6. Recurrent Neural Networks I.ipynb

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        "# Recurrent Neural Networks I\n",
        "\n",
        "Classical neural networks, including convolutional ones, suffer from two severe limitations:\n",
        "\n",
        "+ They only accept a fixed-sized vector as input and produce a fixed-sized vector as output.\n",
        "+ They do not consider the sequential nature of some data (language, video frames, time series, etc.) \n",
        "\n",
        "Recurrent neural networks overcome these limitations by allowing to operate over sequences of vectors (in the input, in the output, or both)."
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        "## Vanilla Recurrent Neural Network\n",
        "\n",
        "The basic formulas of a simple RNN are:\n",
        "\n",
        "$$ \\mathbf s_t = \\mbox{tanh }(U \\mathbf x_t + W \\mathbf s_{t-1}) $$\n",
        "\n",
        "$$ \\mathbf y_t = V \\mathbf s_t $$ \n",
        "\n",
        "where the hiperbollic tangent funtion ``tanh`` is: \n",
        "\n",
        "<img src=\"https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/TanhReal.gif?raw=1\" alt=\"\" style=\"width: 300px;\"/>\n",
        "\n",
        "These equations basically say that the current network state $s_t$, commonly known as hidden state,  is a function of the previous hidden state $\\mathbf s_{t-1}$ and the current input $\\mathbf x_t$. $U, V, W$ matrices are the parameters of the RNN and $\\mathbf y_t$ is its output at time $t$. \n",
        "\n",
        "<img src=\"https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/vanilla.png?raw=1\" alt=\"\" style=\"width: 400px;\"/>\n"
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        "## Unrolling in time of a RNN\n",
        "\n",
        "Given an input sequence, we apply RNN formulas in a recurrent way until we process all input elements. **The RNN shares the parameters  $U,V,W$ across all recurrent steps**. \n",
        "\n",
        "Some important observations:\n",
        "+ We can think of the hidden state  as a memory of the network that captures information about the previous steps. **It embeds the representation of the sequence.**\n",
        "+ The output of the network can be considered at every stage or only at the final one (see bellow).\n",
        "+ When starting to train a RNN we must provide initial values for $U,V,W$ as well as for $\\mathbf s$.\n",
        "\n",
        "By unrolling we mean that we write out the network for the complete sequence:\n",
        "\n",
        "\n",
        "<img src=\"https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/unrolling.png?raw=1\" alt=\"\" style=\"width: 600px;\"/>\n",
        "\n",
        "Training a RNN involves the following steps:\n",
        "+ Providing a training set composed of several input ($n$-dimensional) sequences $\\{\\mathbf{X}_i \\}$ and their expected outcomes. Each element of a sequence $\\mathbf{x}_j \\in \\mathbf{X}_i$ is also a vector.\n",
        "+ Defining a loss function to measure the fitting of the output of the network to the expected outcome.\n",
        "+ Applying SGD (or variants) to optimize the loss function."
      ]
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        "## Vanilla Recurrent Neural Network (minibatch version)\n",
        "\n",
        "Observe that the number of parameters of a RNN can be very high:\n",
        "\n",
        "<img src=\"https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/minibatch.png?raw=1\" alt=\"\" style=\"width: 400px;\"/>\n"
      ]
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        "It is not necessary to have outputs $y_t$ at each time step. Depending on the problem we are solving, we can have the following RNN architectures:"
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        "<img src=\"https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/kar.png?raw=1\" alt=\"\" style=\"width: 600px;\"/>\n",
        "\n",
        "Source: http://karpathy.github.io/2015/05/21/rnn-effectiveness/"
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        "RNN have shown success in:\n",
        "\n",
        "+ Language modeling and generation.\n",
        "+ Machine Translation.\n",
        "+ Speech Recognition.\n",
        "+ Image Description.\n",
        "+ Question Answering.\n",
        "+ Etc."
