x = tf.io.read_file(x)
x = tf.image.decode_jpeg(x, channels=3)
x = tf.image.resize(x, [244, 244])
x = tf.image.random_flip_left_right(x)
x = tf.image.random_crop(x, [224, 224, 3])
x = tf.cast(x, dtype=tf.float32) / 255.0
x = normalize(x)
y = tf.convert_to_tensor(y)
y = tf.one_hot(y, depth=num_classes)
return x, y
# 数据读取
train_images, train_labels, name2label = load_pokemon(data_path, 'train')
train_dataloader = tf.data.Dataset.from_tensor_slices(
(train_images, train_labels))
train_dataloader = train_dataloader.shuffle(
len(train_images)).map(preprocess).batch(batch_size)
val_images, val_labels, name2label = load_pokemon(data_path, 'val')
val_dataloader = tf.data.Dataset.from_tensor_slices((val_images, val_labels))
val_dataloader = val_dataloader.map(preprocess).batch(batch_size)
test_images, test_labels, name2label = load_pokemon(data_path, 'test')
test_dataloader = tf.data.Dataset.from_tensor_slices(
(test_images, test_labels))
test_dataloader = test_dataloader.map(preprocess).batch(batch_size)
# 网络搭建
x = tf.image.random_flip_left_right(x)
# x = tf.image.random_flip_up_down(x)
x = tf.image.random_crop(x, [224,224,3])
# x: [0,255]=> -1~1
x = tf.cast(x, dtype=tf.float32) / 255.
x = normalize(x)
y = tf.convert_to_tensor(y)
y = tf.one_hot(y, depth=5)
return x, y
batchsz = 8
# creat train db 一般训练的时候需要shuffle。其它是不需要的。
images, labels, table = load_pokemon('/Users/wzk/PythonCode/MyPy01/宝可梦/pokeman',mode='train')
db_train = tf.data.Dataset.from_tensor_slices((images, labels)) # 变成个Dataset对象。
db_train = db_train.shuffle(1000).map(preprocess).batch(batchsz) # map函数图片路径变为内容。
# crate validation db
images2, labels2, table = load_pokemon('/Users/wzk/PythonCode/MyPy01/宝可梦/pokeman',mode='val')
db_val = tf.data.Dataset.from_tensor_slices((images2, labels2))
db_val = db_val.map(preprocess).batch(batchsz)
# create test db
images3, labels3, table = load_pokemon('/Users/wzk/PythonCode/MyPy01/宝可梦/pokeman',mode='test')
db_test = tf.data.Dataset.from_tensor_slices((images3, labels3))
db_test = db_test.map(preprocess).batch(batchsz)
# 训练样本太小了，resnet网络表达能力很强。这里换成4层小的网络了。
resnet = keras.Sequential([
layers.Conv2D(16,5,3),
x = tf.image.random_flip_left_right(x)
x = tf.image.random_flip_up_down(x)
x = tf.image.random_crop(x, [224, 224, 3])
# x: [0,255]=> -1~1
x = tf.cast(x, dtype=tf.float32) / 255.
x = normalize(x)
y = tf.convert_to_tensor(y)
y = tf.one_hot(y, depth=5)
return x, y
batchsz = 32
# 创建训练集Datset对象
images, labels, table = load_pokemon('pokemon', mode='train')
db_train = tf.data.Dataset.from_tensor_slices((images, labels))
db_train = db_train.shuffle(1000).map(preprocess).batch(batchsz)
# 创建验证集Datset对象
images2, labels2, table = load_pokemon('pokemon', mode='val')
db_val = tf.data.Dataset.from_tensor_slices((images2, labels2))
db_val = db_val.map(preprocess).batch(batchsz)
# 创建测试集Datset对象
images3, labels3, table = load_pokemon('pokemon', mode='test')
db_test = tf.data.Dataset.from_tensor_slices((images3, labels3))
db_test = db_test.map(preprocess).batch(batchsz)
# 加载DenseNet网络模型，并去掉最后一层全连接层，最后一个池化层设置为max pooling
net = keras.applications.DenseNet121(weights='imagenet',
include_top=False,
pooling='max')