diff --git a/tests/networks_new/AlexNetNetwork.h b/tests/networks_new/AlexNetNetwork.h
new file mode 100644
index 0000000..27b9ef4
--- /dev/null
+++ b/tests/networks_new/AlexNetNetwork.h
@@ -0,0 +1,536 @@
+/*
+ * 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_TEST_MODEL_OBJECTS_ALEXNET_H__
+#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__
+
+#include "Globals.h"
+#include "TensorLibrary.h"
+#include "Utils.h"
+
+#include <memory>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace networks
+{
+/** AlexNet model object */
+template <typename ITensorType,
+          typename TensorType,
+          typename SubTensorType,
+          typename Accessor,
+          typename ActivationLayerFunction,
+          typename ConvolutionLayerFunction,
+          typename FullyConnectedLayerFunction,
+          typename NormalizationLayerFunction,
+          typename PoolingLayerFunction,
+          typename SoftmaxLayerFunction>
+class AlexNetNetwork
+{
+public:
+    void init(DataType data_type, int fixed_point_position, int batches, bool reshaped_weights = false)
+    {
+        _data_type            = data_type;
+        _fixed_point_position = fixed_point_position;
+        _batches              = batches;
+        _reshaped_weights     = reshaped_weights;
+
+        // Initialize weights and biases
+        if(!_reshaped_weights)
+        {
+            init_weights();
+        }
+        else
+        {
+            init_reshaped_weights();
+        }
+    }
+
+    void build()
+    {
+        input.allocator()->init(TensorInfo(TensorShape(227U, 227U, 3U, _batches), 1, _data_type, _fixed_point_position));
+        output.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type, _fixed_point_position));
+
+        // Initialize intermediate tensors
+        // Layer 1
+        conv1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position));
+        act1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position));
+        norm1_out.allocator()->init(TensorInfo(TensorShape(55U, 55U, 96U, _batches), 1, _data_type, _fixed_point_position));
+        pool1_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 96U, _batches), 1, _data_type, _fixed_point_position));
+        pool11_out = std::unique_ptr<SubTensorType>(new SubTensorType(&pool1_out, TensorShape(27U, 27U, 48U, _batches), Coordinates()));
+        pool12_out = std::unique_ptr<SubTensorType>(new SubTensorType(&pool1_out, TensorShape(27U, 27U, 48U, _batches), Coordinates(0, 0, 48)));
+        // Layer 2
+        conv2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type, _fixed_point_position));
+        conv21_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv2_out, TensorShape(27U, 27U, 128U, _batches), Coordinates()));
+        conv22_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv2_out, TensorShape(27U, 27U, 128U, _batches), Coordinates(0, 0, 128)));
+        act2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type, _fixed_point_position));
+        norm2_out.allocator()->init(TensorInfo(TensorShape(27U, 27U, 256U, _batches), 1, _data_type, _fixed_point_position));
+        pool2_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type, _fixed_point_position));
+        // Layer 3
+        conv3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position));
+        act3_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position));
+        act31_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act3_out, TensorShape(13U, 13U, 192U, _batches), Coordinates()));
+        act32_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act3_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192)));
+        // Layer 4
+        conv4_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position));
+        conv41_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates()));
+        conv42_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192)));
+        act4_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 384U, _batches), 1, _data_type, _fixed_point_position));
+        act41_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates()));
+        act42_out = std::unique_ptr<SubTensorType>(new SubTensorType(&act4_out, TensorShape(13U, 13U, 192U, _batches), Coordinates(0, 0, 192)));
+        // Layer 5
+        conv5_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type, _fixed_point_position));
+        conv51_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv5_out, TensorShape(13U, 13U, 128U, _batches), Coordinates()));
+        conv52_out = std::unique_ptr<SubTensorType>(new SubTensorType(&conv5_out, TensorShape(13U, 13U, 128U, _batches), Coordinates(0, 0, 128)));
+        act5_out.allocator()->init(TensorInfo(TensorShape(13U, 13U, 256U, _batches), 1, _data_type, _fixed_point_position));
+        pool5_out.allocator()->init(TensorInfo(TensorShape(6U, 6U, 256U, _batches), 1, _data_type, _fixed_point_position));
+        // Layer 6
+        fc6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position));
+        act6_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position));
+        // Layer 7
+        fc7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position));
+        act7_out.allocator()->init(TensorInfo(TensorShape(4096U, _batches), 1, _data_type, _fixed_point_position));
+        // Layer 8
+        fc8_out.allocator()->init(TensorInfo(TensorShape(1000U, _batches), 1, _data_type, _fixed_point_position));
+
+        // Configure Layers
+        // Layer 1
+        TensorType *b0 = _reshaped_weights ? nullptr : &b[0];
+        conv1.configure(&input, &w[0], b0, &conv1_out, PadStrideInfo(4, 4, 0, 0), WeightsInfo(_reshaped_weights, 11U, 11U));
+        act1.configure(&conv1_out, &act1_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        norm1.configure(&act1_out, &norm1_out, NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f));
+        pool1.configure(&norm1_out, &pool1_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)));
+        // Layer 2
+        conv21.configure(pool11_out.get(), w21.get(), b21.get(), conv21_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U, 5U));
+        conv22.configure(pool12_out.get(), w22.get(), b22.get(), conv22_out.get(), PadStrideInfo(1, 1, 2, 2), WeightsInfo(_reshaped_weights, 5U, 5U));
+        act2.configure(&conv2_out, &act2_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        norm2.configure(&act2_out, &norm2_out, NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f));
+        pool2.configure(&norm2_out, &pool2_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)));
+        // Layer 3
+        TensorType *b2 = _reshaped_weights ? nullptr : &b[2];
+        conv3.configure(&pool2_out, &w[2], b2, &conv3_out, PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U));
+        act3.configure(&conv3_out, &act3_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        // Layer 4
+        conv41.configure(act31_out.get(), w41.get(), b41.get(), conv41_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U));
+        conv42.configure(act32_out.get(), w42.get(), b42.get(), conv42_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U));
+        act4.configure(&conv4_out, &act4_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        // Layer 5
+        conv51.configure(act41_out.get(), w51.get(), b51.get(), conv51_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U));
+        conv52.configure(act42_out.get(), w52.get(), b52.get(), conv52_out.get(), PadStrideInfo(1, 1, 1, 1), WeightsInfo(_reshaped_weights, 3U, 3U));
+        act5.configure(&conv5_out, &act5_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        pool5.configure(&act5_out, &pool5_out, PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0)));
+        // Layer 6
+        fc6.configure(&pool5_out, &w[5], &b[5], &fc6_out, true, _reshaped_weights);
+        act6.configure(&fc6_out, &act6_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        // Layer 7
+        fc7.configure(&act6_out, &w[6], &b[6], &fc7_out, true, _reshaped_weights);
+        act7.configure(&fc7_out, &act7_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        // Layer 8
+        fc8.configure(&act7_out, &w[7], &b[7], &fc8_out, true, _reshaped_weights);
+        // Softmax
+        smx.configure(&fc8_out, &output);
+    }
+
+    void allocate()
+    {
+        input.allocator()->allocate();
+        output.allocator()->allocate();
+
+        if(!_reshaped_weights)
+        {
+            for(auto &wi : w)
+            {
+                wi.allocator()->allocate();
+            }
+
+            for(auto &bi : b)
+            {
