08.TimeSeries-Transformers.ipynb

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  "<a href=\"https://colab.research.google.com/github/oscar-defelice/TimeSeries-lectures/blob/development/08.TimeSeries-Transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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 "source": [
  "# Time Series Analysis and Forecasting\n",
  "\n",
  "This is a series of notebooks to support lectures on _Time series analysis and forecast_.\n",
  "\n",
  "![](https://user-images.githubusercontent.com/49638680/154160268-cf39a1ec-3557-4940-8853-d06fc7a79acf.png)\n",
  "\n",
  "## Time series Forecast and Classification\n",
  "\n",
  "In paticular, over this notebook we are going to use a Recurrent Neural Network architecture with _Attention Mechanism_ in order to implement a classifier of values associated to several time series, that will play the role of features.\n",
  "\n",
  "### Some references\n",
  "\n",
  "1. [Bahdanau Attention Mechanism](https://arxiv.org/abs/1409.0473)\n",
  "2. [Transformer Architecture](https://arxiv.org/abs/1706.03762)\n",
  "3. [Adversarial Sparse Transformer for Time Series Forecasting](https://proceedings.neurips.cc/paper/2020/file/c6b8c8d762da15fa8dbbdfb6baf9e260-Paper.pdf)\n",
  "4. [Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case](https://arxiv.org/abs/2001.08317)\n",
  "5. [The Time Series Transformer](https://towardsdatascience.com/the-time-series-transformer-2a521a0efad3)\n",
  "6. [Transformers for Time-series Forecasting](https://medium.com/mlearning-ai/transformer-implementation-for-time-series-forecasting-a9db2db5c820)"
 ]
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  "# Bahdanau Attention Mechanism\n",
  "\n",
  "Consider a simple encoder-decoder newtork. This model is based on the one described in the [2014 `Seq2Seq` paper by Ilya Sutskever, Oriol Vinyals and Quoc V. Le](https://research.google/pubs/pub43155/).\n",
  "\n",
  "![Image by the author](https://user-images.githubusercontent.com/49638680/156374648-1759a98c-34d2-418a-8b71-0254ca1c87ea.png)\n",
  "\n",
  "The idea is to convert an _input sequence_ - in our case a time series, but it can be a sentence to be translated (as in the paper) - into an hidden latent vector and then use this as input for the decoder part and get the _output sequence_. Again, the output sequence can be a time series forecast or the translated sentence into another language, for instance.\n",
  "For the sake of clarity and to avoid confusion, in the following we are going to refer only to _sequences_ having in mind they can be time series, texts, audio tracks, etc.\n",
  "\n",
  "To schematise:\n",
  "\n",
  "* __Encoder__: Such part of the network takes the input sequence and convert it into a fixed-length (_i.e._ the latent space dimension) encoding vector.\n",
  "\n",
  "* __Decoder__: Such part of the network takes the encoding vector and convert it into a variable-length output sequence.\n",
  "\n",
  "In principle there is no prescription about the kind of cells Encoder and Decoder network should be composed of, however, usually these are LSTM or GRU to avoid vanishing/exploding gradients.\n",
  "\n",
  "## Bottleneck of information\n",
  "\n",
  "The great point of strength of such a system is the conversion of the input sequence into a latent space vector. This allows to put information in a form a computer can handle easily and also to give importance only to the relevant parts of the sequence. However, this is also one of the gratest weaknesses (along with the computational efficiency) of such an architecture.\n",
  "\n",
  "Indeed, the latent space has a _fixed dimension_, meaning that a long sequence will produce the very same amount of information than a shorter one. This would eventually cause what is called _bottleneck of information_, that is possibly only a certain amount of information is transmitted to the decoder.\n",
  "\n",
  "![](https://user-images.githubusercontent.com/49638680/156526457-5411e2cf-c968-4001-a66e-f666b12072d7.png)\n",
  "\n",
  "One solution would be to pass all the hidden states (not only the final one) from the encoder and average them. This of course is not a good idea, since we will lose context information and we would use a large amount of memory.\n",
  "\n",
  "A better solution is to select which hidden states contribute the most at the output. This is precisely what the _Attention mechanism_ pursues.\n",
  "\n",
  "## The Attention Layer\n",
  "\n",
  "Roughly speaking, it is a weighted sum, whose weights are _learnt_. This makes the _Attention layer_ a trainable layer.\n",
  "\n",
  "![](https://user-images.githubusercontent.com/49638680/156533074-8fd5df79-1337-4ee8-8445-2b8ec696b29c.png)\n",
  "\n",
  "# Transformer Architecture: Queries, Keys, Values\n",
  "\n",
  "Inspired to efficient research techniques in databases, we can define the _attention score_ as a similarity between two vectors (_queries_ and _keys_).\n",
  "\n",
  "The famous formula is\n",
  "\n",
  "$$\\mathcal{A} = \\mathrm{softmax}\\left(\\dfrac{QK^T}{\\sqrt{d_K}}\\right) V \\, .$$\n",
  "\n",
  "_Note_: this kind of attention is really easy to compute (it is really just matrix multiplication), but this means the embedding vectors need to be learnt elsewhere.\n",
  "\n",
  "We are going to explore how this is the basic ingredient of _Transformer architecture_.\n",
  "\n",
  "## Transformer Architecture\n",
  "\n",
  "The main idea is to create a _model_ recognising self-patterns, _e.g._ in NLP you want to have high weights in embeddings dot-products of tokens referring to the same property.\n",
  "\n",
  "The two main ingredients of transformer architecture are\n",
  "\n",
  "1. Self-Attention\n",
  "2. Multi-Head Attention\n",
  "\n",
  "### Self-Attention\n",
  "\n",
  "The aim is to create a vector representation (self-attention) for each element of the sequence.\n",
  "\n",
  "We can denote such vectors as\n",
  "\n",
  "$$A^{<t>}(q, k, v)\\, .$$\n",
  "\n",
  "The great advantage is that we can take into account the _context_ that is the other points in the sequence affecting the value of the one we are analysing.\n",
  "\n",
  "**Attention weights**: the vector components can be represented as follows:\n",
  "\n",
  "$$ A^{<t>}(q, k, v) = \\sum_i \\frac{\\exp(q\\cdot k^{<i>}}{\\sum_j \\exp(q \\cdot k^{<j>})} v^{<i>} \\, .$$\n",
  "\n",
  "In other words, we are saying that for each sequence element $<t>$ we have $3$ objects:\n",
  "* $q^{<t>} = W_q x^{<t>}$ query\n",
  "* $k^{<t>} = W_k x^{<t>}$ key\n",
  "* $v^{<t>} = W_v x^{<t>}$ value\n",
  "\n",
  "These are all functions of the word embedding vectors and are the inputs used to calculate attention vector $A^{<t>}$.\n",
  "\n",
  "The formula we wrote above for $\\mathcal{A}$ is the vectorised version of $A^{<t>}$ for all $<t>$.\n",
  "\n",
  "### Multi-Head Attention\n",
  "\n",
  "The idea now is to repeat the calculation for the self-attention in order to get an adapted vector representation for different _queries_.\n",
  "\n",
  "As an intuition, infact, you can think of $q$ as a \"_question_\" about the precise dependence of an element of the sequence with respect to another. _E.g._ We can say that in a time series a certain periodical region of data will manifest a decreasing rate because of seasonality, and gets an higher attention score for the $q$ related to the periodic behaviour of the series. If we look at a query modelling the trend, we may hope that the peaks of the time series would get an higher attention score.\n",
  "\n",
  "The idea is to perform a for-loop over the \"heads\" in order to achieve a richer and more complete representation of sequences, adn also to get a much higher numerical performance.\n",
  "\n",
  "![](https://user-images.githubusercontent.com/49638680/156577344-6d2914b8-f456-4f1e-9dd1-0a31d62b2ace.png)\n",
  "\n",
  "\n",
  "## A nice reference\n",
  "\n",
  "I strongly advice to have a look at [this nice post](https://towardsdatascience.com/illustrated-self-attention-2d627e33b20a#570c) plenty of well exposed ideas and of wonderful illustrations."
