Neural_Turing_Machines_Reports_and_Discussion.ipynb

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        "#  Neural Turing Machines: Reports and Discussions\n",
        "\n",
        "---\n",
        "\n",
        "In this file we will report on the implemention of the Neural Turing Machine (NTM) as described in *[Neural Turing Machines](https://arxiv.org/abs/1410.5401)*. The goal is\n",
        "to better understand attention and memory augmented networks, and the difficulties encountered\n",
        "when training them. This report will be divided into $3$ main parts, as follows:\n",
        "\n",
        "\n",
        "1.   **Filling in the Gaps:** Here we will attempt to cover some missing details of the paper.\n",
        "2.   **Report:** Here we will report on the followings:\n",
        "  *  **(a)** The total number of parameters for each model\n",
        "  *  **(b)** Hyper-parameter values\n",
        "  *  **(c)** Generalization of the models to longer sequences\n",
        "  *  **(d)** Visualizing the read and write heads/attention\n",
        "  *  **(e)** Understanding the shift operator\n",
        "3.   **Discussion:** Here we will discuss the difficulties we have encountered during this task and the improvements that can be done for further work on this topic. We will also provide some analysis of the data we observed.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
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        "## 1. Filling in the Gaps\n",
        "In this section we will attempt to provide details on the followings:\n",
        "\n",
        "\n",
        "1.   **Parameter Constraints:** Describing how we have constrained the output of the controller at each time step to satisfy the constraints on their parameters.\n",
        "2.   **Dependencies:** Describing the dependencies between: $x_t$, $M_{t-1}$, $M_t$, $r_{t-1}$, $r_t$, $e_t$, $a_t$, and $o_t$\n",
        "\n",
        "\n"
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        "### 1.1 Parameter Constraints\n",
        "The output of the controller at each time step consists of parameters that have constraints. For example, $\\beta \\in (0,\\infty)$ Using the following equations, we will describe how to constrain the output to satisfy them.\n",
        "The parameters of the output of the controller are:\n",
        "\n",
        "\n",
        "1.   **$g$:** The constraint on this parameter indicated by the paper is that it has to belong to$(0,1)$. \n",
        "      \n",
        "      To satisfy this we will have $g=\\sigma(\\hat{g})$ the choice that we made here satisfies $g\\in(0,1)$ hence the reason we chose the sigmoid function.\n",
        "      \n",
        "2.   **$\\gamma$:** The paper indicates that this parameter has to be greater than or equal to $1$.\n",
        "\n",
        "      To achieve this condition, we will have $\\gamma$ to be given by $\\gamma=\\mathrm{log}(\\exp(\\hat{\\gamma})+1)+1$, for really small value of $\\hat{\\gamma}$ this will almost be $1$ and for any other value of $\\hat{\\gamma}$, it will always be greater than $1$.\n",
        "\n",
        "3.   **$s$:** The paper indicates that $s$ has to be the normalized distribution over all allowed integer shifts, it then suggest one possible technique would be to use a $\\mathrm{softmax}$ function for $s$, hence that is also what we will consider for this parameter.\n",
        "\n",
        "4.   **$\\beta$:** The paper requires that this parameter to be positive i.e. greater than zero.\n",
        "\n",
        "     To satisfy this condition we let $\\beta$ to be defined as $\\beta=\\exp(\\hat{\\beta})$. This definition insures that $\\beta>0$ as required.\n",
        "     \n",
        "5.   **$k$:** The paper does not require any constraints on this parameter, hence $k\\in \\mathbb{R}^M$ and we can simply let $k=\\hat{k}$ for this purpose. Note that for the task of copying binary values we could refine $k$ to a more specific and bounded version. However we decided to keep our approach and our implementation general.\n",
        "\n"
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        "### 1.2 Dependencies\n",
        "In this section, we will show how we think the followings are dependent on each other.\n",
        "* The input $x_t$\n",
        "* The memory $M_{t-1}$ and $M_t$\n",
        "* The output of the read head $r_{t-1}$ and $r_t$\n",
        "* The erase and add vectors $e_t$ and $a_t$\n",
        "* The output of the controller $o_t$\n",
        "\n",
