環境配置我就不再這贅述了

1利用kares搭建分類模型

import tensorflow as tf import matplotlib as mpl import matplotlib.pyplot as plt %matplotlib inline import numpy as np import sklearn import pandas as pd import os import sys import time from tensorlow import kerasprint(tf.__version__) print(sys.version_info) for module in mpl,np,pd,sklearn,tf,keras:print(module.__name__, module.__version__)%%加載訓練集和測試集 fashion_mnist = keras.datasets.fashion_mnist (x_train_all,y_train_all),(x_test,y_test) = fashion_mnist.load_data() x_vaild,x_train = x_train_all[:5000],x_train_all[5000:] y_vaild,y_train = y_train_all[:5000],y_train_all[5000:] print(x_vaild.shape,y_vaild.shape) print(x_train.shape,y_train.shape) print(x_test.shape,y_test.shape)def show_single_iamge(image_arr):plt.imshow(img_arr,cmap ="binary")plt.show() show_single_image(x_train[0])model = keras.model.Sequential() modle.add(keras.layers.Flatten(input_shape[28,28])) model.add(keras.layers.dense(300,activation="relu")) model.add(keras.layers.dense(100,activation="relu")) model.add(keras.layers.dense(10,activetion="softmax"))model.compile(loss ="sparse_categorical_crossentropy",optimizer ="sgd",metrics = ["accuracy"]) %查看層 model.layersmodel.summary

?

1.1激活函數:

relu:

y=max(1,x)

softmax:

將向量變成概率分布。

x=[x1,x2,x3]

y=[e^x1/sum,e^x2/sum,[e^x3/sum] ;? (sum = e^x1+e^x2+e^x3)

1.2全連接層參數計算

假設輸入為x:[28,28]=784

x*w+b=>w.shape = [784,300], b.shape = [300]

總的參數數量 = w.shape + b.shape

總結

以上是生活随笔為你收集整理的tensorflow2.0学习(一)的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。