i have written a simple program to test mxnet which i done the same for others
like tf and torch.
net = nn.Sequential()
with net.name_scope():
net.add(
nn.Dense(100,activation='relu'),
nn.Dense(10,activation='relu'),
nn.Dense(10)
)
net.initialize(init=init.Xavier())
softmax_crossentropy=gluon.loss.SoftmaxCrossEntropyLoss(sparse_label=False)
trainer = gluon.Trainer(
net.collect_params(),'sgd',{'learning_rate': 0.1}
)
def acc(output, label):
# output: (batch, num_output) float32 ndarray
# label: (batch, ) int32 ndarray
return (output.argmax(0)== label.astype('float32')).mean().asscalar()
for epoch in range(100):
train_loss,train_acc,valid_acc = 0.0,0.0,0.0
tic=time.time()
for data,label in training:
with autograd.record():
output = net(data)
loss = softmax_crossentropy(output,label)
loss.backward()
trainer.step(batch_size)
train_loss += loss.mean().asscalar()
train_acc += acc(output, label)
for data, label in testing:
valid_acc += acc(net(data), label)
print("Epoch %d: loss %.3f, train acc %.3f, test acc %.3f, in %.1f sec"
% (
epoch, train_loss/len(train_data), train_acc/len(train_data),
valid_acc/len(testing), time.time()-tic))
my problem is that loss get lower and lower and accuracy never goes up and also
seems the network doesn't get better over time , just repeating without any
optimization.
i`m very new to mxnet and most likely i made a big mistake somewhere
data is very simple , and one hot encoded labels.
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