        "We will do this via the below diagram. Here $x$ represents **input** and $h_t$ is the **head vector** containing $(k_t,\\beta_t,g_t,s_t,\\gamma_t)$. In this diagram if $B$ is dependent on $A$ then there is an arrow from $A$ to $B$. Note that in the paper *[Neural Turing Machines](https://arxiv.org/abs/1410.5401)* the dependencies are described as follows:\n",
        "    $$w_t,M_t\\to r_t \\ (1)$$\n",
        "    $$M_{t-1},w_t,a_t,e_t\\to M_t \\ (2)$$\n",
        "    $$h_t,M_t,w_{t-1}\\to w_t \\ (3)$$\n",
        "But the indices used by the paper are problematic. For instance, relation $(3)$ needs $M_t$ to produce $w_t$ but according to relation $(2)$, $M_t$ needs $w_t$ to be produced. In order to fix these inconsistencies we used the following relations in the diagram:\n",
        "    $$w_{t-1},M_{t-1}\\to r_t \\ (1)$$\n",
        "    $$M_{t-1},w_{t-1},a_t,e_t\\to M_t \\ (2)$$\n",
        "    $$h_t,M_t,w_{t-1}\\to w_t \\ (3)$$\n",
        "\n"
      ]
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      "source": [
        "Image('depGraph_exp.jpg')\n"
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a3BOsLeZS0jC2hGTE0nCN5SSQK6dFbmSakP5x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\nKAUAoBQCgFAKAUAoBQCgFAKAUAoBQCgFAf/Z\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "1lTSmA_NJYky",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 2. Report\n",
        "As mentioned before, in this section we will report on our findings for the followings:"
      ]
    },
    {
      "metadata": {
        "id": "hOQWFXrDKPcL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **(a)** The Total Number of Parameters for Each Model\n",
        "This is a report on the total number of parameters of all the models, including the baseline.<br>\n",
        "We gathered the information presented in this section from the summary command executed in the file titled **Neural Turing Machines: Implementation** sections:\n",
        "\n",
        "\n",
        "*   Running NTM with LSTM\n",
        "*   Running NTM with Feedforward\n",
        "*   Running LSTM\n",
        "\n",
        "The result is as follows:\n",
        "<table style=\"width:100%\" border=\"1px solid black\">\n",
        "  <tr>\n",
        "    <th>Method's Name</th>\n",
        "    <th>Total Number of Parameters</th> \n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>LSTM-NTM</td>\n",
        "    <td>62,201</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>Feedforward-NTM</td>\n",
        "    <td>13,201</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>LSTM</td>\n",
        "    <td>47,960</td>\n",
        "  </tr>\n",
        "</table>\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "ECKqNg1BNwDd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **(b)** Hyper-parameter Values\n",
        "We experimented with a large set of candidates to choose our hyper-parameters from. We found out a few suitable candidates for our tasks which worked well across all our methods. Below we are providing the hyper-parameters we eventually used.\n",
        "<table style=\"width:100%\" border=\"1px solid black\">\n",
        "  <tr>\n",
        "    <th>Method's Name</th>\n",
        "    <th>Learning Rate</th>\n",
        "    <th>Learning Rate Decay</th>\n",
        "    <th>Batch Size</th>\n",
        "    <th>Optimizer</th>\n",
        "    <th>Activation Function</th>\n",
        "    <th>Gradien Clipped at Max Norm of</th>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>LSTM-NTM</td>\n",
        "    <td>0.01</td>\n",
        "    <td>0.5</td>\n",
        "    <td>100</td>\n",
        "    <td>Stochastic Gradient Descent</td>\n",
        "    <td>Softmax</td>\n",
        "    <td>10</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>Feedforward-NTM</td>\n",
        "    <td>0.01</td>\n",
        "    <td>0.5</td>\n",
        "    <td>100</td>\n",
        "    <td>Stochastic Gradient Descent</td>\n",
        "    <td>Softmax</td>\n",
        "    <td>10</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>LSTM</td>\n",
        "    <td>0.001</td>\n",
        "    <td>0.5</td>\n",
        "    <td>100</td>\n",
        "    <td>Adam</td>\n",
        "    <td>Softmax</td>\n",
        "    <td>10</td>\n",
        "  </tr>\n",
        "</table><br>\n",
        "* Note: In order to eliminate other hyper-parameter values and to pick the best ones for our tasks, we only let each model to progress for $200$ epochs, we observed that the loss curve decreases towards $0$. We then picked the values for which the loss curve better approached zero. The main reason we did not let our models to be trained passed $200$ epochs was purely due to our limited computational resources. We will discuss these issues further in the **Discussion** section."
