mxnet手寫數字識別（2）

還可以寫得簡便一些的，是這個版本

import os,  sys
from utils import get_data

import mxnet as mx
import numpy as np
import logging
# 創建計算圖
data = mx.symbol.Variable('data')
fc1 = mx.symbol.FullyConnected(data, name='fc1', num_hidden=128)
act1 = mx.symbol.Activation(fc1, name='relu1', act_type="relu")
fc2 = mx.symbol.FullyConnected(act1, name = 'fc2', num_hidden = 64)
act2 = mx.symbol.Activation(fc2, name='relu2', act_type="relu")
fc3 = mx.symbol.FullyConnected(act2, name='fc3', num_hidden=10)
# print(fc3)  這時候只是一個符號
softmax = mx.symbol.SoftmaxOutput(fc3, name = 'softmax')

n_epoch = 2
batch_size = 100
# 加載數據
basedir = os.path.dirname(__file__)
get_data.get_mnist(os.path.join(basedir, "data"))

train_dataiter = mx.io.MNISTIter(
        image=os.path.join(basedir, "data", "train-images-idx3-ubyte"),
        label=os.path.join(basedir, "data", "train-labels-idx1-ubyte"),
        data_shape=(784,),
        batch_size=batch_size, shuffle=True, flat=True, silent=False, seed=10)

val_dataiter = mx.io.MNISTIter(
        image=os.path.join(basedir, "data", "t10k-images-idx3-ubyte"),
        label=os.path.join(basedir, "data", "t10k-labels-idx1-ubyte"),
        data_shape=(784,),
        batch_size=batch_size, shuffle=True, flat=True, silent=False)

metric = mx.metric.create('acc')

mod = mx.mod.Module(softmax)

mod.fit(train_dataiter, eval_data=val_dataiter,
        optimizer_params={'learning_rate':0.01, 'momentum': 0.9}, num_epoch=n_epoch)