PaddlePaddle 深度学习实战（第一部分）

今天药忘吃喽~ 2023-07-02 03:29 89阅读 0赞

# **[PaddlePaddle 深度学习实战（第一部分）][PaddlePaddle]** #

# **[PaddlePaddle 深度学习实战（第二部分）][PaddlePaddle 1]** #

# **[PaddlePaddle 深度学习实战（第三部分）][PaddlePaddle 2]** #

# **[PaddlePaddle 深度学习实战（第四部分）][PaddlePaddle 3]** #

# **[PaddlePaddle 深度学习实战（第五部分）][PaddlePaddle 4]** #

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# ** 深度学习框架的作用** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70][]

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# **AI术语 ** #

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# **激活函数、优化方法、损失函数(成本函数)** #

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# **二分类、多分类与多标签问题的区别，对应损失函数的选择** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 17][]

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# **二分类、多分类、多标签和多输出问题解析** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 23][]

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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

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 29][]

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# **高数** #

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# **线性代数基础：向量、矩阵、点乘(内积)、元素乘、转置、权重向量的L1范数、L2范数(L1/L2正则化)**![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 31][] #

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# **微积分基础：导数、偏导数、链式法则、梯度** #

# ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 35][] #

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[PaddlePaddle]: https://azusa.blog.csdn.net/article/details/104084457
[PaddlePaddle 1]: https://azusa.blog.csdn.net/article/details/104084509
[PaddlePaddle 2]: https://azusa.blog.csdn.net/article/details/104084574
[PaddlePaddle 3]: https://azusa.blog.csdn.net/article/details/104084604
[PaddlePaddle 4]: https://azusa.blog.csdn.net/article/details/104084767
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