      ]
    },
    {
      "metadata": {
        "id": "SaXiJ2KnOefY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **(c)** Generalization of The Models to Longer Sequences\n",
        "One of the benefits of the NTM over a vanilla LSTM is the ability to learn a simple algorithm that generalize to larger sequences. We tested our models on sequences of $T\\in\\{10,20,30,\\dots,100\\}$, with $20$ different inputs for each $T$.<br>\n",
        "For this experiment we expect to see a more robust generalization in the case of Neural Turing Machines, rather than the vanilla LSTM. This is because the vanilla LSTM has no access to any memory structure (in a Turing Machine sense of memory) and will fail to remember/recall the input for reproduction in the **Copy Task**. We expect that the inconsistency would increase with the length of the input, as the system will be having a harder time retaining the information. In the case of LSTM-NTM and Feedforward-NTM we expect to see better results for shorter sequences and slightly worse for the longer ones, note that this does not mean the methods are not robust but rather suggests that the cost will be slightly bigger for the longer sequences. We also expect to see a better performance in the case of LSTM-NTM since it will learn the **Copy Task** faster than Feedforward-NTM, and since we are limiting the training to $200$ epochs, LSTM-NTM has a better chance to perform better. Although theoretically the loss of both LSTM-NTM and Feedforard-NTM will converge and they both should perform somewhat similarly in this experiment. However, we did not have the needed computational powers, nor the time to achieve such states for either model and had to limit ourselves to $200$ epochs.\n",
        "\n",
        "To get a sense of convergence of these models we can reference to the paper *[Neural Turing Machines](https://arxiv.org/abs/1410.5401)*, there we can see that the convergence for the LSTM-NTM and Feedforward-NTM happens around $10,000$ and $50,000$ epochs respectively. \n",
        "\n",
        "Below is the plot of average loss vs $T$ found within our experiments:"
      ]
    },
    {
      "metadata": {
        "id": "7GkR7Zxl55ky",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "195fb060-725c-4ec0-8fef-bde4d4ec4970"
      },
      "cell_type": "code",
      "source": [
        "Image(\"avg_loss.png\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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ulFUoFDVeR3mq+wQKPT099O7dG71798Y333yDJUuW4IsvvsCJEyfQp08fuLu74+rVq+jd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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "tanzZnRo7Uma",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "As we have expected there is a huge difference between the loss of the vanilla LSTM and the NTM methods. We can also observe that there is a wide variance in the cost resulted form the vanilla LSTM method, as opposed to almost stationary cost values of the NTM methods. As discussed above this difference is due to the lack of the memory of the vanilla LSTM in comparison to the NTM methods. Note that as we have predicted the NTM methods perform well robust under the variant of the input size, keeping the loss almost stationary. If we also look well we can notice that for most parts LSTM-NTM performs slightly better than the Feedforward-NTM due to the reasons mentioned before."
      ]
    },
    {
      "metadata": {
        "id": "6fLJ_bfSOkD2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **(d)** Visualizing The Read and Write Heads/Attention\n",
        "We can visualize the read and write heads to get an idea of what algorithm is learned for the task. We first would like to take a look at the write and read head/attention provided by the paper *[Neural Turing Machines](https://arxiv.org/abs/1410.5401)*:"
      ]
    },
    {
      "metadata": {
        "id": "tHzjSF1Jrx8y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "74573a2c-37c6-4034-84c6-d5d440a1cb34"
      },
      "cell_type": "code",
      "source": [
        "Image('paper_rw.jpg')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "YdiazMYXulJF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We can see here the behavior of the well trained algorithm. It first writes to the memory and then reads from it to complete the **Copy Task**, much like a real program would do by copying the seen entry data until the end of it and then placing it for show by reading from it. We are expecting to see the same type of behaviour from our algorithm as well. Below is the graph of the write and read head/attention provided by our algorithm (LSTM-NTM) for $T=10$:"
      ]
    },
    {
      "metadata": {
        "id": "poG2N3Ugvzit",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "86e8881a-4b70-4036-8b7e-97d04f376b2f"
      },
      "cell_type": "code",
      "source": [
        "Image(\"exp_rw.jpg\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "N0TBJVrdxvZk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Note that our system is not well trained due to our limited resources (it has only been trained for $200$ epochs) but it is still performing as we have predicted. The accuracy of the **Copy Task** may not be optimal in our model however the mechanism is there, i.e. writing to memory upon seeing an input and then reading from the memory to predict the output or in other words completing the copying task. Our model as seen in the graph above, first tries to write to the memory and then reads form the exact same locations as it should however due to its low accuracy, it only does it well for the first few bits (the red circles). Although if we zoom in the region close to the red circles we will see that the white diagonal lines do actually continue further but turn more grayish. The interesting fact is that the grayish continuum we just mentioned does exist for both write and read, so the system is writing and reading but with less attention. We predict that with more training this attention would be adjusted and the diagonal white line would be more lengthy ($10$ in this case)."
