vandanavk commented on a change in pull request #12376: [MXNET-854] SVRG Optimization in Python Module API URL: https://github.com/apache/incubator-mxnet/pull/12376#discussion_r213765297
########## File path: example/svrg_module/example_api_train.py ########## @@ -0,0 +1,86 @@ +# 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 +import numpy as np +from mxnet.contrib.svrg_optimization.svrg_module import SVRGModule + + +def test_svrg_intermediate_level_api(num_epoch): + """Test intermediate level svrgmodule API where the training process + need to be explicitly defined. KVstore is not explicitly created. + """ + di, mod = create_network() + mod.bind(data_shapes=di.provide_data, label_shapes=di.provide_label) + mod.init_params(initializer=mx.init.Uniform(0.01), allow_missing=False, force_init=False, allow_extra=False) + kv = mx.kv.create("local") + mod.init_optimizer(kvstore=kv, optimizer='sgd', optimizer_params=(('learning_rate', 0.025),)) + metrics = mx.metric.create("mse") + for e in range(num_epoch): + metrics.reset() + if e % (mod.update_freq) == 0: + mod.update_full_grads(di) + di.reset() + for batch in di: + mod.forward_backward(data_batch=batch) + mod.update() + mod.update_metric(metrics, batch.label) + mod.logger.info('Epoch[%d] Train cost=%f', e, metrics.get()[1]) + + +def test_svrg_high_level_api(num_epoch): + """Test high level svrgmodule API. KVStore is explicitly created. + """ + di, mod = create_network() + mod.fit(di, eval_metric='mse', optimizer='sgd', optimizer_params=(('learning_rate', 0.025),), num_epoch=num_epoch, + kvstore='local') + + +def create_network(): + import logging + """Create a linear regression network for performing SVRG optimization. + :return: an instance of mx.io.NDArrayIter + :return: an instance of mx.mod.svrgmodule for performing SVRG optimization + """ + head = '%(asctime)-15s %(message)s' + logging.basicConfig(level=logging.INFO, format=head) + train_data = np.random.randint(1, 5, [1000, 2]) + weights = np.array([1.0, 2.0]) + train_label = train_data.dot(weights) + + di = mx.io.NDArrayIter(train_data, train_label, batch_size=32, shuffle=True, label_name='lin_reg_label') + X = mx.sym.Variable('data') + Y = mx.symbol.Variable('lin_reg_label') + fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden=1) + lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro") + + mod = SVRGModule( + symbol=lro, + data_names=['data'], + label_names=['lin_reg_label'], update_freq=2, logger=logging + ) + + return di, mod + +# run as a script +if __name__ == "__main__": + num_epoch = 100 Review comment: Can this be a user-defined param? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
