diff --git a/example/sparse/wide_deep/README.md b/example/sparse/wide_deep/README.md index 3df5e420ee36..8b987c8fa95c 100644 --- a/example/sparse/wide_deep/README.md +++ b/example/sparse/wide_deep/README.md @@ -3,5 +3,8 @@ The example demonstrates how to train [wide and deep model](https://arxiv.org/abs/1606.07792). The [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) that this example uses for training is hosted by the [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/). Tricks of feature engineering are adapted from tensorflow's [wide and deep tutorial](https://github.com/tensorflow/models/tree/master/official/wide_deep). The final accuracy should be around 85%. - +For training: - `python train.py` + +For inference: +- `python inference.py` diff --git a/example/sparse/wide_deep/config.py b/example/sparse/wide_deep/config.py new file mode 100644 index 000000000000..c0d20c49f981 --- /dev/null +++ b/example/sparse/wide_deep/config.py @@ -0,0 +1,28 @@ +# 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. + +# Related to feature engineering, please see preprocess in data.py +ADULT = { + 'train': 'adult.data', + 'test': 'adult.test', + 'url': 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/', + 'num_linear_features': 3000, + 'num_embed_features': 2, + 'num_cont_features': 38, + 'embed_input_dims': [1000, 1000], + 'hidden_units': [8, 50, 100], +} diff --git a/example/sparse/wide_deep/inference.py b/example/sparse/wide_deep/inference.py new file mode 100644 index 000000000000..e14396e50c15 --- /dev/null +++ b/example/sparse/wide_deep/inference.py @@ -0,0 +1,106 @@ +# 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. + +import mxnet as mx +from mxnet.test_utils import * +from config import * +from data import get_uci_adult +from model import wide_deep_model +import argparse +import os +import time + +parser = argparse.ArgumentParser(description="Run sparse wide and deep inference", + formatter_class=argparse.ArgumentDefaultsHelpFormatter) +parser.add_argument('--num-infer-batch', type=int, default=100, + help='number of batches to inference') +parser.add_argument('--load-epoch', type=int, default=0, + help='loading the params of the corresponding training epoch.') +parser.add_argument('--batch-size', type=int, default=100, + help='number of examples per batch') +parser.add_argument('--benchmark', action='store_true', default=False, + help='run the script for benchmark mode, not set for accuracy test.') +parser.add_argument('--verbose', action='store_true', default=False, + help='accurcy for each batch will be logged if set') +parser.add_argument('--gpu', action='store_true', default=False, + help='Inference on GPU with CUDA') +parser.add_argument('--model-prefix', type=str, default='checkpoint', + help='the model prefix') + +if __name__ == '__main__': + import logging + head = '%(asctime)-15s %(message)s' + logging.basicConfig(level=logging.INFO, format=head) + + # arg parser + args = parser.parse_args() + logging.info(args) + num_iters = args.num_infer_batch + batch_size = args.batch_size + benchmark = args.benchmark + verbose = args.verbose + model_prefix = args.model_prefix + load_epoch = args.load_epoch + ctx = mx.gpu(0) if args.gpu else mx.cpu() + # dataset + data_dir = os.path.join(os.getcwd(), 'data') + val_data = os.path.join(data_dir, ADULT['test']) + val_csr, val_dns, val_label = get_uci_adult(data_dir, ADULT['test'], ADULT['url']) + # load parameters and symbol + sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, load_epoch) + # data iterator + eval_data = mx.io.NDArrayIter({'csr_data': val_csr, 'dns_data': val_dns}, + {'softmax_label': val_label}, batch_size, + shuffle=True, last_batch_handle='discard') + # module + mod = mx.mod.Module(symbol=sym, context=ctx, data_names=['csr_data', 'dns_data'], + label_names=['softmax_label']) + mod.bind(data_shapes=eval_data.provide_data, label_shapes=eval_data.provide_label) + # get the sparse weight parameter + mod.set_params(arg_params=arg_params, aux_params=aux_params) + + data_iter = iter(eval_data) + nbatch = 0 + if benchmark: + logging.info('Inference benchmark started ...') + tic = time.time() + for i in range(num_iters): + try: + batch = data_iter.next() + except StopIteration: + data_iter.reset() + else: + mod.forward(batch, is_train=False) + for output in mod.get_outputs(): + output.wait_to_read() + nbatch += 1 + score = (nbatch*batch_size)/(time.time() - tic) + logging.info('batch size %d, process %s samples/s' % (batch_size, score)) + else: + logging.info('Inference started ...') + # use accuracy as the metric + metric = mx.metric.create(['acc']) + accuracy_avg = 0.0 + for batch in data_iter: + nbatch += 1 + metric.reset() + mod.forward(batch, is_train=False) + mod.update_metric(metric, batch.label) + accuracy_avg += metric.get()[1][0] + if args.verbose: + logging.info('batch %d, accuracy = %s' % (nbatch, metric.get())) + logging.info('averged accuracy on eval set is %.5f' % (accuracy_avg/nbatch)) diff --git a/example/sparse/wide_deep/train.py b/example/sparse/wide_deep/train.py index 6fd81b7fa480..eea70301660d 100644 --- a/example/sparse/wide_deep/train.py +++ b/example/sparse/wide_deep/train.py @@ -17,6 +17,7 @@ import mxnet as mx from mxnet.test_utils import * +from config import * from data import get_uci_adult from model import wide_deep_model import argparse @@ -31,7 +32,7 @@ help='number of examples per batch') parser.add_argument('--lr', type=float, default=0.001, help='learning rate') -parser.add_argument('--cuda', action='store_true', default=False, +parser.add_argument('--gpu', action='store_true', default=False, help='Train on GPU with CUDA') parser.add_argument('--optimizer', type=str, default='adam', help='what optimizer to use', @@ -40,19 +41,6 @@ help='number of batches to wait before logging training status') -# Related to feature engineering, please see preprocess in data.py -ADULT = { - 'train': 'adult.data', - 'test': 'adult.test', - 'url': 'https://archive.ics.uci.edu/ml/machine-learning-databases/adult/', - 'num_linear_features': 3000, - 'num_embed_features': 2, - 'num_cont_features': 38, - 'embed_input_dims': [1000, 1000], - 'hidden_units': [8, 50, 100], -} - - if __name__ == '__main__': import logging head = '%(asctime)-15s %(message)s' @@ -66,7 +54,7 @@ optimizer = args.optimizer log_interval = args.log_interval lr = args.lr - ctx = mx.gpu(0) if args.cuda else mx.cpu() + ctx = mx.gpu(0) if args.gpu else mx.cpu() # dataset data_dir = os.path.join(os.getcwd(), 'data') @@ -88,7 +76,7 @@ shuffle=True, last_batch_handle='discard') # module - mod = mx.mod.Module(symbol=model, context=ctx ,data_names=['csr_data', 'dns_data'], + mod = mx.mod.Module(symbol=model, context=ctx, data_names=['csr_data', 'dns_data'], label_names=['softmax_label']) mod.bind(data_shapes=train_data.provide_data, label_shapes=train_data.provide_label) mod.init_params()