',float(loss))
        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,tf.int32)
            correct = tf.cast(tf.equal(pred,y),dtype=tf.int32)
            correct = tf.reduce_mean(tf.cast(correct,dtype=tf.float32))
        print('acc:',float(correct))