 ]
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  "# Time series with attention models\n",
  "\n",
  "For these kinds of tasks, a pretty straightforward procedure would be to use an autoregressive model of some kind (like *ARIMA*, *ARMAX*); these models allow us to take into account autocorrelations in a time series, and also can accept the deterministic features in the future (typically called “exogenous variables”). One limitation of ARMAX is that it is a linear model, and also one needs to specify the order of autocorrelations to be taken into account parametrically. \n",
  "Attention models, instead, can learn nonlinear patterns, and are able to take into account autocorrelations in a nonparametric way, focusing on the most important contributions.\n",
  "\n",
  "Furthermore, Attention model allows us to take into account longer sequences avoind the information bottleneck problem and focusing on the most relevant part of the past in order to forecast the future behaviour of the time series."
 ]
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  {
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   "text": [
    "INFO:tensorflow:Enabling eager execution\n",
    "INFO:tensorflow:Enabling v2 tensorshape\n",
    "INFO:tensorflow:Enabling resource variables\n",
    "INFO:tensorflow:Enabling tensor equality\n",
    "INFO:tensorflow:Enabling control flow v2\n"
   ]
  }
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 "source": [
  "# Load libraries\n",
  "import tensorflow as tf\n",
  "\n",
  "import os\n",
  "import pandas as pd\n",
  "import numpy as np\n",
  "\n",
  "import matplotlib.pyplot as plt\n",
  "import seaborn as sns\n",
  "%matplotlib inline\n",
  "%config InlineBackend.figure_format = \"retina\"\n",
  "sns.set()"
 ]
},
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 "cell_type": "markdown",
 "metadata": {
  "id": "ZXAtbCCYG5rC"
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 "source": [
  "# Importing data and building dataset"
 ]
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  "Let’s start with a practical example of a time series and look at the [FordA dataset from the UCR/UEA archive.](https://www.cs.ucr.edu/~eamonn/time_series_data_2018/). \n",
  "\n",
  "The dataset we are using here is called FordA. The data comes from the UCR archive. The dataset contains $3601$ training instances and another $1320$ testing instances. \n",
  "Each timeseries corresponds to a measurement of engine noise captured by a motor sensor. \n",
  "\n",
  "An example task, woud be the automatic detection of the presence of a specific issue with the engine. The problem is a balanced binary classification task. The full description of this dataset can be found [here](http://www.j-wichard.de/publications/FordPaper.pdf)."
 ]
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 "source": [
  "def readucr(filename):\n",
  "    data = np.loadtxt(filename, delimiter=\"\\t\")\n",
  "    y = data[:, 0]\n",
  "    x = data[:, 1:]\n",
  "    return x, y.astype(int)\n",
  "\n",
  "\n",
  "root_url = \"https://raw.githubusercontent.com/hfawaz/cd-diagram/master/FordA/\"\n",
  "\n",
  "x_train, y_train = readucr(root_url + \"FordA_TRAIN.tsv\")\n",
  "x_test, y_test = readucr(root_url + \"FordA_TEST.tsv\")\n",
  "\n",
  "x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], 1))\n",
  "x_test = x_test.reshape((x_test.shape[0], x_test.shape[1], 1))\n",
  "\n",
  "n_classes = len(np.unique(y_train))\n",
  "\n",
  "idx = np.random.permutation(len(x_train))\n",
  "x_train = x_train[idx]\n",
  "y_train = y_train[idx]\n",
  "\n",
  "# In the dataset engines with issues are labeled with 1\n",
  "# the ones without issues with -1, we transform these into 0. \n",
  "y_train[y_train == -1] = 0 \n",
  "y_test[y_test == -1] = 0"
 ]
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  "id": "Ew5bxEgrI8vm"
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 "source": [
  "#### Goal\n",
  "> detect whether an engine has an issue, analysing its noise time series. \n",
  "\n",
  "For the sake of simplicity, let’s skip a lot of data cleaning/feature engineering steps one could apply to this dataset. \n",
  "\n",
  "#### Exercise\n",
  "\n",
  "Make a simple analysis on this dataset. Try to decompose the time series and see whether there is a recurrent behaviour in anomalous engines.\n",
  "\n",
  "## Build the model\n",
  "\n",
  "We are ready to build the model based on the attention mechanism.\n",
  "\n",
  "Our model processes a tensor of shape `(batch size, sequence_length, features)`, where `sequence_length` is the number of time steps and features is each input time series.\n",
  "\n",
  "We are going to build an Attention Layer, _i.e._ a tensorflow layer object. This is done for the sake of compatibility.\n",
  "Indeed, in the models we built previously, you can replace your classification RNN layers with this one: the inputs are fully compatible!"
 ]
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 "execution_count": 3,
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  "id": "fWXdUlM7I8vm"
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 "source": [
  "def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0.2):\n",
  "    # Normalization and Attention\n",
  "    x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(inputs)\n",
  "    x = tf.keras.layers.MultiHeadAttention(\n",
  "        key_dim=head_size, num_heads=num_heads, dropout=dropout\n",
  "    )(x, x)\n",
  "    x = tf.keras.layers.Dropout(dropout)(x)\n",
  "    res = x + inputs\n",
  "\n",
  "    # Feed Forward Part\n",
  "    x = tf.keras.layers.LayerNormalization(epsilon=1e-6)(res)\n",
  "    x = tf.keras.layers.Conv1D(filters=ff_dim, kernel_size=1, activation=\"relu\")(x)\n",
  "    x = tf.keras.layers.Dropout(dropout)(x)\n",
  "    x = tf.keras.layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)\n",
  "    return x + res"
 ]
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 "source": [
  "The main part of our model is now complete. \n",
  "\n",
  "We can stack multiple of those `transformer_encode`r blocks and we can also proceed to add the final _Multi-Layer Perceptron_ classification head. \n",
  "Apart from a stack of Dense layers, we need to reduce the output tensor of the `TransformerEncoder` part of our model down to a vector of features for each data point in the current batch. A common way to achieve this is to use a pooling layer. For this example, a `GlobalAveragePooling1D` layer is sufficient.\n",
  "\n",
  "**Note**: here we use only an attention model encoder, a fully transformer architecture would require also a decoder part. We leave the implementation of this as an exercise.\n",
  "If you feel lost, try to have a look at [this](https://www.tensorflow.org/text/tutorials/transformer) Tensorflow tutorial."
