I am trying to follow this example http://gluon.mxnet.io/chapter02_supervised-learning/logistic-regression-gluon.html I am using titanic dataset and have converted categorical fields to numerics. The code is below:

```
from __future__ import print_function
import mxnet as mx
from mxnet import nd, autograd, gluon
import matplotlib.pyplot as plt
import logging
mx.random.seed(1)
data_ctx = mx.cpu()
model_ctx = mx.cpu()
def logistic(z):
return 1. / (1. + nd.exp(-z))
def log_loss(output, y):
yhat = logistic(output)
return - nd.nansum( y * nd.log(yhat) + (1-y) * nd.log(1-yhat))
batch_size = 10
num_fea = 5
train_data = mx.io.CSVIter(data_csv="./titanic_train.csv", data_shape=(num_fea,),
label_csv="./titanic_train_lb.csv", label_shape=(1,),
batch_size=batch_size, round_batch = False)
net = gluon.nn.Dense(1)
net.collect_params().initialize(mx.init.Normal(sigma=0.01), ctx=model_ctx)
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.01})
epochs = 30
loss_sequence = []
for e in range(epochs):
cumulative_loss = 0
for i, batch in enumerate(train_data):
data = batch.data[0].as_in_context(model_ctx)
label = batch.label[0].as_in_context(model_ctx)
with autograd.record():
output = net(data)
loss = log_loss(output, label)
loss.backward()
trainer.step(batch_size)
cumulative_loss += nd.sum(loss).asscalar()
print("Epoch %s, loss: %s" % (e, cumulative_loss ))
loss_sequence.append(cumulative_loss)
```

The output doesn’t show training happens:

```
Epoch 0, loss: 6015.84739304
Epoch 1, loss: 0
Epoch 2, loss: 0
Epoch 3, loss: 0
Epoch 4, loss: 0
Epoch 5, loss: 0
Epoch 6, loss: 0
Epoch 7, loss: 0
Epoch 8, loss: 0
Epoch 9, loss: 0
Epoch 10, loss: 0
Epoch 11, loss: 0
```

My data sample:

titanic_train.csv: (age column normalized to (0, 1] )

3,1,0.275,1,0

1,0,0.475,1,0

3,0,0.325,0,0

1,0,0.4375,1,0

3,1,0.4375,0,0

3,1,0.35,0,0

1,1,0.675,0,0

3,1,0.025,3,1

3,0,0.3375,0,2

2,0,0.175,1,0

```
titanic_train_lb.csv:
0
1
1
1
0
0
0
0
1
1
1
1
0
0
```

What am I doing wrong? Bug is opened: