diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
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+++ b/examples/graph_alexnet.cpp
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+/*
+ * Copyright (c) 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */
+#error "This example needs to be built with -DARM_COMPUTE_CL"
+#endif /* ARM_COMPUTE_CL */
+
+#include "arm_compute/graph/Graph.h"
+#include "arm_compute/graph/Nodes.h"
+#include "arm_compute/runtime/CL/CLScheduler.h"
+#include "arm_compute/runtime/CPP/CPPScheduler.h"
+#include "arm_compute/runtime/Scheduler.h"
+#include "support/ToolchainSupport.h"
+#include "utils/GraphUtils.h"
+#include "utils/Utils.h"
+
+#include <cstdlib>
+#include <iostream>
+#include <memory>
+
+using namespace arm_compute::graph;
+using namespace arm_compute::graph_utils;
+
+/** Generates appropriate accessor according to the specified path
+ *
+ * @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader
+ *
+ * @param[in] path      Path to the data files
+ * @param[in] data_file Relative path to the data files from path
+ *
+ * @return An appropriate tensor accessor
+ */
+std::unique_ptr<ITensorAccessor> get_accessor(const std::string &path, const std::string &data_file)
+{
+    if(path.empty())
+    {
+        return arm_compute::support::cpp14::make_unique<DummyAccessor>();
+    }
+    else
+    {
+        return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(path + data_file);
+    }
+}
+
+/** Example demonstrating how to implement AlexNet's network using the Compute Library's graph API
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches )
+ */
+void main_graph_alexnet(int argc, const char **argv)
+{
+    std::string  data_path;   /** Path to the trainable data */
+    unsigned int batches = 4; /** Number of batches */
+
+    // Parse arguments
+    if(argc < 2)
+    {
+        // Print help
+        std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
+        std::cout << "No data folder provided: using random values\n\n";
+    }
+    else if(argc == 2)
+    {
+        //Do something with argv[1]
+        data_path = argv[1];
+        std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n";
+        std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n";
+    }
+    else
+    {
+        //Do something with argv[1] and argv[2]
+        data_path = argv[1];
+        batches   = std::strtol(argv[2], nullptr, 0);
+    }
+
+    // Check if OpenCL is available and initialize the scheduler
+    Hint hint = Hint::NEON;
+    if(arm_compute::opencl_is_available())
+    {
+        arm_compute::CLScheduler::get().default_init();
+        hint = Hint::OPENCL;
+    }
+
+    Graph graph;
+    graph.set_info_enablement(true);
+
+    graph << hint
+          << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, batches), 1, DataType::F32), DummyAccessor())
+          // Layer 1
+          << ConvolutionLayer(
+              11U, 11U, 96U,
+              get_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"),
+              get_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
+              PadStrideInfo(4, 4, 0, 0))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+          // Layer 2
+          << ConvolutionLayer(
+              5U, 5U, 256U,
+              get_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"),
+              get_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
+              PadStrideInfo(1, 1, 2, 2), 2)
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))
+          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+          // Layer 3
+          << ConvolutionLayer(
+              3U, 3U, 384U,
+              get_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"),
+              get_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
+              PadStrideInfo(1, 1, 1, 1))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          // Layer 4
+          << ConvolutionLayer(
+              3U, 3U, 384U,
+              get_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"),
+              get_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
+              PadStrideInfo(1, 1, 1, 1), 2)
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          // Layer 5
+          << ConvolutionLayer(
+              3U, 3U, 256U,
+              get_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"),
+              get_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
+              PadStrideInfo(1, 1, 1, 1), 2)
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)))
+          // Layer 6
+          << FullyConnectedLayer(
+              4096U,
+              get_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"),
+              get_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          // Layer 7
+          << FullyConnectedLayer(
+              4096U,
+              get_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"),
+              get_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
+          << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+          // Layer 8
+          << FullyConnectedLayer(
+              1000U,
+              get_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"),
+              get_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
+          // Softmax
+          << SoftmaxLayer()
+          << Tensor(DummyAccessor());
+
+    // Run graph
+    graph.run();
+}
+
+/** Main program for AlexNet
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches )
+ */
+int main(int argc, const char **argv)
+{
+    return arm_compute::utils::run_example(argc, argv, main_graph_alexnet);
+}