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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: skip-file
import mxnet as mx
from mxnet.test_utils import get_mnist_iterator
import numpy as np
import logging
class Softmax(mx.operator.CustomOp):
def forward(self, is_train, req, in_data, out_data, aux):
x = in_data[0].asnumpy()
y = np.exp(x - x.max(axis=1).reshape((x.shape[0], 1)))
y /= y.sum(axis=1).reshape((x.shape[0], 1))
self.assign(out_data[0], req[0], mx.nd.array(y))
def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
l = in_data[1].asnumpy().ravel().astype(np.int)
y = out_data[0].asnumpy()
y[np.arange(l.shape[0]), l] -= 1.0
self.assign(in_grad[0], req[0], mx.nd.array(y))
@mx.operator.register("softmax")
class SoftmaxProp(mx.operator.CustomOpProp):
def __init__(self):
super(SoftmaxProp, self).__init__(need_top_grad=False)
def list_arguments(self):
return ['data', 'label']
def list_outputs(self):
return ['output']
def infer_shape(self, in_shape):
data_shape = in_shape[0]
label_shape = (in_shape[0][0],)
output_shape = in_shape[0]
return [data_shape, label_shape], [output_shape], []
def infer_type(self, in_type):
return in_type, [in_type[0]], []
def create_operator(self, ctx, shapes, dtypes):
return Softmax()
# define mlp
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.Softmax(data = fc3, name = 'softmax')
mlp = mx.symbol.Custom(data=fc3, name='softmax', op_type='softmax')
# data
train, val = get_mnist_iterator(batch_size=100, input_shape = (784,))
# train
logging.basicConfig(level=logging.DEBUG)
# MXNET_CPU_WORKER_NTHREADS must be greater than 1 for custom op to work on CPU
context=mx.cpu()
# Uncomment this line to train on GPU
# context=mx.gpu(0)
mod = mx.mod.Module(mlp, context=context)
mod.fit(train_data=train, eval_data=val, optimizer='sgd',
optimizer_params={'learning_rate':0.1, 'momentum': 0.9, 'wd': 0.00001},
num_epoch=10, batch_end_callback=mx.callback.Speedometer(100, 100))