qq_41005604的博客

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Dense,Dropout,Convolution2D,MaxPooling2D,Flattenfrom keras.optimizers import Adamfrom keras.utils.vis_utils

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import Adamfrom keras.layers.recurrent import SimpleRNN # 最简单的RNN# 数据长度 - 一行有28

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequential# from keras.layers import Dense,Dropout,Convolution2D【做二维的卷积】,MaxPooling2D【做二维的最大池化】,Flatten【把数据扁平化】from keras.layers import Dense,Dr

交叉熵import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import SGD# 载入数据(x_train,y_train),(x_test,y_test) = mnist.load_data()# (60

import numpy as npfrom keras.datasets import mnistfrom keras.utils import np_utilsfrom keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import SGD# 载入数据'''(x_train【训练集的数据】,y_train【训练集的标签】),(x_test,y_test) = mnist.l

线性回归import kerasimport numpy as npimport matplotlib.pyplot as plt# Sequential按顺序构成的模型from keras.models import Sequential# Dense全连接层from keras.layers import Dense# 使用numpy生成100个随机点x_data = np.random.rand(100)noise = np.random.normal(0,0.01,x_data