 ]
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 "execution_count": 4,
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  "id": "baecWUqhI8vn"
 },
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 "source": [
  "def build_model(input_shape,\n",
  "                head_size, \n",
  "                num_heads, \n",
  "                ff_dim, \n",
  "                num_transformer_blocks, \n",
  "                mlp_units, \n",
  "                dropout=0, \n",
  "                mlp_dropout=0.2):\n",
  "    inputs = tf.keras.Input(shape=input_shape)\n",
  "    x = inputs\n",
  "    \n",
  "    for _ in range(num_transformer_blocks):\n",
  "        x = transformer_encoder(x, head_size, num_heads, ff_dim, dropout)\n",
  "\n",
  "    x = tf.keras.layers.GlobalAveragePooling1D(data_format=\"channels_first\")(x)\n",
  "    \n",
  "    for dim in mlp_units:\n",
  "        x = tf.keras.layers.Dense(dim, activation=\"relu\")(x)\n",
  "        x = tf.keras.layers.Dropout(mlp_dropout)(x)\n",
  "    outputs = tf.keras.layers.Dense(n_classes, activation=\"softmax\")(x)\n",
  "    \n",
  "    return tf.keras.Model(inputs, outputs, name=\"TimeSeriesTransformer\")"
 ]
},
{
 "cell_type": "markdown",
 "metadata": {
  "id": "_X78iOFEI8vo"
 },
 "source": [
  "### Train and Evaluate\n",
  "\n",
  "We can now go on the juicy part: training.\n",
  "\n",
  "First, we build and compile the model to have a check on dimensions."
 ]
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    "Model: \"TimeSeriesTransformer\"\n",
    "__________________________________________________________________________________________________\n",
    " Layer (type)                   Output Shape         Param #     Connected to                     \n",
    "==================================================================================================\n",
    " input_1 (InputLayer)           [(None, 500, 1)]     0           []                               \n",
    "                                                                                                  \n",
    " layer_normalization (LayerNorm  (None, 500, 1)      2           ['input_1[0][0]']                \n",
    " alization)                                                                                       \n",
    "                                                                                                  \n",
    " multi_head_attention (MultiHea  (None, 500, 1)      7169        ['layer_normalization[0][0]',    \n",
    " dAttention)                                                      'layer_normalization[0][0]']    \n",
    "                                                                                                  \n",
    " dropout (Dropout)              (None, 500, 1)       0           ['multi_head_attention[0][0]']   \n",
    "                                                                                                  \n",
    " tf.__operators__.add (TFOpLamb  (None, 500, 1)      0           ['dropout[0][0]',                \n",
    " da)                                                              'input_1[0][0]']                \n",
    "                                                                                                  \n",
    " layer_normalization_1 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add[0][0]']   \n",
    " rmalization)                                                                                     \n",
    "                                                                                                  \n",
    " conv1d (Conv1D)                (None, 500, 4)       8           ['layer_normalization_1[0][0]']  \n",
    "                                                                                                  \n",
    " dropout_1 (Dropout)            (None, 500, 4)       0           ['conv1d[0][0]']                 \n",
    "                                                                                                  \n",
    " conv1d_1 (Conv1D)              (None, 500, 1)       5           ['dropout_1[0][0]']              \n",
    "                                                                                                  \n",
    " tf.__operators__.add_1 (TFOpLa  (None, 500, 1)      0           ['conv1d_1[0][0]',               \n",
    " mbda)                                                            'tf.__operators__.add[0][0]']   \n",
    "                                                                                                  \n",
    " layer_normalization_2 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_1[0][0]'] \n",
    " rmalization)                                                                                     \n",
    "                                                                                                  \n",
    " multi_head_attention_1 (MultiH  (None, 500, 1)      7169        ['layer_normalization_2[0][0]',  \n",
    " eadAttention)                                                    'layer_normalization_2[0][0]']  \n",
    "                                                                                                  \n",
    " dropout_2 (Dropout)            (None, 500, 1)       0           ['multi_head_attention_1[0][0]'] \n",
    "                                                                                                  \n",
    " tf.__operators__.add_2 (TFOpLa  (None, 500, 1)      0           ['dropout_2[0][0]',              \n",
    " mbda)                                                            'tf.__operators__.add_1[0][0]'] \n",
    "                                                                                                  \n",
    " layer_normalization_3 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_2[0][0]'] \n",
    " rmalization)                                                                                     \n",
    "                                                                                                  \n",
    " conv1d_2 (Conv1D)              (None, 500, 4)       8           ['layer_normalization_3[0][0]']  \n",
    "                                                                                                  \n",
    " dropout_3 (Dropout)            (None, 500, 4)       0           ['conv1d_2[0][0]']               \n",
    "                                                                                                  \n",
    " conv1d_3 (Conv1D)              (None, 500, 1)       5           ['dropout_3[0][0]']              \n",
    "                                                                                                  \n",
    " tf.__operators__.add_3 (TFOpLa  (None, 500, 1)      0           ['conv1d_3[0][0]',               \n",
    " mbda)                                                            'tf.__operators__.add_2[0][0]'] \n",
    "                                                                                                  \n",
    " layer_normalization_4 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_3[0][0]'] \n",
    " rmalization)                                                                                     \n",
    "                                                                                                  \n",
    " multi_head_attention_2 (MultiH  (None, 500, 1)      7169        ['layer_normalization_4[0][0]',  \n",
    " eadAttention)                                                    'layer_normalization_4[0][0]']  \n",
    "                                                                                                  \n",
    " dropout_4 (Dropout)            (None, 500, 1)       0           ['multi_head_attention_2[0][0]'] \n",
    "                                                                                                  \n",
    " tf.__operators__.add_4 (TFOpLa  (None, 500, 1)      0           ['dropout_4[0][0]',              \n",
    " mbda)                                                            'tf.__operators__.add_3[0][0]'] \n",
    "                                                                                                  \n",
    " layer_normalization_5 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_4[0][0]'] \n",
    " rmalization)                                                                                     \n",
