我在Spyder IDE中运行mxnet的mnist示例。培训没有进展，就像学习率是0一样（见下面的输出）。
如果我使用python或ipython在控制台上运行相同的文件，它会按预期工作（在第二个纪元中看到改进）。
如果我使用Spyder的普通Python控制台运行该文件，它也可以。
但是如果我使用ipython控制台运行该文件，我会得到如下所示的输出。
我是Python的新手。有什么想法吗？
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
#%%
import mxnet as mx
#%% load mnist
mnist = mx.test_utils.get_mnist()
#%% define data iterators
batch_size = 100
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)
#%% create input variable
data = mx.sym.var('data')
# Flatten the data from 4-D shape into 2-D (batch_size, num_channel*width*height)
data = mx.sym.flatten(data=data)
#%% Multilayer Perceptron with softmax
# The first fully-connected layer and the corresponding activation function
fc1 = mx.sym.FullyConnected(data=data, num_hidden=128)
act1 = mx.sym.Activation(data=fc1, act_type="relu")
# The second fully-connected layer and the corresponding activation function
fc2 = mx.sym.FullyConnected(data=act1, num_hidden = 64)
act2 = mx.sym.Activation(data=fc2, act_type="relu")
# MNIST has 10 classes
fc3 = mx.sym.FullyConnected(data=act2, num_hidden=10)
# Softmax with cross entropy loss
mlp = mx.sym.SoftmaxOutput(data=fc3, name='softmax')
#%% Training
import logging
logging.getLogger().setLevel(logging.DEBUG) # logging to stdout
# create a trainable module on CPU
mlp_model = mx.mod.Module(symbol=mlp, context=mx.cpu())
mlp_model.fit(train_iter, # train data
eval_data=val_iter, # validation data
optimizer='sgd', # use SGD to train
optimizer_params={'learning_rate':0.1}, # use fixed learning rate
eval_metric='acc', # report accuracy during training
batch_end_callback = mx.callback.Speedometer(batch_size, 100), # output progress for each 100 data batches
num_epoch=10) # train for at most 10 dataset passes
ipython控制台的输出runfile('/home/thomas/workspace/clover-python/mnist.py', wdir='/home/thomas/workspace/clover-python')
INFO:root:train-labels-idx1-ubyte.gz exists, skipping download
INFO:root:train-images-idx3-ubyte.gz exists, skipping download
INFO:root:t10k-labels-idx1-ubyte.gz exists, skipping download
INFO:root:t10k-images-idx3-ubyte.gz exists, skipping download
INFO:root:Epoch[0] Batch [100] Speed: 1304.05 samples/sec accuracy=0.097921
INFO:root:Epoch[0] Batch [200] Speed: 1059.19 samples/sec accuracy=0.098200
INFO:root:Epoch[0] Batch [300] Speed: 1178.64 samples/sec accuracy=0.099600
INFO:root:Epoch[0] Batch [400] Speed: 1292.71 samples/sec accuracy=0.098900
INFO:root:Epoch[0] Batch [500] Speed: 1394.21 samples/sec accuracy=0.096500
INFO:root:Epoch[0] Train-accuracy=0.101212
INFO:root:Epoch[0] Time cost=47.798
INFO:root:Epoch[0] Validation-accuracy=0.098000
INFO:root:Epoch[1] Batch [100] Speed: 1247.47 samples/sec accuracy=0.097921
INFO:root:Epoch[1] Batch [200] Speed: 1673.79 samples/sec accuracy=0.098200
INFO:root:Epoch[1] Batch [300] Speed: 1283.91 samples/sec accuracy=0.099600
INFO:root:Epoch[1] Batch [400] Speed: 1247.79 samples/sec accuracy=0.098900
INFO:root:Epoch[1] Batch [500] Speed: 1371.93 samples/sec accuracy=0.096500
INFO:root:Epoch[1] Train-accuracy=0.101212
INFO:root:Epoch[1] Time cost=44.201
INFO:root:Epoch[1] Validation-accuracy=0.098000
INFO:root:Epoch[2] Batch [100] Speed: 1387.72 samples/sec accuracy=0.097921
INFO:root:Epoch[2] Batch [200] Speed: 1196.37 samples/sec accuracy=0.098200
INFO:root:Epoch[2] Batch [300] Speed: 1220.44 samples/sec accuracy=0.099600
INFO:root:Epoch[2] Batch [400] Speed: 1387.75 samples/sec accuracy=0.098900
INFO:root:Epoch[2] Batch [500] Speed: 1279.58 samples/sec accuracy=0.096500
INFO:root:Epoch[2] Train-accuracy=0.101212
INFO:root:Epoch[2] Time cost=46.929
INFO:root:Epoch[2] Validation-accuracy=0.098000
INFO:root:Epoch[3] Batch [100] Speed: 1266.24 samples/sec accuracy=0.097921
and so on…
答案 0 :(得分：1)
根据Eric的评论，让我们验证您是否在每个环境中运行相同版本的mxnet。您可以输入
import mxnet
print(mxnet.__version__)
答案 1 :(得分：0)
您是否安装了多个版本的MXNet？我能想到的一件事是验证struct PostModelSpeedRunModel {
var id = ""
var international = ""
var abbreviation = ""
var runsLink = ""
var uri = ""
}
extension PostModelSpeedRunModel: Mappable {
init?(map: Map) {
}
mutating func mapping(map: Map) {
id <- map["id"]
international <- map["international"]
abbreviation <- map["abbreviation"]
uri <- map["logo"]
var links: [Link]?
links <- map["links"]
if let uri = links?.first(where: {$0.rel == "runs"})?.uri {
runsLink = uri
}
}
}
struct Link {
var rel = ""
var uri = ""
}
extension Link: Mappable {
init?(map: Map) {
}
mutating func mapping(map: Map) {
rel <- map["rel"]
uri <- map["uri"]
}
}
控制台和ipython控制台是否使用相同版本的python和MXNet。在ipython中，您可以键入python以查看您正在使用的MXNet版本。