Python_Learning_Day_4_Sankey_Flow_Tokenization_Trial.ipynb

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          },
          "metadata": {}
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      ],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Percobaan untuk membuat posisi encoding\n",
        "def get_positional_encoding(max_seq_len, d_model):\n",
        "    pos = np.arange(max_seq_len)[:, np.newaxis]\n",
        "    dim = np.arange(d_model)[np.newaxis, :]\n",
        "    angle = pos / np.power(10000, (2 * (dim // 2)) / d_model)\n",
        "    angle[:, 0::2] = np.sin(angle[:, 0::2])\n",
        "    angle[:, 1::2] = np.cos(angle[:, 1::2])\n",
        "    return angle\n",
        "\n",
        "# Implementasi Self-Attention dan cara kerjanya\n",
        "class SelfAttention(tf.keras.layers.Layer):\n",
        "    def __init__(self, d_model):\n",
        "        super(SelfAttention, self).__init__()\n",
        "        self.d_model = d_model\n",
        "        self.query = tf.keras.layers.Dense(d_model)\n",
        "        self.key = tf.keras.layers.Dense(d_model)\n",
        "        self.value = tf.keras.layers.Dense(d_model)\n",
        "\n",
        "    def call(self, x):\n",
        "        Q = self.query(x)\n",
        "        K = self.key(x)\n",
        "        V = self.value(x)\n",
        "        attention_scores = tf.matmul(Q, K, transpose_b=True) / tf.sqrt(tf.cast(self.d_model, tf.float32))\n",
        "        attention_weights = tf.nn.softmax(attention_scores, axis=-1)\n",
        "        return tf.matmul(attention_weights, V), attention_weights\n",
        "\n",
        "# Contoh penggunaan dan aplikasinya\n",
        "max_seq_len = 5\n",
        "d_model = 64\n",
        "positional_encoding = get_positional_encoding(max_seq_len, d_model)\n",
        "x = tf.constant(positional_encoding, dtype=tf.float32)\n",
        "\n",
        "# Membuat layer self-attention\n",
        "attention_layer = SelfAttention(d_model)\n",
        "output, attention_weights = attention_layer(x)\n",
        "\n",
        "# Visualisasi dengan model Sankey Diagram\n",
        "fig = go.Figure()\n",
        "source = []\n",
        "target = []\n",
        "value = []\n",
        "\n",
        "for i in range(max_seq_len):\n",
        "    for j in range(max_seq_len):\n",
        "        source.append(i)\n",
        "        target.append(j)\n",
        "        value.append(attention_weights[i, j].numpy())\n",
        "\n",
        "fig.add_trace(go.Sankey(\n",
        "    node=dict(\n",
        "        pad=15,\n",
        "        thickness=20,\n",
        "        line=dict(color=\"black\", width=0.5),\n",
        "        label=[f\"Token {i}\" for i in range(max_seq_len)]\n",
        "    ),\n",
        "    link=dict(\n",
        "        source=source,\n",
        "        target=target,\n",
        "        value=value\n",
        "    )\n",
        "))\n",
        "\n",
        "fig.update_layout(title_text=\"Sankey Diagram of Attention Weights\", font_size=12)\n",
        "fig.show()"
      ]
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