    "                                                                                                  \n",
    " conv1d_4 (Conv1D)              (None, 500, 4)       8           ['layer_normalization_5[0][0]']  \n",
    "                                                                                                  \n",
    " dropout_5 (Dropout)            (None, 500, 4)       0           ['conv1d_4[0][0]']               \n",
    "                                                                                                  \n",
    " conv1d_5 (Conv1D)              (None, 500, 1)       5           ['dropout_5[0][0]']              \n",
    "                                                                                                  \n",
    " tf.__operators__.add_5 (TFOpLa  (None, 500, 1)      0           ['conv1d_5[0][0]',               \n",
    " mbda)                                                            'tf.__operators__.add_4[0][0]'] \n",
    "                                                                                                  \n",
    " layer_normalization_6 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_5[0][0]'] \n",
    " rmalization)                                                                                     \n",
    "                                                                                                  \n",
    " multi_head_attention_3 (MultiH  (None, 500, 1)      7169        ['layer_normalization_6[0][0]',  \n",
    " eadAttention)                                                    'layer_normalization_6[0][0]']  \n",
    "                                                                                                  \n",
    " dropout_6 (Dropout)            (None, 500, 1)       0           ['multi_head_attention_3[0][0]'] \n",
    "                                                                                                  \n",
    " tf.__operators__.add_6 (TFOpLa  (None, 500, 1)      0           ['dropout_6[0][0]',              \n",
    " mbda)                                                            'tf.__operators__.add_5[0][0]'] \n",
    "                                                                                                  \n",
    " layer_normalization_7 (LayerNo  (None, 500, 1)      2           ['tf.__operators__.add_6[0][0]'] \n",
    " rmalization)                                                                                     \n",
    "                                                                                                  \n",
    " conv1d_6 (Conv1D)              (None, 500, 4)       8           ['layer_normalization_7[0][0]']  \n",
    "                                                                                                  \n",
    " dropout_7 (Dropout)            (None, 500, 4)       0           ['conv1d_6[0][0]']               \n",
    "                                                                                                  \n",
    " conv1d_7 (Conv1D)              (None, 500, 1)       5           ['dropout_7[0][0]']              \n",
    "                                                                                                  \n",
    " tf.__operators__.add_7 (TFOpLa  (None, 500, 1)      0           ['conv1d_7[0][0]',               \n",
    " mbda)                                                            'tf.__operators__.add_6[0][0]'] \n",
    "                                                                                                  \n",
    " global_average_pooling1d (Glob  (None, 500)         0           ['tf.__operators__.add_7[0][0]'] \n",
    " alAveragePooling1D)                                                                              \n",
    "                                                                                                  \n",
    " dense (Dense)                  (None, 128)          64128       ['global_average_pooling1d[0][0]'\n",
    "                                                                 ]                                \n",
    "                                                                                                  \n",
    " dropout_8 (Dropout)            (None, 128)          0           ['dense[0][0]']                  \n",
    "                                                                                                  \n",
    " dense_1 (Dense)                (None, 2)            258         ['dropout_8[0][0]']              \n",
    "                                                                                                  \n",
    "==================================================================================================\n",
    "Total params: 93,130\n",
    "Trainable params: 93,130\n",
    "Non-trainable params: 0\n",
    "__________________________________________________________________________________________________\n"
   ]
  }
 ],
 "source": [
  "input_shape = x_train.shape[1:]\n",
  "\n",
  "model = build_model(\n",
  "    input_shape,\n",
  "    head_size=256,\n",
  "    num_heads=4,\n",
  "    ff_dim=4,\n",
  "    num_transformer_blocks=4,\n",
  "    mlp_units=[128],\n",
  "    mlp_dropout=0.4,\n",
  "    dropout=0.25,\n",
  ")\n",
  "\n",
  "model.compile(\n",
  "    loss=\"sparse_categorical_crossentropy\",\n",
  "    optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
  "    metrics=[\"sparse_categorical_accuracy\"],\n",
  ")\n",
  "\n",
  "model.summary()"
 ]
},
{
 "cell_type": "markdown",
 "metadata": {
  "id": "z5gh9MAMI8vp"
 },
 "source": [
  "#### Some comments\n",
  "\n",
  "As you can see, we have ~$93$k parameters to train. The input shape is given by the whole length of the input time series. We can imagine this to be the length of the time window in the case of a series forecast.\n",
  "\n",
  "Let's move on and train such a model.\n",
  "\n",
  "We want to give an earlystopping callback, in fact it is expected that this model can easily overfit otherwise.\n",
  "\n",
  "We also want to save model weights during training. To do so we also define a callback."
 ]
},
{
 "cell_type": "code",
 "execution_count": 9,
 "metadata": {
  "id": "jfM01i8nI8vp"
 },
 "outputs": [],
 "source": [
  "es_callback = tf.keras.callbacks.EarlyStopping(patience=10, \n",
  "                                            monitor='val_sparse_categorical_accuracy', \n",
  "                                            mode='max', \n",
  "                                            verbose=1,  \n",
  "                                            restore_best_weights=True)\n",
  "\n",
  "checkpoint_path = \"model/cp.ckpt\"\n",
  "checkpoint_dir = os.path.dirname(checkpoint_path)\n",
  "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
  "                                                 save_weights_only=True,\n",
  "                                                 save_best_only=True,\n",
  "                                                 verbose=1)"
 ]
},
{
 "cell_type": "code",
 "execution_count": 11,
 "metadata": {
  "colab": {
   "base_uri": "https://localhost:8080/"
  },
  "id": "Asi1d9gEI8vp",
  "outputId": "2e417d09-f7ca-4bde-f8da-e94d2b64f395"
 },
 "outputs": [
  {
   "name": "stdout",
   "output_type": "stream",
   "text": [
    "Epoch 1/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.6472 - sparse_categorical_accuracy: 0.6799\n",
    "Epoch 1: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 487ms/step - loss: 0.6472 - sparse_categorical_accuracy: 0.6799 - val_loss: 0.5862 - val_sparse_categorical_accuracy: 0.6713\n",
    "Epoch 2/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.6338 - sparse_categorical_accuracy: 0.6684\n",
    "Epoch 2: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 490ms/step - loss: 0.6338 - sparse_categorical_accuracy: 0.6684 - val_loss: 0.5759 - val_sparse_categorical_accuracy: 0.6796\n",
    "Epoch 3/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.5976 - sparse_categorical_accuracy: 0.6934\n",
    "Epoch 3: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 489ms/step - loss: 0.5976 - sparse_categorical_accuracy: 0.6934 - val_loss: 0.5650 - val_sparse_categorical_accuracy: 0.6907\n",
    "Epoch 4/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.5917 - sparse_categorical_accuracy: 0.6938\n",