+                bi.allocator()->allocate();
+            }
+        }
+        else
+        {
+            w[0].allocator()->allocate();
+            w[2].allocator()->allocate();
+            w[5].allocator()->allocate();
+            w[6].allocator()->allocate();
+            w[7].allocator()->allocate();
+
+            b[5].allocator()->allocate();
+            b[6].allocator()->allocate();
+            b[7].allocator()->allocate();
+
+            dynamic_cast<TensorType *>(w21.get())->allocator()->allocate();
+            dynamic_cast<TensorType *>(w22.get())->allocator()->allocate();
+            dynamic_cast<TensorType *>(w41.get())->allocator()->allocate();
+            dynamic_cast<TensorType *>(w42.get())->allocator()->allocate();
+            dynamic_cast<TensorType *>(w51.get())->allocator()->allocate();
+            dynamic_cast<TensorType *>(w52.get())->allocator()->allocate();
+        }
+
+        conv1_out.allocator()->allocate();
+        act1_out.allocator()->allocate();
+        norm1_out.allocator()->allocate();
+        pool1_out.allocator()->allocate();
+        conv2_out.allocator()->allocate();
+        act2_out.allocator()->allocate();
+        norm2_out.allocator()->allocate();
+        pool2_out.allocator()->allocate();
+        conv3_out.allocator()->allocate();
+        act3_out.allocator()->allocate();
+        conv4_out.allocator()->allocate();
+        act4_out.allocator()->allocate();
+        conv5_out.allocator()->allocate();
+        act5_out.allocator()->allocate();
+        pool5_out.allocator()->allocate();
+        fc6_out.allocator()->allocate();
+        act6_out.allocator()->allocate();
+        fc7_out.allocator()->allocate();
+        act7_out.allocator()->allocate();
+        fc8_out.allocator()->allocate();
+    }
+
+    /** Fills the trainable parameters and input with random data. */
+    void fill_random()
+    {
+        library->fill_tensor_uniform(Accessor(input), 0);
+
+        if(!_reshaped_weights)
+        {
+            for(unsigned int i = 0; i < w.size(); ++i)
+            {
+                library->fill_tensor_uniform(Accessor(w[i]), i + 1);
+                library->fill_tensor_uniform(Accessor(b[i]), i + 10);
+            }
+        }
+        else
+        {
+            library->fill_tensor_uniform(Accessor(w[0]), 1);
+            library->fill_tensor_uniform(Accessor(w[2]), 2);
+
+            library->fill_tensor_uniform(Accessor(w[5]), 3);
+            library->fill_tensor_uniform(Accessor(b[5]), 4);
+            library->fill_tensor_uniform(Accessor(w[6]), 5);
+            library->fill_tensor_uniform(Accessor(b[6]), 6);
+            library->fill_tensor_uniform(Accessor(w[7]), 7);
+            library->fill_tensor_uniform(Accessor(b[7]), 8);
+
+            library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w21.get())), 9);
+            library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w22.get())), 10);
+            library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w41.get())), 11);
+            library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w42.get())), 12);
+            library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w51.get())), 13);
+            library->fill_tensor_uniform(Accessor(*dynamic_cast<TensorType *>(w52.get())), 14);
+        }
+    }
+
+#ifdef INTERNAL_ONLY
+    /** Fills the trainable parameters from binary files
+     *
+     * @param weights Files names containing the weights data
+     * @param biases  Files names containing the bias data
+     */
+    void fill(std::vector<std::string> weights, std::vector<std::string> biases)
+    {
+        ARM_COMPUTE_ERROR_ON(weights.size() != w.size());
+        ARM_COMPUTE_ERROR_ON(biases.size() != b.size());
+        ARM_COMPUTE_ERROR_ON(_reshaped_weights);
+
+        for(unsigned int i = 0; i < weights.size(); ++i)
+        {
+            library->fill_layer_data(Accessor(w[i]), weights[i]);
+            library->fill_layer_data(Accessor(b[i]), biases[i]);
+        }
+    }
+
+    /** Feed input to network from file.
+     *
+     * @param name File name of containing the input data.
+     */
+    void feed(std::string name)
+    {
+        library->fill_layer_data(Accessor(input), name);
+    }
+#endif /* INTERNAL_ONLY */
+
+    /** Get the classification results.
+     *
+     * @return Vector containing the classified labels
+     */
+    std::vector<unsigned int> get_classifications()
+    {
+        std::vector<unsigned int> classified_labels;
+        Accessor                  output_accessor(output);
+
+        Window window;
+        window.set(Window::DimX, Window::Dimension(0, 1, 1));
+        for(unsigned int d = 1; d < output_accessor.shape().num_dimensions(); ++d)
+        {
+            window.set(d, Window::Dimension(0, output_accessor.shape()[d], 1));
+        }
+
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            int               max_idx = 0;
+            float             val     = 0;
+            const void *const out_ptr = output_accessor(id);
+            for(unsigned int l = 0; l < output_accessor.shape().x(); ++l)
+            {
+                float curr_val = reinterpret_cast<const float *>(out_ptr)[l];
+                if(curr_val > val)
+                {
+                    max_idx = l;
+                    val     = curr_val;
+                }
+            }
+            classified_labels.push_back(max_idx);
+        });
+        return classified_labels;
+    }
+
+    /** Clear all allocated memory from the tensor objects */
+    void clear()
+    {
+        // Free allocations
+        input.allocator()->free();
+        output.allocator()->free();
+
+        if(!_reshaped_weights)
+        {
+            for(auto &wi : w)
+            {
+                wi.allocator()->free();
+            }
+
+            for(auto &bi : b)
+            {
+                bi.allocator()->free();
+            }
+        }
+        else
+        {
+            w[0].allocator()->free();
+            w[2].allocator()->free();
+            w[5].allocator()->free();
+            w[6].allocator()->free();
+            w[7].allocator()->free();
+
+            b[5].allocator()->free();
+            b[6].allocator()->free();
+            b[7].allocator()->free();
+        }
+
+        w21.reset();
+        w22.reset();
+        b21.reset();
+        b21.reset();
+        w41.reset();
+        w42.reset();
+        b41.reset();
+        b42.reset();
+        w51.reset();
+        w52.reset();
+        b51.reset();
+        b52.reset();
+
+        conv1_out.allocator()->free();
+        act1_out.allocator()->free();
+        norm1_out.allocator()->free();
+        pool1_out.allocator()->free();
+        conv2_out.allocator()->free();
+        act2_out.allocator()->free();
+        norm2_out.allocator()->free();
+        pool2_out.allocator()->free();
+        conv3_out.allocator()->free();
+        act3_out.allocator()->free();
+        conv4_out.allocator()->free();
+        act4_out.allocator()->free();
+        conv5_out.allocator()->free();
+        act5_out.allocator()->free();
+        pool5_out.allocator()->free();
+        fc6_out.allocator()->free();
+        act6_out.allocator()->free();
+        fc7_out.allocator()->free();
+        act7_out.allocator()->free();
+        fc8_out.allocator()->free();
+    }
+
+    /** Runs the model */
+    void run()
+    {
+        // Layer 1
+        conv1.run();
+        act1.run();
+        norm1.run();
+        pool1.run();
+        // Layer 2
+        conv21.run();
+        conv22.run();
+        act2.run();
+        norm2.run();
+        pool2.run();
+        // Layer 3
+        conv3.run();
+        act3.run();
+        // Layer 4
+        conv41.run();
+        conv42.run();
+        act4.run();
+        // Layer 5
+        conv51.run();
+        conv52.run();
+        act5.run();
+        pool5.run();
+        // Layer 6
+        fc6.run();
+        act6.run();
+        // Layer 7
+        fc7.run();
+        act7.run();
+        // Layer 8
+        fc8.run();
+        // Softmax
+        smx.run();
+    }
+
+private:
+    void init_weights()
+    {
+        w[0].allocator()->init(TensorInfo(TensorShape(11U, 11U, 3U, 96U), 1, _data_type, _fixed_point_position));
+        b[0].allocator()->init(TensorInfo(TensorShape(96U), 1, _data_type, _fixed_point_position));
+        w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 48U, 256U), 1, _data_type, _fixed_point_position));
+        b[1].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position));
+        w[2].allocator()->init(TensorInfo(TensorShape(3U, 3U, 256U, 384U), 1, _data_type, _fixed_point_position));
+        b[2].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position));
+        w[3].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 384U), 1, _data_type, _fixed_point_position));
+        b[3].allocator()->init(TensorInfo(TensorShape(384U), 1, _data_type, _fixed_point_position));
+        w[4].allocator()->init(TensorInfo(TensorShape(3U, 3U, 192U, 256U), 1, _data_type, _fixed_point_position));
+        b[4].allocator()->init(TensorInfo(TensorShape(256U), 1, _data_type, _fixed_point_position));
+        w[5].allocator()->init(TensorInfo(TensorShape(9216U, 4096U), 1, _data_type, _fixed_point_position));
+        b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
+        w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type, _fixed_point_position));
+        b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
+        w[7].allocator()->init(TensorInfo(TensorShape(4096U, 1000U), 1, _data_type, _fixed_point_position));
+        b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type, _fixed_point_position));
+
+        w21 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates()));
+        w22 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[1], TensorShape(5U, 5U, 48U, 128U), Coordinates(0, 0, 0, 128)));
+        b21 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates()));
+        b22 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[1], TensorShape(128U), Coordinates(128)));
+
+        w41 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates()));
+        w42 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[3], TensorShape(3U, 3U, 192U, 192U), Coordinates(0, 0, 0, 192)));
+        b41 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates()));
+        b42 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[3], TensorShape(192U), Coordinates(192)));
+
+        w51 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates()));
+        w52 = std::unique_ptr<SubTensorType>(new SubTensorType(&w[4], TensorShape(3U, 3U, 192U, 128U), Coordinates(0, 0, 0, 128)));
+        b51 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates()));
+        b52 = std::unique_ptr<SubTensorType>(new SubTensorType(&b[4], TensorShape(128U), Coordinates(128)));
+    }
+
+    void init_reshaped_weights()
+    {
+        const unsigned int data_type_size = 16 / arm_compute::data_size_from_type(_data_type);
+
+        // Create tensor for the reshaped weights
+        auto w21_tensor = std::unique_ptr<TensorType>(new TensorType());
+        auto w22_tensor = std::unique_ptr<TensorType>(new TensorType());
+        auto w41_tensor = std::unique_ptr<TensorType>(new TensorType());
+        auto w42_tensor = std::unique_ptr<TensorType>(new TensorType());
+        auto w51_tensor = std::unique_ptr<TensorType>(new TensorType());
+        auto w52_tensor = std::unique_ptr<TensorType>(new TensorType());
+
+        w[0].allocator()->init(TensorInfo(TensorShape(366U * data_type_size, 96U / data_type_size), 1, _data_type, _fixed_point_position));
+        w21_tensor->allocator()->init(TensorInfo(TensorShape(1248U * data_type_size, 128U / data_type_size), 1, _data_type, _fixed_point_position));
+        w22_tensor->allocator()->init(TensorInfo(TensorShape(1248U * data_type_size, 128U / data_type_size), 1, _data_type, _fixed_point_position));
+        w[2].allocator()->init(TensorInfo(TensorShape(2560U * data_type_size, 384U / data_type_size), 1, _data_type, _fixed_point_position));
+        w41_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 192U / data_type_size), 1, _data_type, _fixed_point_position));
+        w42_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 192U / data_type_size), 1, _data_type, _fixed_point_position));
+        w51_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 128U / data_type_size), 1, _data_type, _fixed_point_position));
+        w52_tensor->allocator()->init(TensorInfo(TensorShape(1920U * data_type_size, 128U / data_type_size), 1, _data_type, _fixed_point_position));
+
+        w21 = std::move(w21_tensor);
+        w22 = std::move(w22_tensor);
+        w41 = std::move(w41_tensor);
+        w42 = std::move(w42_tensor);
+        w51 = std::move(w51_tensor);
+        w52 = std::move(w52_tensor);
+
+        b[5].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
+        b[6].allocator()->init(TensorInfo(TensorShape(4096U), 1, _data_type, _fixed_point_position));
+        b[7].allocator()->init(TensorInfo(TensorShape(1000U), 1, _data_type, _fixed_point_position));
+
+        if(_batches > 1)
+        {
+            w[5].allocator()->init(TensorInfo(TensorShape(9216U * data_type_size, 4096U / data_type_size), 1, _data_type, _fixed_point_position));
+            w[6].allocator()->init(TensorInfo(TensorShape(4096U * data_type_size, 4096U / data_type_size), 1, _data_type, _fixed_point_position));
+            w[7].allocator()->init(TensorInfo(TensorShape(4096U * data_type_size, 1000U / data_type_size), 1, _data_type, _fixed_point_position));
+        }
+        else
+        {
+            w[5].allocator()->init(TensorInfo(TensorShape(4096U, 9216U), 1, _data_type, _fixed_point_position));
+            w[6].allocator()->init(TensorInfo(TensorShape(4096U, 4096U), 1, _data_type, _fixed_point_position));
+            w[7].allocator()->init(TensorInfo(TensorShape(1000U, 4096U), 1, _data_type, _fixed_point_position));
+        }
+    }
+
+    DataType     _data_type{ DataType::UNKNOWN };
+    int          _fixed_point_position{ 0 };
+    unsigned int _batches{ 0 };
+    bool         _reshaped_weights{ false };
+
+    ActivationLayerFunction     act1{}, act2{}, act3{}, act4{}, act5{}, act6{}, act7{};
+    ConvolutionLayerFunction    conv1{}, conv21{}, conv22{}, conv3{}, conv41{}, conv42{}, conv51{}, conv52{};
+    FullyConnectedLayerFunction fc6{}, fc7{}, fc8{};
+    NormalizationLayerFunction  norm1{}, norm2{};
+    PoolingLayerFunction        pool1{}, pool2{}, pool5{};
+    SoftmaxLayerFunction        smx{};
+
+    TensorType input{}, output{};
+    std::array<TensorType, 8> w{ {} }, b{ {} };
+    std::unique_ptr<ITensorType> w21{ nullptr }, w22{ nullptr }, b21{ nullptr }, b22{ nullptr };
+    std::unique_ptr<ITensorType> w41{ nullptr }, w42{ nullptr }, b41{ nullptr }, b42{ nullptr };
+    std::unique_ptr<ITensorType> w51{ nullptr }, w52{ nullptr }, b51{ nullptr }, b52{ nullptr };
+
+    TensorType conv1_out{}, act1_out{}, norm1_out{}, pool1_out{};
+    TensorType conv2_out{}, act2_out{}, pool2_out{}, norm2_out{};
+    TensorType conv3_out{}, act3_out{};
+    TensorType conv4_out{}, act4_out{};
+    TensorType conv5_out{}, act5_out{}, pool5_out{};
+    TensorType fc6_out{}, act6_out{};
+    TensorType fc7_out{}, act7_out{};
+    TensorType fc8_out{};
+
+    std::unique_ptr<SubTensorType> pool11_out{}, pool12_out{};
+    std::unique_ptr<SubTensorType> conv21_out{}, conv22_out{};
+    std::unique_ptr<SubTensorType> act31_out{}, act32_out{};
+    std::unique_ptr<SubTensorType> conv41_out{}, conv42_out{}, act41_out{}, act42_out{};
+    std::unique_ptr<SubTensorType> conv51_out{}, conv52_out{};
+};
+} // namespace networks
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_ALEXNET_H__
diff --git a/tests/networks_new/LeNet5Network.h b/tests/networks_new/LeNet5Network.h
new file mode 100644
index 0000000..17421d9
--- /dev/null
+++ b/tests/networks_new/LeNet5Network.h
@@ -0,0 +1,255 @@
+/*
+ * 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_TEST_MODEL_OBJECTS_LENET5_H__
+#define __ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__
+
+#include "Globals.h"
+#include "TensorLibrary.h"
+#include "Utils.h"
+
+#include <memory>
+
+using namespace arm_compute;
+using namespace arm_compute::test;
+
+namespace arm_compute
+{
+namespace test
+{
+namespace networks
+{
+/** Lenet5 model object */
+template <typename TensorType,
+          typename Accessor,
+          typename ActivationLayerFunction,
+          typename ConvolutionLayerFunction,
+          typename FullyConnectedLayerFunction,
+          typename PoolingLayerFunction,
+          typename SoftmaxLayerFunction>
+class LeNet5Network
+{
+public:
+    void init(int batches)
+    {
+        _batches = batches;
+
+        // Initialize input, output, weights and biases
+        input.allocator()->init(TensorInfo(TensorShape(28U, 28U, 1U, _batches), 1, DataType::F32));
+        output.allocator()->init(TensorInfo(TensorShape(10U, _batches), 1, DataType::F32));
+        w[0].allocator()->init(TensorInfo(TensorShape(5U, 5U, 1U, 20U), 1, DataType::F32));
+        b[0].allocator()->init(TensorInfo(TensorShape(20U), 1, DataType::F32));
+        w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 20U, 50U), 1, DataType::F32));
+        b[1].allocator()->init(TensorInfo(TensorShape(50U), 1, DataType::F32));
+        w[2].allocator()->init(TensorInfo(TensorShape(800U, 500U), 1, DataType::F32));
+        b[2].allocator()->init(TensorInfo(TensorShape(500U), 1, DataType::F32));
+        w[3].allocator()->init(TensorInfo(TensorShape(500U, 10U), 1, DataType::F32));
+        b[3].allocator()->init(TensorInfo(TensorShape(10U), 1, DataType::F32));
+    }
+
+    /** Build the model. */
+    void build()
+    {
+        // Initialize intermediate tensors
+        // Layer 1
+        conv1_out.allocator()->init(TensorInfo(TensorShape(24U, 24U, 20U, _batches), 1, DataType::F32));
+        pool1_out.allocator()->init(TensorInfo(TensorShape(12U, 12U, 20U, _batches), 1, DataType::F32));
+        // Layer 2
+        conv2_out.allocator()->init(TensorInfo(TensorShape(8U, 8U, 50U, _batches), 1, DataType::F32));
+        pool2_out.allocator()->init(TensorInfo(TensorShape(4U, 4U, 50U, _batches), 1, DataType::F32));
+        // Layer 3
+        fc1_out.allocator()->init(TensorInfo(TensorShape(500U, _batches), 1, DataType::F32));
+        act1_out.allocator()->init(TensorInfo(TensorShape(500U, _batches), 1, DataType::F32));
+        // Layer 6
+        fc2_out.allocator()->init(TensorInfo(TensorShape(10U, _batches), 1, DataType::F32));
+
+        // Configure Layers
+        conv1.configure(&input, &w[0], &b[0], &conv1_out, PadStrideInfo(1, 1, 0, 0));
+        pool1.configure(&conv1_out, &pool1_out, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)));
+        conv2.configure(&pool1_out, &w[1], &b[1], &conv2_out, PadStrideInfo(1, 1, 0, 0));
+        pool2.configure(&conv2_out, &pool2_out, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0)));
+        fc1.configure(&pool2_out, &w[2], &b[2], &fc1_out);
+        act1.configure(&fc1_out, &act1_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+        fc2.configure(&act1_out, &w[3], &b[3], &fc2_out);
+        smx.configure(&fc2_out, &output);
+    }
+
+    void allocate()
+    {
+        // Allocate tensors
+        input.allocator()->allocate();
+        output.allocator()->allocate();
+        for(auto &wi : w)
+        {
+            wi.allocator()->allocate();
+        }
+        for(auto &bi : b)
+        {
+            bi.allocator()->allocate();
+        }
+        conv1_out.allocator()->allocate();
+        pool1_out.allocator()->allocate();
+        conv2_out.allocator()->allocate();
+        pool2_out.allocator()->allocate();
+        fc1_out.allocator()->allocate();
+        act1_out.allocator()->allocate();
+        fc2_out.allocator()->allocate();
+    }
+
+    /** Fills the trainable parameters and input with random data. */
+    void fill_random()
+    {
+        std::uniform_real_distribution<> distribution(-1, 1);
+        library->fill(Accessor(input), distribution, 0);
+        for(unsigned int i = 0; i < w.size(); ++i)
+        {
+            library->fill(Accessor(w[i]), distribution, i + 1);
+            library->fill(Accessor(b[i]), distribution, i + 10);
+        }
+    }
+
+#ifdef INTERNAL_ONLY
+    /** Fills the trainable parameters from binary files
+     *
+     * @param weights Files names containing the weights data
+     * @param biases  Files names containing the bias data
+     */
+    void fill(std::vector<std::string> weights, std::vector<std::string> biases)
+    {
+        ARM_COMPUTE_ERROR_ON(weights.size() != w.size());
+        ARM_COMPUTE_ERROR_ON(biases.size() != b.size());
+
+        for(unsigned int i = 0; i < weights.size(); ++i)
+        {
+            library->fill_layer_data(Accessor(w[i]), weights[i]);
+            library->fill_layer_data(Accessor(b[i]), biases[i]);
+        }
+    }
+
+    /** Feed input to network from file.
