AI实战 | Tensorflow自定义数据集和迁移学习（附代码下载）

def preprocess(x,y):
    # x: 图片的路径，y：图片的数字编码
    x = tf.io.read_file(x)
    x = tf.image.decode_jpeg(x, channels=3) # RGBA
    x = tf.image.resize(x, [244, 244])
    return x, y

# 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)

# 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)

class ResNet(keras.Model):
    def __init__(self, num_classes, initial_filters=16, **kwargs):
        super(ResNet, self).__init__(**kwargs)
        self.stem = layers.Conv2D(initial_filters, 3, strides=3, padding='valid')
        self.blocks = keras.models.Sequential([
            ResnetBlock(initial_filters * 2, strides=3),
            ResnetBlock(initial_filters * 2, strides=1),
            # layers.Dropout(rate=0.5),
            ResnetBlock(initial_filters * 4, strides=3),
            ResnetBlock(initial_filters * 4, strides=1),
            ResnetBlock(initial_filters * 8, strides=2),
            ResnetBlock(initial_filters * 8, strides=1),
            ResnetBlock(initial_filters * 16, strides=2),
            ResnetBlock(initial_filters * 16, strides=1),
        ])
        self.final_bn = layers.BatchNormalization()
        self.avg_pool = layers.GlobalMaxPool2D()
        self.fc = layers.Dense(num_classes)
    def call(self, inputs, training=None):
        # print('x:',inputs.shape)
        out = self.stem(inputs，training=training)
        out = tf.nn.relu(out)
        # print('stem:',out.shape)
        out = self.blocks(out, training=training)
        # print('res:',out.shape)
        out = self.final_bn(out, training=training)
        # out = tf.nn.relu(out)
        out = self.avg_pool(out)
        # print('avg_pool:',out.shape)
        out = self.fc(out)
        # print('out:',out.shape)
        return out

resnet = keras.Sequential([
    layers.Conv2D(16,5,3),
    layers.MaxPool2D(3,3),
    layers.ReLU(),
    layers.Conv2D(64,5,3),
    layers.MaxPool2D(2,2),
    layers.ReLU(),
    layers.Flatten(),
    layers.Dense(64),
    layers.ReLU(),
    layers.Dense(5)
])
resnet = ResNet(5)
resnet.build(input_shape=(4, 224, 224, 3))
resnet.summary()
early_stopping = EarlyStopping(
    monitor='val_accuracy',
    min_delta=0.001,
    patience=5
)
resnet.compile(optimizer=optimizers.Adam(lr=1e-3),
               loss=losses.CategoricalCrossentropy(from_logits=True),
               metrics=['accuracy'])
resnet.fit(db_train, validation_data=db_val, validation_freq=1, epochs=100,
           callbacks=[early_stopping])
resnet.evaluate(db_test)

net = keras.applications.VGG19(weights='imagenet', include_top=False,
                               pooling='max')
net.trainable = False
newnet = keras.Sequential([
    net,
    layers.Dense(5)
])
newnet.build(input_shape=(4,224,224,3))
newnet.summary()
early_stopping = EarlyStopping(
    monitor='val_accuracy',
    min_delta=0.001,
    patience=5
)
newnet.compile(optimizer=optimizers.Adam(lr=1e-3),
               loss=losses.CategoricalCrossentropy(from_logits=True),
               metrics=['accuracy'])
newnet.fit(db_train, validation_data=db_val, validation_freq=1, epochs=100,
           callbacks=[early_stopping])
newnet.evaluate(db_test)