keras-简单实现Mnist数据集分类
1.载入数据以及预处理
import numpy as np from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import * from keras.optimizers import SGD import os import tensorflow as tf # 载入数据 (x_train,y_train),(x_test,y_test) = mnist.load_data() # 预处理 # 将(60000,28,28)转化为(600000,784),好输入展开层 x_train = x_train.reshape(x_train.shape[0],-1)/255.0 x_test= x_test.reshape(x_test.shape[0],-1)/255.0 # 将输出转化为one_hot编码 y_train = np_utils.to_categorical(y_train,num_classes=10) y_test = np_utils.to_categorical(y_test,num_classes=10)
2.创建网络打印训练结果
# 创建网络 model = Sequential([ # 输入784输出10个 Dense(units=10,input_dim=784,bias_initializer='one',activation='softmax') ]) # 编译 # 自定义优化器 sgd = SGD(lr=0.1) model.compile(optimizer=sgd, loss='mse', # 得到训练过程中的准确率 metrics=['accuracy']) model.fit(x_train,y_train,batch_size=32,epochs=10,validation_split=0.2) # 评估模型 loss,acc = model.evaluate(x_test,y_test,) print('\ntest loss',loss) print('test acc',acc)
out:
Epoch 1/10
32/48000 [..............................] - ETA: 2:27 - loss: 0.0905 - acc: 0.1875
1248/48000 [..............................] - ETA: 5s - loss: 0.0907 - acc: 0.1346
......
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Epoch 10/10
45952/48000 [===========================>..] - ETA: 0s - loss: 0.0164 - acc: 0.9005
47616/48000 [============================>.] - ETA: 0s - loss: 0.0163 - acc: 0.9008
48000/48000 [==============================] - 2s 37us/step - loss: 0.0163 - acc: 0.9010 - val_loss: 0.0149 - val_acc: 0.9084
32/10000 [..............................] - ETA: 4s
3360/10000 [=========>....................] - ETA: 0s
5824/10000 [================>.............] - ETA: 0s
8512/10000 [========================>.....] - ETA: 0s
10000/10000 [==============================] - 0s 20us/step
test loss 0.015059704356454312
test acc 0.908