+     *
+     * @param name File name of containing the input data.
+     */
+    void feed(std::string name)
+    {
+        library->fill_layer_data(Accessor(input), name);
+    }
+#endif /* INTERNAL_ONLY */
+
+    /** Get the classification results.
+     *
+     * @return Vector containing the classified labels
+     */
+    std::vector<unsigned int> get_classifications()
+    {
+        std::vector<unsigned int> classified_labels;
+        Accessor                  output_accessor(output);
+
+        Window window;
+        window.set(Window::DimX, Window::Dimension(0, 1, 1));
+        for(unsigned int d = 1; d < output_accessor.shape().num_dimensions(); ++d)
+        {
+            window.set(d, Window::Dimension(0, output_accessor.shape()[d], 1));
+        }
+
+        execute_window_loop(window, [&](const Coordinates & id)
+        {
+            int               max_idx = 0;
+            float             val     = 0;
+            const void *const out_ptr = output_accessor(id);
+            for(unsigned int l = 0; l < output_accessor.shape().x(); ++l)
+            {
+                float curr_val = reinterpret_cast<const float *>(out_ptr)[l];
+                if(curr_val > val)
+                {
+                    max_idx = l;
+                    val     = curr_val;
+                }
+            }
+            classified_labels.push_back(max_idx);
+        });
+        return classified_labels;
+    }
+
+    /** Clear all allocated memory from the tensor objects */
+    void clear()
+    {
+        input.allocator()->free();
+        output.allocator()->free();
+        for(auto &wi : w)
+        {
+            wi.allocator()->free();
+        }
+        for(auto &bi : b)
+        {
+            bi.allocator()->free();
+        }
+
+        conv1_out.allocator()->free();
+        pool1_out.allocator()->free();
+        conv2_out.allocator()->free();
+        pool2_out.allocator()->free();
+        fc1_out.allocator()->free();
+        act1_out.allocator()->free();
+        fc2_out.allocator()->free();
+    }
+
+    /** Runs the model */
+    void run()
+    {
+        // Layer 1
+        conv1.run();
+        pool1.run();
+        // Layer 2
+        conv2.run();
+        pool2.run();
+        // Layer 3
+        fc1.run();
+        act1.run();
+        // Layer 4
+        fc2.run();
+        // Softmax
+        smx.run();
+    }
+
+private:
+    unsigned int _batches{ 0 };
+
+    ActivationLayerFunction     act1{};
+    ConvolutionLayerFunction    conv1{}, conv2{};
+    FullyConnectedLayerFunction fc1{}, fc2{};
+    PoolingLayerFunction        pool1{}, pool2{};
+    SoftmaxLayerFunction        smx{};
+
+    TensorType input{}, output{};
+    std::array<TensorType, 4> w{ {} }, b{ {} };
+
+    TensorType conv1_out{}, pool1_out{};
+    TensorType conv2_out{}, pool2_out{};
+    TensorType fc1_out{}, act1_out{};
+    TensorType fc2_out{};
+};
+} // namespace networks
+} // namespace test
+} // namespace arm_compute
+#endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__