深度学习框架的作用

AI术语

激活函数、优化方法、损失函数(成本函数)

二分类、多分类与多标签问题的区别，对应损失函数的选择

二分类、多分类、多标签和多输出问题解析

创建模型的关键代码
class FashionNet:
	@staticmethod
	def build_category_branch(inputs, numCategories,
		finalAct="softmax", chanDim=-1):
		# utilize a lambda layer to convert the 3 channel input to a
		# grayscale representation
		x = Lambda(lambda c: tf.image.rgb_to_grayscale(c))(inputs)

		# CONV => RELU => POOL
		x = Conv2D(32, (3, 3), padding="same")(x)
		x = Activation("relu")(x)
		x = BatchNormalization(axis=chanDim)(x)
		x = MaxPooling2D(pool_size=(3, 3))(x)
		x = Dropout(0.25)(x)

    # Omit some similar code

		# define a branch of output layers for the number of different
		# clothing categories (i.e., shirts, jeans, dresses, etc.)
		x = Flatten()(x)
		x = Dense(256)(x)
		x = Activation("relu")(x)
		x = BatchNormalization()(x)
		x = Dropout(0.5)(x)
		x = Dense(numCategories)(x)
		x = Activation(finalAct, name="category_output")(x)

		# return the category prediction sub-network
		return x

	@staticmethod
	def build_color_branch(inputs, numColors, finalAct="softmax",
		chanDim=-1):
		# CONV => RELU => POOL
		x = Conv2D(16, (3, 3), padding="same")(inputs)
		x = Activation("relu")(x)
		x = BatchNormalization(axis=chanDim)(x)
		x = MaxPooling2D(pool_size=(3, 3))(x)
		x = Dropout(0.25)(x)

    # Omit some similar code

		# define a branch of output layers for the number of different
		# colors (i.e., red, black, blue, etc.)
		x = Flatten()(x)
		x = Dense(128)(x)
		x = Activation("relu")(x)
		x = BatchNormalization()(x)
		x = Dropout(0.5)(x)
		x = Dense(numColors)(x)
		x = Activation(finalAct, name="color_output")(x)

		# return the color prediction sub-network
		return x

	@staticmethod
	def build(width, height, numCategories, numColors,
		finalAct="softmax"):
		# initialize the input shape and channel dimension (this code
		# assumes you are using TensorFlow which utilizes channels
		# last ordering)
		inputShape = (height, width, 3)
		chanDim = -1

		# construct both the "category" and "color" sub-networks
		inputs = Input(shape=inputShape)
		categoryBranch = FashionNet.build_category_branch(inputs,
			numCategories, finalAct=finalAct, chanDim=chanDim)
		colorBranch = FashionNet.build_color_branch(inputs,
			numColors, finalAct=finalAct, chanDim=chanDim)

		# create the model using our input (the batch of images) and
		# two separate outputs -- one for the clothing category
		# branch and another for the color branch, respectively
		model = Model(
			inputs=inputs,
			outputs=[categoryBranch, colorBranch],
			name="fashionnet")

		# return the constructed network architecture
		return model