    "Epoch 4: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 489ms/step - loss: 0.5917 - sparse_categorical_accuracy: 0.6938 - val_loss: 0.5578 - val_sparse_categorical_accuracy: 0.6852\n",
    "Epoch 5/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.5651 - sparse_categorical_accuracy: 0.7108\n",
    "Epoch 5: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 492ms/step - loss: 0.5651 - sparse_categorical_accuracy: 0.7108 - val_loss: 0.5509 - val_sparse_categorical_accuracy: 0.6990\n",
    "Epoch 6/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.5543 - sparse_categorical_accuracy: 0.7226\n",
    "Epoch 6: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 495ms/step - loss: 0.5543 - sparse_categorical_accuracy: 0.7226 - val_loss: 0.5438 - val_sparse_categorical_accuracy: 0.7046\n",
    "Epoch 7/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.5410 - sparse_categorical_accuracy: 0.7306\n",
    "Epoch 7: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 494ms/step - loss: 0.5410 - sparse_categorical_accuracy: 0.7306 - val_loss: 0.5363 - val_sparse_categorical_accuracy: 0.7115\n",
    "Epoch 8/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.5138 - sparse_categorical_accuracy: 0.7441\n",
    "Epoch 8: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 498ms/step - loss: 0.5138 - sparse_categorical_accuracy: 0.7441 - val_loss: 0.5329 - val_sparse_categorical_accuracy: 0.7115\n",
    "Epoch 9/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.5132 - sparse_categorical_accuracy: 0.7517\n",
    "Epoch 9: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.5132 - sparse_categorical_accuracy: 0.7517 - val_loss: 0.5274 - val_sparse_categorical_accuracy: 0.7143\n",
    "Epoch 10/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.5013 - sparse_categorical_accuracy: 0.7556\n",
    "Epoch 10: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 501ms/step - loss: 0.5013 - sparse_categorical_accuracy: 0.7556 - val_loss: 0.5210 - val_sparse_categorical_accuracy: 0.7226\n",
    "Epoch 11/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4938 - sparse_categorical_accuracy: 0.7649\n",
    "Epoch 11: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 501ms/step - loss: 0.4938 - sparse_categorical_accuracy: 0.7649 - val_loss: 0.5154 - val_sparse_categorical_accuracy: 0.7309\n",
    "Epoch 12/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4837 - sparse_categorical_accuracy: 0.7549\n",
    "Epoch 12: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.4837 - sparse_categorical_accuracy: 0.7549 - val_loss: 0.5100 - val_sparse_categorical_accuracy: 0.7323\n",
    "Epoch 13/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4701 - sparse_categorical_accuracy: 0.7764\n",
    "Epoch 13: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 505ms/step - loss: 0.4701 - sparse_categorical_accuracy: 0.7764 - val_loss: 0.5064 - val_sparse_categorical_accuracy: 0.7365\n",
    "Epoch 14/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4626 - sparse_categorical_accuracy: 0.7785\n",
    "Epoch 14: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.4626 - sparse_categorical_accuracy: 0.7785 - val_loss: 0.5011 - val_sparse_categorical_accuracy: 0.7420\n",
    "Epoch 15/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4559 - sparse_categorical_accuracy: 0.7868\n",
    "Epoch 15: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 505ms/step - loss: 0.4559 - sparse_categorical_accuracy: 0.7868 - val_loss: 0.4966 - val_sparse_categorical_accuracy: 0.7462\n",
    "Epoch 16/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4537 - sparse_categorical_accuracy: 0.7872\n",
    "Epoch 16: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 505ms/step - loss: 0.4537 - sparse_categorical_accuracy: 0.7872 - val_loss: 0.4934 - val_sparse_categorical_accuracy: 0.7517\n",
    "Epoch 17/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4336 - sparse_categorical_accuracy: 0.8028\n",
    "Epoch 17: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 508ms/step - loss: 0.4336 - sparse_categorical_accuracy: 0.8028 - val_loss: 0.4893 - val_sparse_categorical_accuracy: 0.7490\n",
    "Epoch 18/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4235 - sparse_categorical_accuracy: 0.8069\n",
    "Epoch 18: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.4235 - sparse_categorical_accuracy: 0.8069 - val_loss: 0.4871 - val_sparse_categorical_accuracy: 0.7559\n",
    "Epoch 19/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4175 - sparse_categorical_accuracy: 0.8139\n",
    "Epoch 19: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.4175 - sparse_categorical_accuracy: 0.8139 - val_loss: 0.4804 - val_sparse_categorical_accuracy: 0.7573\n",
    "Epoch 20/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4171 - sparse_categorical_accuracy: 0.8191\n",
    "Epoch 20: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 500ms/step - loss: 0.4171 - sparse_categorical_accuracy: 0.8191 - val_loss: 0.4775 - val_sparse_categorical_accuracy: 0.7531\n",
    "Epoch 21/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3984 - sparse_categorical_accuracy: 0.8205\n",
    "Epoch 21: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.3984 - sparse_categorical_accuracy: 0.8205 - val_loss: 0.4744 - val_sparse_categorical_accuracy: 0.7559\n",
    "Epoch 22/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4035 - sparse_categorical_accuracy: 0.8278\n",
    "Epoch 22: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 505ms/step - loss: 0.4035 - sparse_categorical_accuracy: 0.8278 - val_loss: 0.4726 - val_sparse_categorical_accuracy: 0.7614\n",
    "Epoch 23/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.4037 - sparse_categorical_accuracy: 0.8215\n",
    "Epoch 23: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.4037 - sparse_categorical_accuracy: 0.8215 - val_loss: 0.4680 - val_sparse_categorical_accuracy: 0.7684\n",
    "Epoch 24/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3821 - sparse_categorical_accuracy: 0.8378\n",
    "Epoch 24: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.3821 - sparse_categorical_accuracy: 0.8378 - val_loss: 0.4637 - val_sparse_categorical_accuracy: 0.7753\n",
    "Epoch 25/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3734 - sparse_categorical_accuracy: 0.8420\n",
    "Epoch 25: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 505ms/step - loss: 0.3734 - sparse_categorical_accuracy: 0.8420 - val_loss: 0.4609 - val_sparse_categorical_accuracy: 0.7712\n",
    "Epoch 26/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3825 - sparse_categorical_accuracy: 0.8358\n",
    "Epoch 26: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.3825 - sparse_categorical_accuracy: 0.8358 - val_loss: 0.4598 - val_sparse_categorical_accuracy: 0.7712\n",
    "Epoch 27/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3664 - sparse_categorical_accuracy: 0.8413\n",
    "Epoch 27: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.3664 - sparse_categorical_accuracy: 0.8413 - val_loss: 0.4579 - val_sparse_categorical_accuracy: 0.7739\n",
    "Epoch 28/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3679 - sparse_categorical_accuracy: 0.8438\n",
    "Epoch 28: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.3679 - sparse_categorical_accuracy: 0.8438 - val_loss: 0.4545 - val_sparse_categorical_accuracy: 0.7781\n",
    "Epoch 29/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3564 - sparse_categorical_accuracy: 0.8538\n",
    "Epoch 29: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.3564 - sparse_categorical_accuracy: 0.8538 - val_loss: 0.4512 - val_sparse_categorical_accuracy: 0.7753\n",
    "Epoch 30/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3580 - sparse_categorical_accuracy: 0.8458\n",
    "Epoch 30: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 505ms/step - loss: 0.3580 - sparse_categorical_accuracy: 0.8458 - val_loss: 0.4483 - val_sparse_categorical_accuracy: 0.7809\n",
    "Epoch 31/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3500 - sparse_categorical_accuracy: 0.8545\n",
    "Epoch 31: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.3500 - sparse_categorical_accuracy: 0.8545 - val_loss: 0.4442 - val_sparse_categorical_accuracy: 0.7864\n",
    "Epoch 32/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3493 - sparse_categorical_accuracy: 0.8528\n",
