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目录 Outline Save/load weights Save/load entire model saved_model Outline save/load weights # 记录部分信息 save/load entire model # 记录所有信息 saved_model # 通用,包括 阅读全文
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目录 Outline keras.Sequential Layer/Model MyDense MyModel Outline keras.Sequential keras.layers.Layer keras.Model keras.Sequential model.trainable_varia 阅读全文
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目录 Keras != tf.keras Outline1 Metrics Step1.Build a meter Step2.Update data Step3.Get Average data Clear buffer Outline2 Compile + Fit Individual loss 阅读全文
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目录 Keras != tf.keras Outline1 Metrics Step1.Build a meter Step2.Update data Step3.Get Average data Clear buffer Outline2 Compile + Fit Individual loss 阅读全文
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FashionMNIST import tensorflow as tf from tensorflow import keras from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics def p 阅读全文
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目录 Himmelblau function Minima Plot Gradient Descent Himmelblau function f(x,y)=(x2+y−11)2+(x+y2−7)2f(x,y)=(x2+y−11)2+(x+y2−7)2 Minima f(3.0,2.0)=0.0 f 阅读全文
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目录 Chain rule Multi-output Perceptron Multi-Layer Perceptron Chain rule Multi-output Perceptron Multi-Layer Perceptron 对于多隐藏层结构的神经网络可以把隐藏层的节点看成输出层的节点 阅读全文
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目录 Derivative Rules Chain rule Derivative Rules Chain rule import tensorflow as tf x = tf.constant(1.) w1 = tf.constant(2.) b1 = tf.constant(1.) w2 = 阅读全文
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目录 Multi-output Perceptron Multi-output Perceptron E=12∑(O1i−ti)2E=12∑(Oi1−ti)2 对于多输出感知机,每个输出元只和输出元上的x和w和σσ 有关。 import tensorflow as tf x = tf.random. 阅读全文
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目录 recap Perceptron Derivative recap y=XW+by=XW+b y=∑xi∗wi+by=∑xi∗wi+b Perceptron x0ixi0 i表示当成第i个节点 w0ijwij0 表示当层的第i个节点,j表示下一个隐藏层的第j个节点 σσ 表示激活函数后的节点 阅读全文