[GitHub] nswamy commented on a change in pull request #12636: [MXNET-982] Provide example to illustrate usage of CSVIter in C++ API

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nswamy commented on a change in pull request #12636: [MXNET-982] Provide example to illustrate usage of CSVIter in C++ API
URL: https://github.com/apache/incubator-mxnet/pull/12636#discussion_r219775911
 
 

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 File path: cpp-package/example/mlp_csv_cpu.cpp
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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.
+ */
+
+/*!
+ * Amol Lele amol.github@gmail.com
+ */
+
+/*
+ * Example: mlp_csv_cpu
+ * Description:
+ * The following example demonstrates how to use CSVIter. This example creates
+ * mlp (multi-layer perceptron) model and trains the MNIST data which is in
+ * CSV format.
+ */
+#include <chrono>
+#include "utils.h"
+#include "mxnet-cpp/MxNetCpp.h"
+
+using namespace mxnet::cpp;
+
+/*
+ * Implementing the mlp symbol with given layer configuration.
+ */
+Symbol mlp(const std::vector<int> &layers)
+{
+    auto x = Symbol::Variable("X");
+    auto label = Symbol::Variable("label");
+
+    std::vector<Symbol> weights(layers.size());
+    std::vector<Symbol> biases(layers.size());
+    std::vector<Symbol> outputs(layers.size());
+
+    for (size_t i = 0; i < layers.size(); ++i) {
+        weights[i] = Symbol::Variable("w" + std::to_string(i));
+        biases[i] = Symbol::Variable("b" + std::to_string(i));
+        Symbol fc = FullyConnected(
+                                   i == 0? x : outputs[i-1],  // data
+                                   weights[i],
+                                   biases[i],
+                                   layers[i]);
+        outputs[i] = i == layers.size()-1 ? fc : Activation(fc, ActivationActType::kRelu);
+    }
+    return SoftmaxOutput(outputs.back(), label);
+}
+
+
+int main(int argc, char** argv)
+{
+    const int image_size = 28;
+    const int num_mnist_features = image_size * image_size;
+    const std::vector<int> layers{128, 64, 10};
 
 Review comment:
   layers->hidden_layer_size? layers generally is referred to convolution, pooling, dense, etc.,

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