    "Epoch 32: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.3493 - sparse_categorical_accuracy: 0.8528 - val_loss: 0.4419 - val_sparse_categorical_accuracy: 0.7809\n",
    "Epoch 33/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3450 - sparse_categorical_accuracy: 0.8660\n",
    "Epoch 33: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.3450 - sparse_categorical_accuracy: 0.8660 - val_loss: 0.4384 - val_sparse_categorical_accuracy: 0.7906\n",
    "Epoch 34/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3380 - sparse_categorical_accuracy: 0.8663\n",
    "Epoch 34: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.3380 - sparse_categorical_accuracy: 0.8663 - val_loss: 0.4350 - val_sparse_categorical_accuracy: 0.7933\n",
    "Epoch 35/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3201 - sparse_categorical_accuracy: 0.8698\n",
    "Epoch 35: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.3201 - sparse_categorical_accuracy: 0.8698 - val_loss: 0.4317 - val_sparse_categorical_accuracy: 0.7961\n",
    "Epoch 36/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3274 - sparse_categorical_accuracy: 0.8670\n",
    "Epoch 36: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.3274 - sparse_categorical_accuracy: 0.8670 - val_loss: 0.4312 - val_sparse_categorical_accuracy: 0.8058\n",
    "Epoch 37/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3235 - sparse_categorical_accuracy: 0.8750\n",
    "Epoch 37: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.3235 - sparse_categorical_accuracy: 0.8750 - val_loss: 0.4282 - val_sparse_categorical_accuracy: 0.8058\n",
    "Epoch 38/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3137 - sparse_categorical_accuracy: 0.8771\n",
    "Epoch 38: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.3137 - sparse_categorical_accuracy: 0.8771 - val_loss: 0.4261 - val_sparse_categorical_accuracy: 0.8100\n",
    "Epoch 39/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3089 - sparse_categorical_accuracy: 0.8851\n",
    "Epoch 39: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.3089 - sparse_categorical_accuracy: 0.8851 - val_loss: 0.4253 - val_sparse_categorical_accuracy: 0.8003\n",
    "Epoch 40/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3110 - sparse_categorical_accuracy: 0.8764\n",
    "Epoch 40: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.3110 - sparse_categorical_accuracy: 0.8764 - val_loss: 0.4230 - val_sparse_categorical_accuracy: 0.8100\n",
    "Epoch 41/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3093 - sparse_categorical_accuracy: 0.8740\n",
    "Epoch 41: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.3093 - sparse_categorical_accuracy: 0.8740 - val_loss: 0.4189 - val_sparse_categorical_accuracy: 0.8100\n",
    "Epoch 42/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2977 - sparse_categorical_accuracy: 0.8868\n",
    "Epoch 42: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 500ms/step - loss: 0.2977 - sparse_categorical_accuracy: 0.8868 - val_loss: 0.4180 - val_sparse_categorical_accuracy: 0.8072\n",
    "Epoch 43/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2979 - sparse_categorical_accuracy: 0.8865\n",
    "Epoch 43: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 505ms/step - loss: 0.2979 - sparse_categorical_accuracy: 0.8865 - val_loss: 0.4167 - val_sparse_categorical_accuracy: 0.8072\n",
    "Epoch 44/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.3042 - sparse_categorical_accuracy: 0.8858\n",
    "Epoch 44: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.3042 - sparse_categorical_accuracy: 0.8858 - val_loss: 0.4129 - val_sparse_categorical_accuracy: 0.8128\n",
    "Epoch 45/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2918 - sparse_categorical_accuracy: 0.8878\n",
    "Epoch 45: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.2918 - sparse_categorical_accuracy: 0.8878 - val_loss: 0.4125 - val_sparse_categorical_accuracy: 0.8072\n",
    "Epoch 46/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2902 - sparse_categorical_accuracy: 0.8830\n",
    "Epoch 46: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.2902 - sparse_categorical_accuracy: 0.8830 - val_loss: 0.4107 - val_sparse_categorical_accuracy: 0.8128\n",
    "Epoch 47/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2796 - sparse_categorical_accuracy: 0.9014\n",
    "Epoch 47: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 505ms/step - loss: 0.2796 - sparse_categorical_accuracy: 0.9014 - val_loss: 0.4093 - val_sparse_categorical_accuracy: 0.8086\n",
    "Epoch 48/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2829 - sparse_categorical_accuracy: 0.8944\n",
    "Epoch 48: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 505ms/step - loss: 0.2829 - sparse_categorical_accuracy: 0.8944 - val_loss: 0.4057 - val_sparse_categorical_accuracy: 0.8141\n",
    "Epoch 49/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2783 - sparse_categorical_accuracy: 0.8955\n",
    "Epoch 49: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.2783 - sparse_categorical_accuracy: 0.8955 - val_loss: 0.4062 - val_sparse_categorical_accuracy: 0.8141\n",
    "Epoch 50/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2722 - sparse_categorical_accuracy: 0.9000\n",
    "Epoch 50: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.2722 - sparse_categorical_accuracy: 0.9000 - val_loss: 0.4030 - val_sparse_categorical_accuracy: 0.8155\n",
    "Epoch 51/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2647 - sparse_categorical_accuracy: 0.9003\n",
    "Epoch 51: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 506ms/step - loss: 0.2647 - sparse_categorical_accuracy: 0.9003 - val_loss: 0.4002 - val_sparse_categorical_accuracy: 0.8100\n",
    "Epoch 52/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2765 - sparse_categorical_accuracy: 0.8924\n",
    "Epoch 52: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.2765 - sparse_categorical_accuracy: 0.8924 - val_loss: 0.3992 - val_sparse_categorical_accuracy: 0.8128\n",
    "Epoch 53/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2637 - sparse_categorical_accuracy: 0.9007\n",
    "Epoch 53: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.2637 - sparse_categorical_accuracy: 0.9007 - val_loss: 0.3969 - val_sparse_categorical_accuracy: 0.8155\n",
    "Epoch 54/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2680 - sparse_categorical_accuracy: 0.8934\n",
    "Epoch 54: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.2680 - sparse_categorical_accuracy: 0.8934 - val_loss: 0.3965 - val_sparse_categorical_accuracy: 0.8141\n",
    "Epoch 55/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2611 - sparse_categorical_accuracy: 0.9007\n",
    "Epoch 55: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 523ms/step - loss: 0.2611 - sparse_categorical_accuracy: 0.9007 - val_loss: 0.3960 - val_sparse_categorical_accuracy: 0.8169\n",
    "Epoch 56/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2591 - sparse_categorical_accuracy: 0.9035\n",
    "Epoch 56: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.2591 - sparse_categorical_accuracy: 0.9035 - val_loss: 0.3933 - val_sparse_categorical_accuracy: 0.8169\n",
    "Epoch 57/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2587 - sparse_categorical_accuracy: 0.9069\n",
    "Epoch 57: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 22s 501ms/step - loss: 0.2587 - sparse_categorical_accuracy: 0.9069 - val_loss: 0.3908 - val_sparse_categorical_accuracy: 0.8155\n",
    "Epoch 58/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2544 - sparse_categorical_accuracy: 0.9042\n",
    "Epoch 58: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.2544 - sparse_categorical_accuracy: 0.9042 - val_loss: 0.3923 - val_sparse_categorical_accuracy: 0.8128\n",
    "Epoch 59/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2462 - sparse_categorical_accuracy: 0.9104\n",
    "Epoch 59: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.2462 - sparse_categorical_accuracy: 0.9104 - val_loss: 0.3906 - val_sparse_categorical_accuracy: 0.8183\n",
    "Epoch 60/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2431 - sparse_categorical_accuracy: 0.9115\n",
    "Epoch 60: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.2431 - sparse_categorical_accuracy: 0.9115 - val_loss: 0.3900 - val_sparse_categorical_accuracy: 0.8128\n",
