mxnet 训练 MNIST

偏执的太偏执、 2022-05-08 15:10 241阅读 0赞

import numpy as np  
import mxnet as mx  
import logging

logging.getLogger().setLevel(logging.DEBUG)

batch\_size = 100  
mnist = mx.test\_utils.get\_mnist()  
train\_iter = mx.io.NDArrayIter(mnist\['train\_data'\], mnist\['train\_label'\], batch\_size, shuffle=True)  
val\_iter = mx.io.NDArrayIter(mnist\['test\_data'\], mnist\['test\_label'\], batch\_size)

data = mx.sym.var('data')  
\# first conv layer  
conv1= mx.sym.Convolution(data=data, kernel=(5,5), num\_filter=20)  
tanh1= mx.sym.Activation(data=conv1, act\_type="tanh")  
pool1= mx.sym.Pooling(data=tanh1, pool\_type="max", kernel=(2,2), stride=(2,2))  
\# second conv layer  
conv2= mx.sym.Convolution(data=pool1, kernel=(5,5), num\_filter=50)  
tanh2= mx.sym.Activation(data=conv2, act\_type="tanh")  
pool2= mx.sym.Pooling(data=tanh2, pool\_type="max", kernel=(2,2), stride=(2,2))  
\# first fullc layer  
flatten= mx.sym.Flatten(data=pool2)  
fc1= mx.symbol.FullyConnected(data=flatten, num\_hidden=500)  
tanh3= mx.sym.Activation(data=fc1, act\_type="tanh")  
\# second fullc  
fc2= mx.sym.FullyConnected(data=tanh3, num\_hidden=10)  
\# softmax loss  
lenet= mx.sym.SoftmaxOutput(data=fc2, name='softmax')

\# create a trainable module on GPU 0  
lenet\_model = mx.mod.Module(  
symbol=lenet,  
context=mx.cpu())

\# train with the same  
lenet\_model.fit(train\_iter,  
eval\_data=val\_iter,  
optimizer='sgd',  
optimizer\_params=\{'learning\_rate':0.1\},  
eval\_metric='acc',  
batch\_end\_callback = mx.callback.Speedometer(batch\_size, 100),  
num\_epoch=10)  
显示结果：

