TF_Forum_17061.ipynb

tf_forum_17061.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPO5D4+IiGfOfbQiGiAA0Yp",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/kiransair/acb664912071933630d87bfaa3ccf159/tf_forum_17061.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1p9VimQ1ljYw",
        "outputId": "534f3630-3f85-4deb-dccb-76f7160c2ddd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.6931 - accuracy: 0.5000\n",
            "Epoch 2/10\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "Epoch 3/10\n",
            "1/1 [==============================] - 0s 20ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "Epoch 4/10\n",
            "1/1 [==============================] - 0s 23ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "Epoch 5/10\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "Epoch 6/10\n",
            "1/1 [==============================] - 0s 23ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "Epoch 7/10\n",
            "1/1 [==============================] - 0s 24ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "Epoch 8/10\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "Epoch 9/10\n",
            "1/1 [==============================] - 0s 24ms/step - loss: 0.6931 - accuracy: 0.5000\n",
            "Epoch 10/10\n",
            "1/1 [==============================] - 0s 21ms/step - loss: 0.6931 - accuracy: 0.5000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f75b1cd6950>"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "# Create the model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Dense(3, activation='relu', input_shape=(None, 9)),  # Adjusted input shape\n",
        "    tf.keras.layers.Dense(2, activation='softmax')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "data = np.array([[[2, 3, 5], [6, 8, 9], [10, 11, 12]], [[2, 1, 3], [3, 5, 6], [4, 5, 2]]])\n",
        "labels = np.array([0, 1])  # One label for each group of arrays\n",
        "\n",
        "# Reshape the data to combine all groups into one sample\n",
        "data = np.reshape(data, (2, -1))\n",
        "\n",
        "model.fit(data, labels, epochs=10)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "to_predict = np.array([[2,14,3],[3,56,6]])\n",
        "x=np.pad(to_predict,((0, 0), (0, 6)), 'constant', constant_values=0)\n",
        "predictions = model.predict(x)\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCoS1t0JlrTh",
        "outputId": "0fdb666b-8b18-41d8-9288-661819decfdf"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 404ms/step\n",
            "[[0.5        0.5       ]\n",
            " [0.49962947 0.50037056]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bicOUQ9eltrd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}