      ]
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        "## RNN Computation\n",
        "\n",
        "Defining a simple RNN with code is simple:\n",
        "\n",
        "```python\n",
        "class RNN:\n",
        "    #...\n",
        "    def step(self,x):\n",
        "        self.h = np.tanh(np.dot(self.W_ss, self.h) + \n",
        "                 np.dot(self.W_xs, self.x))\n",
        "        y = np.dot(self.W_sy, self.h)\n",
        "        return y\n",
        "    #...\n",
        "```\n",
        "\n",
        "We can go deep by stacking RNNs:\n",
        "```python\n",
        "y1 = rnn1.step(x)\n",
        "y2 = rnn2.step(y1)\n",
        "```"
      ]
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        "### RNN and information representation\n",
        "\n",
        "The inputs of a recurrent network must be always **sequences of vectors**, but we can process sequences of symbols/words by representing these symbols by numerical vectors.\n",
        "\n",
        "Let's suppose we are classifying a series of words $\\mathbf x_1, ..., \\mathbf x_{t-1}, \\mathbf x_t, \\mathbf x_{t+1}, ... \\mathbf x_{n}$, where $\\mathbf x_i$ are word vectors corresponding to a document with $n$ words that belong to a corpus with $|V|$ symbols. \n",
        "\n",
        "Then, the relationship to compute the hidden layer features at each time-step $t$ is $\\mathbf s_t = \\sigma(W^{(ss)} \\mathbf s_{t-1} + W^{(sx)} \\mathbf x_{t})$, where:\n",
        "\n",
        "   + $\\mathbf x_{t} \\in \\mathbb{R}^{d}$ is input word vector at time $t$. \n",
        "   + $W^{(sx)} \\in \\mathbb{R}^{D_s \\times d}$ is the weights matrix used to condition the input word vector, $x_t$.\n",
        "   + $W^{(ss)} \\in \\mathbb{R}^{D_s \\times D_s}$ is the weights matrix used to condition the state of the previous time-step state, $s_{t-1}$.\n",
        "   + $\\mathbf s_{t-1}  \\in \\mathbb{R}^{D_s}$ is the state at the previous time-step, $s-1$. \n",
        "   + $\\mathbf s_0 \\in \\mathbb{R}^{D_s}$ is an initialization vector for the hidden layer at time-step $t = 0$.\n",
        "   + $\\sigma ()$ is the non-linearity function (normally, ``tanh``).\n",
        "   \n",
        "In this case, the output of the network can be defined to be $\\hat{\\mathbf y}_t = softmax (W^{(sy)}\\mathbf s_t)$ and the loss function cross-entropy, because we are dealing with a multiclass classification problem. \n",
        "\n",
        "$\\hat{\\mathbf y}$ is the output probability distribution over the vocabulary at each time-step $t$ (i.e. it is a high dimensional vector!).  \n",
        "\n",
        "Essentially, $\\hat{\\mathbf y}_t$ is the next predicted word given the document context score so far (i.e. $\\mathbf s_{t-1}$) and the last observed word vector $\\mathbf x^{(t)}$. Here, $W^{(sy)} \\in \\mathbb{R}^{|V|\\times D_h}$ and $\\hat{\\mathbf y} \\in \\mathbb{R}^{|V|}$ where $|V|$ is the cardinality of the vocabulary.\n",
        "\n",
        "The loss function used in RNNs is often the cross entropy error:\n",
        "\n",
        "$$\n",
        "\tL^{(t)} = - \\sum_{j=1}^{|V|} y_{t,j} \\times log (\\hat{y}_{t,j})\n",
        "$$\n",
        "\n",
        "The cross entropy error over a document of size $n$ is:\n",
        "\n",
        "$$\n",
        "\tL = \\dfrac{1}{n} \\sum_{t=1}^n L^{(t)}(W) = - \\dfrac{1}{n} \\sum_{t=1}^{n} \\sum_{j=1}^{|V|} y_{t,j} \\times log (\\hat{y}_{t,j})\n",
        "$$\n"
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        "In the case of classifying a series of symbols/words, the *perplexity* measure can be used to assess the goodness of our model. It is basically 2 to the power of the negative log probability of the cross entropy error function:\n",
        "\n",
        "$$\n",
        "\tPerplexity = 2^{L}\n",
        "$$\n",
        "\n",
        "Perplexity is a measure of confusion where lower values imply more confidence in predicting the next word in the sequence (compared to the ground truth outcome)."
      ]
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        "## RNN Training\n",
        "\n",
        "> Training a RNN is similar to training a traditional NN, but some modifications. The main reason is that parameters are shared by all time steps: in order to compute the gradient at $t=4$, we need to propagate 3 steps and sum up the gradients. This is called **Backpropagation through time (BPTT)**.\n",
        "\n",
        "Recurrent neural networks propagate weight matrices from one time-step to the next. Recall the goal of a RNN implementation is to enable propagating context information through faraway time-steps. When these propagation results in a long series of matrix multiplications, weights can **vanish or explode**. \n",
        "\n",
        "+ Once the gradient value grows extremely large, it causes an overflow (i.e. ``NaN``) which is easily detectable at runtime; this issue is called the *Gradient Explosion Problem*. \n",
        "\n",
        "+ When the gradient value goes to zero, however, it can go undetected while drastically reducing the learning quality of the model for far-away words in the corpus; this issue is called the *Vanishing Gradient Problem*.\n",
        "\n",
        "There are several tricks to mitigate these problems:"
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        "### Gradient Clipping\n",
        "\n",
        "To solve the problem of exploding gradients, Thomas Mikolov first introduced a simple heuristic solution that *clips* gradients to a small number whenever they explode. That is, whenever they reach a certain threshold, they are set back to a small number. \n",
        "\n",
        "<img src=\"https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/exploding.png?raw=1\" alt=\"\" style=\"width: 400px;\"/>\n"
      ]
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        "### Better initialization\n",
        "\n",
        "To solve the problem of vanishing gradients, instead of initializing $W^{ss}$ randomly, starting off from **random orthogonal matrices** works better,  i.e., a square matrix $W$ for which $W^T W=I$.\n",
        "\n",
        "There are two properties of orthogonal matrices that are useful for training deep neural networks:\n",
        "+ they are norm-preserving, i.e., $ ||W \\mathbf x||^2=||\\mathbf x||^2$, and\n",
        "+ their columns (and rows) are all orthonormal to one another.\n",
        "\n",
        "At least at the start of training, the first of these should help to keep the norm of the input constant throughout the network, which can help with the problem of exploding/vanishing gradients. \n",
        "\n",
        "Similarly, an intuitive understanding of the second is that having orthonormal weight vectors encourages the weights to learn different input features.\n",
        "\n",
        "You can obtain a random $n \\times n$ orthogonal matrix $W$, (uniformly distributed) by performing a QR factorization of an $n \\times n$ matrix with elements i.i.d. Gaussian random variables of mean $0$ and variance $1$. Here is an example:"
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      "source": [
        "import numpy as np\n",
        "from scipy.linalg import qr\n",
        "\n",
        "n = 3\n",
        "H = np.random.randn(n, n)\n",
        "print(H)\n",
        "print('\\n')\n",
        "\n",
        "Q, R = qr(H)\n",
        "\n",
        "print (Q.dot(Q.T))"
      ],
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            "[[ 1.80419174  0.44753171 -0.74705221]\n",
            " [-0.36897419 -1.44770945  0.43443346]\n",
            " [ 1.49721941 -0.62861191 -2.09674099]]\n",
            "\n",
            "\n",
            "[[ 1.00000000e+00 -4.83279136e-18 -1.72126600e-16]\n",
            " [-4.83279136e-18  1.00000000e+00  4.02741050e-17]\n",
            " [-1.72126600e-16  4.02741050e-17  1.00000000e+00]]\n"
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        "### Steeper Gates\n",
        "\n",
        "We can make the \"gates steeper\" so they change more repidly from 0 to 1 and the model is learnt quicker.\n",
        "\n",
        "<img src=\"https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/steeper.png?raw=1\" alt=\"\" style=\"width: 600px;\"/>\n"
      ]
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        "## Gated RNN\n",
        "\n",
        "Recurrent Neural Networks work just fine when we are dealing with short-term dependencies. \n",
        "However, vanilla RNNs fail to understand the lomg-term context dependencies (when relevant information may be separated from the point where it is needed by a huge load of irrelevant data).\n",
        "\n",
        "Gated RNNs (with units that are designed to forget and to update relevant information) are a solution to this problem.\n",
        "\n",
        "The most important types of gated RNNs are:\n",
        "\n",
        "+ **Long Short Term Memories** (LSTM). It was introduced by S.Hochreiter and J.Schmidhuber in 1997 and is widely used. LSTM is very good in the long run due to its high complexity.\n",
        "+ **Gated Recurrent Units** (GRU). It was recently introduced by K.Cho. It is simpler than LSTM, faster and optimizes quicker. \n"
      ]
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        "#### LSTM\n",
        "\n",
        "The key idea of LSTMs is to have two state representations: the hidden state $\\mathbf h$ and the cell state $\\mathbf C$ (instead of $\\mathbf s$). \n",
        "\n",
        "The cell state $\\mathbf C$ is like a conveyor belt. It runs straight down the entire chain, with only some minor linear interactions. It’s very easy for information to just flow along it unchanged.\n",
        "\n",
        "<img src=\"https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/lstm.png?raw=1\" alt=\"Source: http://colah.github.io/posts/2015-08-Understanding-LSTMs/\" style=\"width: 600px;\"/> \n",
        "\n",
        "LSTM has the ability to remove or add information to the cell state, carefully regulated by structures called **gates**.\n",
        "\n",
        "Gates are a way to optionally let information through. They are composed out of a *sigmoid* neural net layer and a pointwise multiplication operation.\n",
        "\n",
        "Let us see how a LSTM uses $\\mathbf h_{t-1}, \\mathbf C_{t-1}$ and $\\mathbf x_{t}$ to generate the next states $\\mathbf C_t, \\mathbf h_{t}$:\n",
        "\n",
        "$$ \\mathbf f_t = \\sigma(W^f \\cdot [\\mathbf h_{t-1}, \\mathbf x_t]) \\mbox{ (Forget gate)} $$\n",
        "$$ \\mathbf i_t = \\sigma(W^i \\cdot [\\mathbf h_{t-1}, \\mathbf x_t]) \\mbox{ (Input gate)} $$\n",
        "$$ \\tilde {\\mathbf C_t} = \\operatorname{tanh}(W^C \\cdot [\\mathbf h_{t-1}, \\mathbf x_t]) $$\n",
        "$$ \\mathbf C_t = \\mathbf f_t \\cdot \\mathbf C_{t-1} + \\mathbf i_t \\cdot \\tilde {\\mathbf C_t} \\mbox{ (Update gate)} $$\n",
        "$$ \\mathbf o_t = \\sigma(W^o \\cdot [\\mathbf h_{t-1}, \\mathbf x_t]) $$\n",
        "$$ \\mathbf h_t = \\mathbf o_t \\cdot \\operatorname{tanh}(\\mathbf C_t) \\mbox{ (Output gate)} $$\n",
        "\n",
        "where $[\\mathbf h_{t-1}, \\mathbf x_t]$ represents the concatenation of two vectors.\n",
        "\n",
        "There are other variants of LSTM (f.e. LSTM with peephole connections of Gers & Schmidhuber (2000))"
      ]
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        "#### GRU\n",
        "\n",
        "The transition from hidden state $\\mathbf h_{t-1}$ to $\\mathbf h_{t}$ in vanilla RNN is defined by using an affine transformation and a point-wise nonlinearity. \n",
        "\n",
        "What motivates the use of gated units? Although RNNs can theoretically capture long-term dependencies, they are very hard to actually train to do this. Gated recurrent units are designed in a manner to have more persistent memory thereby making it easier for RNNs to capture long-term dependencies. \n",
        "\n",
        "<img src=\"https://github.com/DataScienceUB/DeepLearningMaster2019/blob/master/images/gru.png?raw=1\" alt=\"Source: http://colah.github.io/posts/2015-08-Understanding-LSTMs/\" style=\"width: 300px;\"/> \n",
        "\n",
        "Let us see how a GRU uses $\\mathbf h_{t-1}$ and $\\mathbf x_{t}$ to generate the next hidden state $\\mathbf h_{t}$.\n",
        "\n",
        "$$ \\mathbf z_{t} = \\sigma(W_z \\cdot [\\mathbf x_{t}, \\mathbf h_{t-1}]) \\mbox{ (Update gate)}$$\n",
        "$$ \\mathbf r_{t} = \\sigma(W_r \\cdot [\\mathbf x_{t}, \\mathbf h_{t-1}]) \\mbox{ (Reset gate)}$$\n",
        "$$ \\tilde{\\mathbf h}_{t} = \\operatorname{tanh}(\\mathbf r_{t} \\cdot  [\\mathbf x_{t}, \\mathbf r_t \\cdot \\mathbf h_{t-1}]   ) \\mbox{ (New memory)}$$\n",
        "$$ \\mathbf h_{t} = (1 - \\mathbf z_{t}) \\cdot \\mathbf h_{t-1} + \\mathbf z_{t} \\cdot \\tilde{\\mathbf h}_{t} \\mbox{(Hidden state)}$$\n",
        "\n",
        "It combines the forget and input gates into a single “update gate.” It also merges the cell state and hidden state, and makes some other changes. The resulting model is simpler than standard LSTM models."
      ]
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      "source": [
        "### RNN in Keras\n",
        "\n",
        "Whenever you train or test your LSTM/GRU, you first have to build your input matrix $\\mathbf X$ of shape ``[nb_samples,timesteps,input_dim]`` where your batch size divides ``nb_samples``. \n",
        "\n",
        "For instance, if ``nb_samples=1024`` and ``batch_size=64``, it means that your model will receive blocks of 64 samples, compute each output (whatever the number of timesteps is for every sample), average the gradients and propagate it to update the parameters vector. \n",
        "\n",
        "> By default, **Keras shuffles (permutes) the samples in $\\mathbf X$** and consequently the dependencies between $\\mathbf X_i$ and $\\mathbf X_{i+1}$ are not considered. If\n",
        "$\\mathbf X_i$ and $\\mathbf X_{i+1}$ represent independent sequences (f.e. different instances of a times series), this is not a problem. but if it represents parts of a bigger sequence (f.e. words in a text) it is!\n",
        "\n",
        "> By using the **stateful model** all the states are propagated to the next batch. We must also prevent shuffling in the ``fit`` method."
      ]
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      "source": [
        "from __future__ import print_function\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import LSTM, GRU\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "tsteps = 1\n",
        "\n",
        "# number of elements ahead that are used to make the prediction\n",
        "lahead = 1\n",
        "\n",
        "\n",
        "def gen_cosine_amp(amp=100, period=10, x0=0, xn=50000, step=1, k=0.0001):\n",
        "    \"\"\"\n",
        "    Generates an absolute cosine time series with the amplitude\n",
        "    exponentially decreasing\n",
        "    Arguments:\n",
        "        amp: amplitude of the cosine function\n",
        "        period: period of the cosine function\n",
        "        x0: initial x of the time series\n",
        "        xn: final x of the time series\n",
        "        step: step of the time series discretization\n",
        "        k: exponential rate\n",
        "    \"\"\"\n",
        "    cos = np.zeros((xn - x0) * step)\n",
        "    for i in range(len(cos)):\n",
        "        idx = x0 + i * step\n",
        "        cos[i] = amp * np.cos(idx / (2 * np.pi * period))\n",
        "        cos[i] = cos[i] * np.exp(-k * idx)\n",
        "    cos = cos / np.max(cos)\n",
        "    return cos\n",
        "\n",
        "cos = gen_cosine_amp()\n",
        "plt.figure(figsize=(15,7))\n",
        "plt.plot(cos)"
      ],
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kwNVOHvzxrrV0/jlz2siCRuS5DU209YZOzpk4w1acOfkes07pl0qqp3IGreOl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\nU97z6c4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "g6bT_95_hqeK",
        "colab_type": "code",
        "outputId": "31bf45c0-c630-4291-8ff0-1626e410ea71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "# print('Generating Input Data')\n",
        "\n",
        "lenght_series = 400\n",
        "cos = gen_cosine_amp()\n",
        "cos_series = np.zeros((50000-lenght_series, lenght_series, 1))\n",
        "\n",
        "for i in range(50000-lenght_series):\n",
        "    cos_series[i,:,:] = cos[i:i+lenght_series, np.newaxis]\n",
        "    \n",
        "\n",
        "\n",
        "print('Input shape:', cos_series.shape)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input shape: (49600, 400, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hmbtkFJhhqeM",
        "colab_type": "code",
        "outputId": "ba899ff8-c57f-4384-a04f-bc11193b0ab5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "cell_type": "code",
      "source": [
        "expected_output = np.zeros((len(cos)-lenght_series, 1))\n",
        "for i in range(len(cos_series)-1):\n",
        "    expected_output[i, 0] = cos[i+lenght_series]\n",
        "\n",
        "print('Output shape', expected_output.shape)\n",
        "print(\"Sample: \", expected_output[0])\n",
        "\n",
        "plt.figure(figsize=(20,5))\n",
        "plt.plot(expected_output,'-r')\n",
        "plt.title('Expected')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Output shape (49600, 1)\n",