      ]
    },
    {
      "metadata": {
        "id": "Em8uOuJ_OsE7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### **(e)** Understanding the shift operator\n",
        "In this section we will discuss the relationship between the shift operator and convolutions. We will also modify the code-base so that the shift operator only allows forward shifts. we will show the snippet of code before and after the modification.\n",
        "\n",
        "The shift operator defined in this setting is basically a convolution, if one knows what convolution is. In this discrete setting convolution is the sum of the point-wise multiplication of two vectors. Equation $[8]$ of the paper *[Neural Turing Machines](https://arxiv.org/abs/1410.5401)* depicts the said definition, hence the shift operator suggested in this setting is actually a simple convolution.\n",
        "\n",
        "The main function in the code that applies shift operator is the following:"
      ]
    },
    {
      "metadata": {
        "id": "FDtUMds00G_9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def gen_shift_tensor(leng, n_shifts):\n",
        "    \"\"\"\n",
        "    Paramters:  \n",
        "    ----------\n",
        "    leng: int > 0, number of memory locations\n",
        "    n_shifts: int > 0, number of allowed shifts (if 1, no shift)\n",
        "\n",
        "    Returns:\n",
        "    --------\n",
        "    shift operation, a tensor with dimensions (n_shifts, leng, leng)\n",
        "    \"\"\"\n",
        "    eye = np.eye(leng)\n",
        "    shifts = range(n_shifts//2, -n_shifts//2, -1)\n",
        "    C = np.asarray([np.roll(eye, s, axis=1) for s in shifts])\n",
        "    return K.variable(C.astype(K.floatx()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "K2HL5R1z0NA4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The above function first creates an identity matrix and then shifts it multiple times. The input **n_shifts** of this function indicates what kind of shifts we have. When the **shift_n** is $n$, we have a shift range of $\\left\\{-\\frac{n}{2},\\dots,-1,0,1,\\dots,\\frac{n}{2}\\right\\}$ i.e. for each value in this set there is a corresponding indentity matrix that has been shifted according to that value. Each of these shifted matrices when multiplied by another matrix, will shift that matrix with the corresponding shift number.\n",
        "\n",
        "This function then returns a tensor with **n_shifts** of shifted versions of the identity matrix. Therefore, when this tensor is multiplied by a vector, the results are **n_shifts** shifted versions of that vector.\n",
        "\n",
        "If we aim to allow only forward shifts, we need to not allow the negative numbers in this sequence. Therefore, we change this piece of code as follows:  "
      ]
    },
    {
      "metadata": {
        "id": "UZdCxuhk0Q0Q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def gen_shift_tensor(leng, n_shifts):\n",
        "    eye = np.eye(leng)\n",
        "    shifts = range(n_shifts//2, 0 , -1)\n",
        "    C = np.asarray([np.roll(eye, s, axis=1) for s in shifts])\n",
        "    return K.variable(C.astype(K.floatx()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "pDNU6Klo0TkZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The change is made on the third line: we pass $0$ as the second argument to the range function. This will only allow the positive numbers for the shift, which are only the forward shifts."