    "Epoch 61/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2527 - sparse_categorical_accuracy: 0.9049\n",
    "Epoch 61: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.2527 - sparse_categorical_accuracy: 0.9049 - val_loss: 0.3886 - val_sparse_categorical_accuracy: 0.8155\n",
    "Epoch 62/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2456 - sparse_categorical_accuracy: 0.9122\n",
    "Epoch 62: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.2456 - sparse_categorical_accuracy: 0.9122 - val_loss: 0.3862 - val_sparse_categorical_accuracy: 0.8141\n",
    "Epoch 63/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2461 - sparse_categorical_accuracy: 0.9073\n",
    "Epoch 63: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 505ms/step - loss: 0.2461 - sparse_categorical_accuracy: 0.9073 - val_loss: 0.3832 - val_sparse_categorical_accuracy: 0.8197\n",
    "Epoch 64/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2425 - sparse_categorical_accuracy: 0.9056\n",
    "Epoch 64: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.2425 - sparse_categorical_accuracy: 0.9056 - val_loss: 0.3791 - val_sparse_categorical_accuracy: 0.8169\n",
    "Epoch 65/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2325 - sparse_categorical_accuracy: 0.9194\n",
    "Epoch 65: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 501ms/step - loss: 0.2325 - sparse_categorical_accuracy: 0.9194 - val_loss: 0.3829 - val_sparse_categorical_accuracy: 0.8197\n",
    "Epoch 66/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2346 - sparse_categorical_accuracy: 0.9160\n",
    "Epoch 66: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.2346 - sparse_categorical_accuracy: 0.9160 - val_loss: 0.3826 - val_sparse_categorical_accuracy: 0.8183\n",
    "Epoch 67/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2276 - sparse_categorical_accuracy: 0.9208\n",
    "Epoch 67: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.2276 - sparse_categorical_accuracy: 0.9208 - val_loss: 0.3838 - val_sparse_categorical_accuracy: 0.8128\n",
    "Epoch 68/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2282 - sparse_categorical_accuracy: 0.9208\n",
    "Epoch 68: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.2282 - sparse_categorical_accuracy: 0.9208 - val_loss: 0.3801 - val_sparse_categorical_accuracy: 0.8169\n",
    "Epoch 69/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2364 - sparse_categorical_accuracy: 0.9080\n",
    "Epoch 69: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.2364 - sparse_categorical_accuracy: 0.9080 - val_loss: 0.3788 - val_sparse_categorical_accuracy: 0.8183\n",
    "Epoch 70/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2243 - sparse_categorical_accuracy: 0.9226\n",
    "Epoch 70: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.2243 - sparse_categorical_accuracy: 0.9226 - val_loss: 0.3783 - val_sparse_categorical_accuracy: 0.8141\n",
    "Epoch 71/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2307 - sparse_categorical_accuracy: 0.9160\n",
    "Epoch 71: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 508ms/step - loss: 0.2307 - sparse_categorical_accuracy: 0.9160 - val_loss: 0.3778 - val_sparse_categorical_accuracy: 0.8183\n",
    "Epoch 72/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2254 - sparse_categorical_accuracy: 0.9226\n",
    "Epoch 72: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 501ms/step - loss: 0.2254 - sparse_categorical_accuracy: 0.9226 - val_loss: 0.3718 - val_sparse_categorical_accuracy: 0.8169\n",
    "Epoch 73/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2173 - sparse_categorical_accuracy: 0.9243\n",
    "Epoch 73: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.2173 - sparse_categorical_accuracy: 0.9243 - val_loss: 0.3707 - val_sparse_categorical_accuracy: 0.8211\n",
    "Epoch 74/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2157 - sparse_categorical_accuracy: 0.9302\n",
    "Epoch 74: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 503ms/step - loss: 0.2157 - sparse_categorical_accuracy: 0.9302 - val_loss: 0.3697 - val_sparse_categorical_accuracy: 0.8252\n",
    "Epoch 75/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2168 - sparse_categorical_accuracy: 0.9236\n",
    "Epoch 75: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 506ms/step - loss: 0.2168 - sparse_categorical_accuracy: 0.9236 - val_loss: 0.3695 - val_sparse_categorical_accuracy: 0.8211\n",
    "Epoch 76/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2169 - sparse_categorical_accuracy: 0.9184\n",
    "Epoch 76: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.2169 - sparse_categorical_accuracy: 0.9184 - val_loss: 0.3675 - val_sparse_categorical_accuracy: 0.8225\n",
    "Epoch 77/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2111 - sparse_categorical_accuracy: 0.9247\n",
    "Epoch 77: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.2111 - sparse_categorical_accuracy: 0.9247 - val_loss: 0.3660 - val_sparse_categorical_accuracy: 0.8169\n",
    "Epoch 78/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2189 - sparse_categorical_accuracy: 0.9205\n",
    "Epoch 78: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 502ms/step - loss: 0.2189 - sparse_categorical_accuracy: 0.9205 - val_loss: 0.3632 - val_sparse_categorical_accuracy: 0.8197\n",
    "Epoch 79/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2135 - sparse_categorical_accuracy: 0.9201\n",
    "Epoch 79: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.2135 - sparse_categorical_accuracy: 0.9201 - val_loss: 0.3614 - val_sparse_categorical_accuracy: 0.8225\n",
    "Epoch 80/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2092 - sparse_categorical_accuracy: 0.9267\n",
    "Epoch 80: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 504ms/step - loss: 0.2092 - sparse_categorical_accuracy: 0.9267 - val_loss: 0.3640 - val_sparse_categorical_accuracy: 0.8155\n",
    "Epoch 81/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2046 - sparse_categorical_accuracy: 0.9302\n",
    "Epoch 81: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 505ms/step - loss: 0.2046 - sparse_categorical_accuracy: 0.9302 - val_loss: 0.3632 - val_sparse_categorical_accuracy: 0.8141\n",
    "Epoch 82/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.1931 - sparse_categorical_accuracy: 0.9340\n",
    "Epoch 82: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 506ms/step - loss: 0.1931 - sparse_categorical_accuracy: 0.9340 - val_loss: 0.3628 - val_sparse_categorical_accuracy: 0.8169\n",
    "Epoch 83/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.2023 - sparse_categorical_accuracy: 0.9278\n",
    "Epoch 83: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 506ms/step - loss: 0.2023 - sparse_categorical_accuracy: 0.9278 - val_loss: 0.3621 - val_sparse_categorical_accuracy: 0.8197\n",
    "Epoch 84/200\n",
    "45/45 [==============================] - ETA: 0s - loss: 0.1992 - sparse_categorical_accuracy: 0.9274Restoring model weights from the end of the best epoch: 74.\n",
    "\n",
    "Epoch 84: saving model to training_1/cp.ckpt\n",
    "45/45 [==============================] - 23s 506ms/step - loss: 0.1992 - sparse_categorical_accuracy: 0.9274 - val_loss: 0.3595 - val_sparse_categorical_accuracy: 0.8183\n",
    "Epoch 84: early stopping\n"
   ]
  }
 ],
 "source": [
  "callbacks = [es_callback, cp_callback]\n",
  "\n",
  "history = model.fit(x_train,\n",
  "                    y_train,\n",
  "                    validation_split=0.2,\n",
  "                    epochs=200,\n",
  "                    batch_size=64,\n",
  "                    callbacks=callbacks);"
 ]
},
{
 "cell_type": "markdown",
 "metadata": {
  "id": "K80L53nBPkHl"
 },
 "source": [
  "### Load model"
 ]
},
{
 "cell_type": "code",
 "execution_count": 12,
 "metadata": {
  "colab": {
   "base_uri": "https://localhost:8080/"
  },
  "id": "eFPPCN7_PjOj",
  "outputId": "890af131-f2ab-42ad-a88b-bcbeb49ac721"
 },
 "outputs": [
  {
   "data": {
    "text/plain": [
     "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7fe90dbbe5d0>"
    ]
   },
   "execution_count": 12,
   "metadata": {},
   "output_type": "execute_result"
  }
 ],
 "source": [
  "model.load_weights(checkpoint_path);"
 ]
},
{
 "cell_type": "markdown",
 "metadata": {
  "id": "qE7vBocEI8vq"
 },
 "source": [
  "### Learning curves\n",
  "Let's procede to analyse learning curves of our model."