            "Sample:  [0.95748091]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IIYQQQgghB4azjp4lSfKZOI6viuP4MwBSAL8cx/HzAWwkSfKP+csuAXDCOPb3AP42juNf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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Vhht7jj3hqeP",
        "colab_type": "code",
        "outputId": "f111a7a6-4237-495d-fc60-e898645e2a8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "print('Creating Model')\n",
        "\n",
        "model = Sequential()\n",
        "model.add(LSTM(50,\n",
        "               batch_input_shape=(None, lenght_series, 1),\n",
        "               return_sequences=True,   # This param indicates whether to return \n",
        "                                        # the last output in the output\n",
        "                                        # sequence, or the full sequence.\n",
        "               stateful=False))\n",
        "model.add(LSTM(50,\n",
        "               batch_input_shape=(None, lenght_series, 1),\n",
        "               return_sequences=False,\n",
        "               stateful=False))\n",
        "model.add(Dense(1))\n",
        "\n",
        "model.compile(loss='mse', optimizer='adam')\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Creating Model\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/losses_utils.py:170: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm (LSTM)                  (None, 400, 50)           10400     \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 50)                20200     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 51        \n",
            "=================================================================\n",
            "Total params: 30,651\n",
            "Trainable params: 30,651\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "L85H0kc_hqeR",
        "colab_type": "code",
        "outputId": "97116408-8b39-4f62-9846-9fc924c117c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "cell_type": "code",
      "source": [
        "print('Training')\n",
        "\n",
        "epochs = 1\n",
        "batch_size = 100\n",
        "\n",
        "\n",
        "model.fit(cos_series,\n",
        "          expected_output,\n",
        "          batch_size=batch_size,\n",
        "          verbose=1, \n",
        "          epochs=epochs,\n",
        "          shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "49600/49600 [==============================] - 491s 10ms/sample - loss: 6.2636e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fd93b3d0ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "myKqX2ZThqeT",
        "colab_type": "code",
        "outputId": "2033401e-499e-48c4-acdf-b1df71f48e55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "cell_type": "code",
      "source": [
        "print('Predicting...')\n",
        "predicted_output = model.predict(cos_series, batch_size=batch_size)\n",
        "\n",
        "print('Ploting Results')\n",
        "plt.figure(figsize=(20,5))\n",
        "plt.plot(predicted_output,'-g')\n",
        "plt.plot(expected_output - predicted_output,'-r')\n",
        "\n",
        "\n",
        "plt.title('Predicted Output and Error')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicting...\n",
            "Ploting Results\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CyNkNhaIp0XSbotAz1FxCkuMoTuLCCVQnJKltaf2oL863RuOFos/v+xyWe0fw+1/43bHH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\n7fykeUvhKwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7DEiyisshqeW",
        "colab_type": "code",
        "outputId": "9cbd3f3d-3f50-42ac-9d7a-c304a22631e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 6968
        }
      },
      "cell_type": "code",
      "source": [
        "future = np.zeros((1,400,1))\n",
        "future[0,:,:] = cos[10000:10400, np.newaxis]\n",
        "print(future)\n",
        "\n",
        "predicted_future = model.predict(future)\n",
        "print(predicted_future)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[[-0.17782635]\n",
            "  [-0.18291082]\n",
            "  [-0.18794796]\n",
            "  [-0.19293648]\n",
            "  [-0.19787513]\n",
            "  [-0.20276269]\n",