      ]
    },
    {
      "metadata": {
        "id": "NvdpEs4lWHUP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 3. Discussion \n",
        "We have trained $3$ models for the **Copy Task** (LSTM, Forward-NTM, LSTM-NTM). In this section we aim to address the difficulties we have encountered during this process and provide some analysis of the observed values.\n",
        "\n",
        "We tried to accomplish our experiment within a reasonable amount of time and with the limited computational resources at hand. Most of the results provided in this report is produced via the use of *[Google Colab](https://colab.research.google.com/)*. A few of the graphs (e.g the write and read head/attention) was produced off-line due to the incompatibility of Google Colab and Tensorflow (eg. *tensorflow.print*) at the time of this report. Using Google Colab let us to have access to a GPU unit which in return cut our computational time to almost $\\frac{1}{12}$ of the time that would have been needed without a GPU. It also let us accomplish our task in parallel in a group environment. However, we encountered a few drawbacks. The system would sometimes disconnect us from the GPU and lead to loss of our progress due to memory limitations and/or the process using the GPU for too long. These issues forced us to choose to only train each of our models for a maximum of $200$ epochs. Hence, not being able to fully converge the loss to an optimal value as we originally desired. \n",
        "\n",
        "Note that according to the paper *[Neural Turing Machines](https://arxiv.org/abs/1410.5401)* to get a sense of convergence for the LSTM-NTM and Feedforward-NTM we had to train our models around $10,000$ and $50,000$ epochs respectively (the paper reported the loss over $1\\mathrm{M}$ **epochs**). With our resources this would have taken us $27$ **hours** and $6$ **days** respectively. However as we have mentioned due to interruptions of Google Colab we could not let such trainings to take place.\n",
        "\n",
        "Now we would like to bring our attention to the plot below:\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "L9UGpWaeMSky",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "ee25b978-fd06-47a4-f981-ab2a2143a28d"
      },
      "cell_type": "code",
      "source": [
        "Image(\"loss_comp_all.jpg\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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Ql5AG6wNmqAJkKZyu2SUA6U+K9yXnb/avwspu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OMHJ0zgbhAwV9H5f5fx8/E5U174slnYmPaaj92Vq08h3X1qUG4SrWnDVUON1bVruJka9pXw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\nQCgFAKAUAoBQCgFAKAUAoBQCgFAKAUAoBQCgFAKAUAoBQH//2Q==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "NrgNIH40MuOT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here we can see that each of these models are progressing to decrease the loss. However, the vanilla LSTM is working in much larger scale of loss. If we let the vanilla LSTM to run even further we usually encounter a **reset** in the loss value, i.e. the loss increases and then starts to decreases again (i.e the model forgrets what it has learned and it starts to learn again). In the case of LSTM-NTM and Feedforward-NTM we can see that the loss is decreasing and the model is learning however the learning is more significant in the LSTM-NTM. To better compare their progress lets focus on the graph below:"
      ]
    },
    {
      "metadata": {
        "id": "n2TFkFPFNyIF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "464aef22-a891-404c-d0bb-1c50a60fb82c"
      },
      "cell_type": "code",
      "source": [
        "Image(\"loss_com_in_one.png\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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HXP8BOHv2ImlMboOTJltKCjlTfxVZz72wAOfpfUkYPiJygld/m104GKTs/Vns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\n4EBMAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "97fmua4sN8vz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here we can see the significance of NTM methods over the vanilla LSTM. The loss scale is much higher for the vanilla LSTM and it somewhat does not decrease past the first few epochs, as we expected, since it is not suitable for the **Copy Task**. To better see the difference between the Feedforward-NTM and the LSTM-NTM lets focus now on the graph below:"
      ]
    },
    {
      "metadata": {
        "id": "SMni-svjOr1a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "3c5651d7-b514-4d43-cc0f-8877eb3f3bcb"
      },
      "cell_type": "code",
      "source": [
        "Image(\"loss_comop_in_one2.png\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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8rGYjjMp8WdXJ84KIeo8VdiuLRr8dJzuHIUlSnnM2sQZYTEYtrC1JEk73RFHv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\nXYS6WewRI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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "us5rDq7yO4DP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here we can see that the drop in loss for LSTM-NTM  is much more significant in comparison to Feedforward-LSTM. This is consistent with the theory mentioned in the paper *[Neural Turing Machines](https://arxiv.org/abs/1410.5401)*. As the training continues we expect that the LSTM-NTM converges faster in comparison to the Feedforward-NTM. One could investigate this claim by letting the models run for about $10,000$ and $50,000$ epochs respectively. A survey on the web revealed in practice, by using good hyper-parameters, the said epochs should be more or less enough for convergence. A few of such surveys could be found in the **References** section of our report."
      ]
    },
    {
      "metadata": {
        "id": "O5MJceuh8yHh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## References\n",
        "\n",
        "1.    Graves, Alex, Greg Wayne, and Ivo Danihelka. \"Neural turing machines.\" arXiv preprint arXiv:1410.5401 (2014). https://arxiv.org/abs/1410.5401\n",
        "2.    Graves, Alex. \"Generating sequences with recurrent neural networks.\" arXiv preprint arXiv:1308.0850 (2013).[https://arxiv.org/abs/1308.0850](https://arxiv.org/abs/1308.0850)\n",
        "\n",
        "3.   https://github.com/EderSantana/seya/blob/master/seya/layers/ntm.py\n",
        "\n",
        "4.    https://github.com/flomlo/ntm_keras\n",
        "\n",
        "5.    https://github.com/SigmaQuan/NTM-Keras\n",
        "\n",
        "6.    https://github.com/carpedm20/NTM-tensorflow\n",
        "\n",
        "7.    https://github.com/shawntan/neural-turing-machines\n",
        "\n",
        "8.    https://github.com/snipsco/ntm-lasagne\n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ]
}