running_2000_epoch_model_with_random_noise.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "running 2000 epoch model with noise.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVYkdiKCWzgb"
      },
      "source": [
        "from keras.models import load_model\n",
        "from numpy import asarray\n",
        "from matplotlib import pyplot\n",
        "from numpy.random import randn"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j67lTUWbYMDE",
        "outputId": "e6505bfc-8df6-4161-93aa-db95f30564e8"
      },
      "source": [
        "#CONNECTING FILE TO DRIVE TO EXRTACT AND SAVE DATA AND MODELS\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "MHLXEUb0YSgD",
        "outputId": "1354d41f-5634-4f78-de60-d3beef5ebff7"
      },
      "source": [
        "model = load_model('/content/drive/MyDrive/GAN/generator_model_2000.h5')\n",
        "# generating any randon input (noice) of vector 100\n",
        "vector = randn(100)\n",
        "vector = vector.reshape(1, 100)\n",
        "\n",
        "# generate image\n",
        "X = model.predict(vector)\n",
        "\n",
        "# plot the result\n",
        "pyplot.imshow(X[0, :, :, 0], cmap='gray_r')\n",
        "pyplot.show()\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n",
            "WARNING:tensorflow:10 out of the last 10 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7fb6638b6a70> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}