凡科快图官方网络营销的优化和推广方式

最后一节作业是水果分类的任务，一共6类，使用之前学习的知识在代码段上进行填空。

加载ImageNet预训练的基础模型

from tensorflow import kerasbase_model = keras.applications.VGG16(weights="imagenet",input_shape=(224, 224, 3),include_top=False)

冻结基础模型

# Freeze base model
base_model.trainable = False

向模型添加新层

# Create inputs with correct shape
inputs = keras.Input(shape=(224, 224, 3))x = base_model(inputs, training=False)# Add pooling layer or flatten layer
x = keras.layers.GlobalAveragePooling2D()(x)# Add final dense layer
outputs = keras.layers.Dense(6, activation = 'softmax')(x)# Combine inputs and outputs to create model
model = keras.Model(inputs, outputs)

model.summary()

编译模型

model.compile(loss='categorical_crossentropy', optimizer='adam',  metrics=['accuracy'])

扩充数据

from tensorflow.keras.preprocessing.image import ImageDataGeneratordatagen_train = ImageDataGenerator(featurewise_center=True,  # set input mean to 0 over the datasetsamplewise_center=True,  # set each sample mean to 0rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)zoom_range = 0.1, # Randomly zoom image width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)horizontal_flip=True,  # randomly flip imagesvertical_flip=False)
datagen_valid = ImageDataGenerator(featurewise_center=True,  # set input mean to 0 over the datasetsamplewise_center=True,  # set each sample mean to 0rotation_range=10,  # randomly rotate images in the range (degrees, 0 to 180)zoom_range = 0.1, # Randomly zoom image width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)horizontal_flip=True,  # randomly flip imagesvertical_flip=False)

加载数据集

# load and iterate training dataset
train_it = datagen_train.flow_from_directory("data/fruits/train", target_size=(224, 224), color_mode="rgb",class_mode="categorical",
)# load and iterate validation dataset
valid_it = datagen_valid.flow_from_directory("data/fruits/valid",                                     target_size=(224, 224), color_mode="rgb",class_mode="categorical",
)

训练模型
现在开始训练模型！将训练和测试数据集传递给fit函数，并设置所需的训练次数（epochs）

model.fit(train_it,validation_data=valid_it,steps_per_epoch=train_it.samples/train_it.batch_size,validation_steps=valid_it.samples/valid_it.batch_size,epochs=10)

其实到这里已经满足了评估需求，达到了92%以上的准确率
所以可以不进行微调的部分，直接运行后边的代码

就可以生成证书了