 ]
},
{
 "cell_type": "code",
 "execution_count": 15,
 "metadata": {
  "colab": {
   "base_uri": "https://localhost:8080/",
   "height": 1000
  },
  "id": "S2rq5z1NI8vq",
  "outputId": "5fa33274-e616-4eb4-853f-da107f2913cc"
 },
 "outputs": [
  {
   "name": "stdout",
   "output_type": "stream",
   "text": [
    "dict_keys(['loss', 'sparse_categorical_accuracy', 'val_loss', 'val_sparse_categorical_accuracy'])\n"
   ]
  },
  {
   "data": {
    "image/png": 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    "text/plain": [
     "<Figure size 1080x720 with 1 Axes>"
    ]
   },
   "metadata": {
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\n",
    "text/plain": [
     "<Figure size 1080x720 with 1 Axes>"
    ]
   },
   "metadata": {
    "image/png": {
     "height": 608,
     "width": 894
    }
   },
   "output_type": "display_data"
  }
 ],
 "source": [
  "print(history.history.keys())\n",
  "\n",
  "plt.figure(figsize=(15,10))\n",
  "\n",
  "# summarize history for accuracy\n",
  "plt.plot(history.history['sparse_categorical_accuracy'])\n",
  "plt.plot(history.history['val_sparse_categorical_accuracy'])\n",
  "plt.title('model accuracy')\n",
  "plt.ylabel('accuracy')\n",
  "plt.xlabel('epoch')\n",
  "plt.legend(['train', 'test'], loc='upper left')\n",
  "\n",
  "plt.figure(figsize=(15,10))\n",
  "# summarize history for loss\n",
  "plt.plot(history.history['loss'])\n",
  "plt.plot(history.history['val_loss'])\n",
  "plt.title('model loss')\n",
  "plt.ylabel('loss')\n",
  "plt.xlabel('epoch')\n",
  "plt.legend(['train', 'test'], loc='upper left');"
 ]
},
{
 "cell_type": "markdown",
 "metadata": {},
 "source": [
  "### Comments on the plots\n",
  "\n",
  "We can clearly state the model is slightly overfitting. This suggests to take some actions in order to reduce such issue.\n",
  "One can simplify the model, increase the dropout rates, regularise a bit more, etc.\n",
  "\n",
  "**Exercise**: Perform such optimisation in order to avoid overfitting."
 ]
},
{
 "cell_type": "markdown",
 "metadata": {
  "id": "6SSzn2l-I8vq"
 },
 "source": [
  "### Model evaluation"
 ]
},
{
 "cell_type": "code",
 "execution_count": 16,
 "metadata": {
  "colab": {
   "base_uri": "https://localhost:8080/"
  },
  "id": "l9xb1GBeAGhv",
  "outputId": "42c39619-336d-4235-aaf5-6b7d5948ac3e"
 },
 "outputs": [
  {
   "name": "stdout",
   "output_type": "stream",
   "text": [
    "42/42 [==============================] - 3s 75ms/step - loss: 0.3805 - sparse_categorical_accuracy: 0.8341\n"
   ]
  },
  {
   "data": {
    "text/plain": [
     "[0.38051778078079224, 0.8340908885002136]"
    ]
   },
   "execution_count": 16,
   "metadata": {},
   "output_type": "execute_result"
  }
 ],
 "source": [
  "model.evaluate(x_test, y_test, verbose=1)"
 ]
},
{
 "cell_type": "markdown",
 "metadata": {
  "id": "QNntiSqnI8vr"
 },
 "source": [
  "## Some observations\n",
  "\n",
  "In about $110$-$120$ epochs (~$25$s each on Colab with enabled GPU), the model reaches a training accuracy of ~$0.95$, validation accuracy of ~$0.84$ and a testing accuracy of ~$0.85$, without hyperparameter tuning. And that is for a model with less than $100$k parameters. Of course, parameter count and accuracy could be improved by a hyperparameter search and a more sophisticated learning rate schedule, or a different optimizer.\n",
  "\n",
  "---\n",
  "\n",
  "## Exercises \n",
  "\n",
  "1. Perform hyperparameter finetuning (you can use  a tool like the holy spirit, grid search or a framework like [optuna](https://optuna.org) if you like).\n",
  "\n",
  "2. Implement the same model using an LSTM based architecture and compare results.\n",
  "\n",
  "3. Apply the transformer architecture to a time series forecast problem\n",
  "\n",
  "---\n",
  "\n",
  "# Conclusion\n",
  "\n",
  "The final model shows very good results on the data-set, this would outperform an implementation based on LSTM, which would suffer from short-term memory. Moreover, the architecture allows for much more rapid training, as the computations during training are done concurrently rather than sequentially. \n",
  "\n",
  "It can be concluded that the Transformer architecture, which is traditionally applied to NLP problems, has large potential in time series forecasting.\n",
  "\n",
  "## Other libraries to look at\n",
  "\n",
  "1. [flow-forecast](https://github.com/AIStream-Peelout/flow-forecast)\n",
  "2. [darts](https://unit8co.github.io/darts/)\n",
  "3. [neural-prophet](https://neuralprophet.com/)"
 ]
}
],
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      "name": "09.TimeSeries-TransformersDarts.ipynb",
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