| # pylint: skip-file |
| from data import mnist_iterator |
| import mxnet as mx |
| import numpy as np |
| import logging |
| |
| data = mx.symbol.Variable('data') |
| fc1 = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128) |
| act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type="relu") |
| fc2 = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64) |
| act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type="relu") |
| fc3 = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10) |
| mlp = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax') |
| |
| # data |
| |
| train, val = mnist_iterator(batch_size=100, input_shape = (784,)) |
| |
| # train |
| |
| logging.basicConfig(level=logging.DEBUG) |
| |
| model = mx.model.FeedForward( |
| ctx = mx.cpu(), symbol = mlp, num_epoch = 20, |
| learning_rate = 0.1, momentum = 0.9, wd = 0.00001) |
| |
| def norm_stat(d): |
| return mx.nd.norm(d)/np.sqrt(d.size) |
| mon = mx.mon.Monitor(100, norm_stat) |
| model.fit(X=train, eval_data=val, monitor=mon, |
| batch_end_callback = mx.callback.Speedometer(100, 100)) |
| |