            "  [-0.2075979 ]\n",
            "  [-0.21237957]\n",
            "  [-0.21710649]\n",
            "  [-0.22177748]\n",
            "  [-0.22639136]\n",
            "  [-0.23094698]\n",
            "  [-0.23544319]\n",
            "  [-0.23987887]\n",
            "  [-0.2442529 ]\n",
            "  [-0.24856419]\n",
            "  [-0.25281167]\n",
            "  [-0.25699426]\n",
            "  [-0.26111093]\n",
            "  [-0.26516063]\n",
            "  [-0.26914237]\n",
            "  [-0.27305514]\n",
            "  [-0.27689797]\n",
            "  [-0.2806699 ]\n",
            "  [-0.28436998]\n",
            "  [-0.2879973 ]\n",
            "  [-0.29155094]\n",
            "  [-0.29503004]\n",
            "  [-0.29843371]\n",
            "  [-0.30176111]\n",
            "  [-0.30501142]\n",
            "  [-0.30818382]\n",
            "  [-0.31127753]\n",
            "  [-0.31429178]\n",
            "  [-0.31722582]\n",
            "  [-0.32007893]\n",
            "  [-0.3228504 ]\n",
            "  [-0.32553954]\n",
            "  [-0.32814569]\n",
            "  [-0.3306682 ]\n",
            "  [-0.33310646]\n",
            "  [-0.33545986]\n",
            "  [-0.33772783]\n",
            "  [-0.3399098 ]\n",
            "  [-0.34200524]\n",
            "  [-0.34401364]\n",
            "  [-0.3459345 ]\n",
            "  [-0.34776736]\n",
            "  [-0.34951177]\n",
            "  [-0.35116731]\n",
            "  [-0.35273357]\n",
            "  [-0.35421017]\n",
            "  [-0.35559677]\n",
            "  [-0.35689302]\n",
            "  [-0.35809862]\n",
            "  [-0.35921328]\n",
            "  [-0.36023673]\n",
            "  [-0.36116874]\n",
            "  [-0.36200908]\n",
            "  [-0.36275756]\n",
            "  [-0.36341402]\n",
            "  [-0.36397829]\n",
            "  [-0.36445027]\n",
            "  [-0.36482984]\n",
            "  [-0.36511693]\n",
            "  [-0.36531148]\n",
            "  [-0.36541347]\n",
            "  [-0.36542289]\n",
            "  [-0.36533975]\n",
            "  [-0.36516409]\n",
            "  [-0.36489597]\n",
            "  [-0.36453549]\n",
            "  [-0.36408275]\n",
            "  [-0.36353789]\n",
            "  [-0.36290106]\n",
            "  [-0.36217243]\n",
            "  [-0.36135222]\n",
            "  [-0.36044066]\n",
            "  [-0.35943798]\n",
            "  [-0.35834446]\n",
            "  [-0.3571604 ]\n",
            "  [-0.35588611]\n",
            "  [-0.35452194]\n",
            "  [-0.35306824]\n",
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            "  [-0.19123374]]]\n",
            "[[-0.19472048]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "zf6MVkMhhqea",
        "colab_type": "code",
        "outputId": "6e7a1a21-c221-4822-87e2-e281c01699ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "cell_type": "code",
      "source": [
        "future = np.zeros((1,lenght_series,1))\n",
        "future[0,:,:] = cos[:lenght_series, np.newaxis]\n",
        "predicted_future = np.zeros((1000,))\n",
        "\n",
        "\n",
        "for i in range(1000):\n",
        "    predicted_future[i] = model.predict(future)\n",
        "    future[0,0:lenght_series-1,0] = future[0,1:lenght_series,0]\n",
        "    future[0,lenght_series-1,0] = predicted_future[i]\n",
        "\n",
        "print(predicted_future[0])\n",
        "plt.plot(predicted_future,'-g')\n",
        "plt.plot(cos[:1000],'-r')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9502222537994385\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd93b3e2c88>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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spSN8HoyaY/v8W3iy7kn+WP4XvlyyxOspvP3dFrILSRQE7G/softzX/V6/kBy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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "q7rLU-rThqed",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Optional Exercise\n",
        "\n",
        "Read and execute carefully this notebook: [Chollet-advanced-usage-of-recurrent-neural-networks.ipynb](Chollet-advanced-usage-of-recurrent-neural-networks.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "q0hFsN6UmnXv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}