INFO:root:train-labels-idx1-ubyte.gz exists, skip to downloada  
INFO:root:train-images-idx3-ubyte.gz exists, skip to downloada  
INFO:root:t10k-labels-idx1-ubyte.gz exists, skip to downloada  
INFO:root:t10k-images-idx3-ubyte.gz exists, skip to downloada  
INFO:root:Epoch\[0\] Batch \[100\] Speed: 722.13 samples/sec accuracy=0.103366  
INFO:root:Epoch\[0\] Batch \[200\] Speed: 713.60 samples/sec accuracy=0.115500  
INFO:root:Epoch\[0\] Batch \[300\] Speed: 714.94 samples/sec accuracy=0.110900  
INFO:root:Epoch\[0\] Batch \[400\] Speed: 709.44 samples/sec accuracy=0.111200  
INFO:root:Epoch\[0\] Batch \[500\] Speed: 714.26 samples/sec accuracy=0.114600  
INFO:root:Epoch\[0\] Train-accuracy=0.113434  
INFO:root:Epoch\[0\] Time cost=83.928  
INFO:root:Epoch\[0\] Validation-accuracy=0.113500  
INFO:root:Epoch\[1\] Batch \[100\] Speed: 716.48 samples/sec accuracy=0.161683  
INFO:root:Epoch\[1\] Batch \[200\] Speed: 675.00 samples/sec accuracy=0.591100  
INFO:root:Epoch\[1\] Batch \[300\] Speed: 668.75 samples/sec accuracy=0.861500  
INFO:root:Epoch\[1\] Batch \[400\] Speed: 647.97 samples/sec accuracy=0.899400  
INFO:root:Epoch\[1\] Batch \[500\] Speed: 666.97 samples/sec accuracy=0.920600  
INFO:root:Epoch\[1\] Train-accuracy=0.932828  
INFO:root:Epoch\[1\] Time cost=88.947  
INFO:root:Epoch\[1\] Validation-accuracy=0.940800  
INFO:root:Epoch\[2\] Batch \[100\] Speed: 660.08 samples/sec accuracy=0.944653  
INFO:root:Epoch\[2\] Batch \[200\] Speed: 650.96 samples/sec accuracy=0.954200  
INFO:root:Epoch\[2\] Batch \[300\] Speed: 669.57 samples/sec accuracy=0.958800  
INFO:root:Epoch\[2\] Batch \[400\] Speed: 644.97 samples/sec accuracy=0.963200  
INFO:root:Epoch\[2\] Batch \[500\] Speed: 654.75 samples/sec accuracy=0.967100  
INFO:root:Epoch\[2\] Train-accuracy=0.969394  
INFO:root:Epoch\[2\] Time cost=91.671  
INFO:root:Epoch\[2\] Validation-accuracy=0.973100  
INFO:root:Epoch\[3\] Batch \[100\] Speed: 660.64 samples/sec accuracy=0.970990  
INFO:root:Epoch\[3\] Batch \[200\] Speed: 669.49 samples/sec accuracy=0.974400  
INFO:root:Epoch\[3\] Batch \[300\] Speed: 650.88 samples/sec accuracy=0.973900  
INFO:root:Epoch\[3\] Batch \[400\] Speed: 665.29 samples/sec accuracy=0.976800  
INFO:root:Epoch\[3\] Batch \[500\] Speed: 664.31 samples/sec accuracy=0.976000  
INFO:root:Epoch\[3\] Train-accuracy=0.978384  
INFO:root:Epoch\[3\] Time cost=90.576  
INFO:root:Epoch\[3\] Validation-accuracy=0.981600  
INFO:root:Epoch\[4\] Batch \[100\] Speed: 657.94 samples/sec accuracy=0.978416  
INFO:root:Epoch\[4\] Batch \[200\] Speed: 651.82 samples/sec accuracy=0.980100  
INFO:root:Epoch\[4\] Batch \[300\] Speed: 653.96 samples/sec accuracy=0.982100  
INFO:root:Epoch\[4\] Batch \[400\] Speed: 647.17 samples/sec accuracy=0.982400  
INFO:root:Epoch\[4\] Batch \[500\] Speed: 656.77 samples/sec accuracy=0.981900  
INFO:root:Epoch\[4\] Train-accuracy=0.984646  
INFO:root:Epoch\[4\] Time cost=91.804  
INFO:root:Epoch\[4\] Validation-accuracy=0.983400  
INFO:root:Epoch\[5\] Batch \[100\] Speed: 649.50 samples/sec accuracy=0.983069  
INFO:root:Epoch\[5\] Batch \[200\] Speed: 649.20 samples/sec accuracy=0.984600  
INFO:root:Epoch\[5\] Batch \[300\] Speed: 647.68 samples/sec accuracy=0.985200  
INFO:root:Epoch\[5\] Batch \[400\] Speed: 658.71 samples/sec accuracy=0.985900  
INFO:root:Epoch\[5\] Batch \[500\] Speed: 646.41 samples/sec accuracy=0.984900  
INFO:root:Epoch\[5\] Train-accuracy=0.987071  
INFO:root:Epoch\[5\] Time cost=92.219  
INFO:root:Epoch\[5\] Validation-accuracy=0.985100  
INFO:root:Epoch\[6\] Batch \[100\] Speed: 645.74 samples/sec accuracy=0.985842  
INFO:root:Epoch\[6\] Batch \[200\] Speed: 653.40 samples/sec accuracy=0.987800  
INFO:root:Epoch\[6\] Batch \[300\] Speed: 646.12 samples/sec accuracy=0.987800  
INFO:root:Epoch\[6\] Batch \[400\] Speed: 641.82 samples/sec accuracy=0.988100  
INFO:root:Epoch\[6\] Batch \[500\] Speed: 643.05 samples/sec accuracy=0.986900  
INFO:root:Epoch\[6\] Train-accuracy=0.989192  
INFO:root:Epoch\[6\] Time cost=96.044  
INFO:root:Epoch\[6\] Validation-accuracy=0.986100  
INFO:root:Epoch\[7\] Batch \[100\] Speed: 653.00 samples/sec accuracy=0.987327  
INFO:root:Epoch\[7\] Batch \[200\] Speed: 650.61 samples/sec accuracy=0.988800  
INFO:root:Epoch\[7\] Batch \[300\] Speed: 649.02 samples/sec accuracy=0.989100  
INFO:root:Epoch\[7\] Batch \[400\] Speed: 644.93 samples/sec accuracy=0.990000  
INFO:root:Epoch\[7\] Batch \[500\] Speed: 554.87 samples/sec accuracy=0.988700  
INFO:root:Epoch\[7\] Train-accuracy=0.990202  
INFO:root:Epoch\[7\] Time cost=94.743  
INFO:root:Epoch\[7\] Validation-accuracy=0.987600  
INFO:root:Epoch\[8\] Batch \[100\] Speed: 649.92 samples/sec accuracy=0.988812  
INFO:root:Epoch\[8\] Batch \[200\] Speed: 654.07 samples/sec accuracy=0.990800  
INFO:root:Epoch\[8\] Batch \[300\] Speed: 656.73 samples/sec accuracy=0.990700  
INFO:root:Epoch\[8\] Batch \[400\] Speed: 653.70 samples/sec accuracy=0.990900  
INFO:root:Epoch\[8\] Batch \[500\] Speed: 631.36 samples/sec accuracy=0.990200  
INFO:root:Epoch\[8\] Train-accuracy=0.991616  
INFO:root:Epoch\[8\] Time cost=92.349  
INFO:root:Epoch\[8\] Validation-accuracy=0.988500  
INFO:root:Epoch\[9\] Batch \[100\] Speed: 647.88 samples/sec accuracy=0.990792  
INFO:root:Epoch\[9\] Batch \[200\] Speed: 635.89 samples/sec accuracy=0.991900  
INFO:root:Epoch\[9\] Batch \[300\] Speed: 637.18 samples/sec accuracy=0.991700  
INFO:root:Epoch\[9\] Batch \[400\] Speed: 640.23 samples/sec accuracy=0.992300  
INFO:root:Epoch\[9\] Batch \[500\] Speed: 640.93 samples/sec accuracy=0.991900  
INFO:root:Epoch\[9\] Train-accuracy=0.992828  
INFO:root:Epoch\[9\] Time cost=93.533  
INFO:root:Epoch\[9\] Validation-accuracy=0.988700