Cats_Vs_Dogs_Mini_DataSet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Cats Vs Dogs Mini DataSet ",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ikramooe/DataScience-1/blob/master/Cats_Vs_Dogs_Mini_DataSet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMThpsM1d7jD",
        "colab_type": "code",
        "outputId": "083612e4-5f09-4d9e-d0ba-aa737e496135",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "  https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip \\\n",
        "  -O /tmp/cats_and_dogs_filtered.zip\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-05-25 14:29:32--  https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.26.16, 2404:6800:4004:801::2010\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.26.16|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 68606236 (65M) [application/zip]\n",
            "Saving to: ‘/tmp/cats_and_dogs_filtered.zip’\n",
            "\n",
            "/tmp/cats_and_dogs_ 100%[===================>]  65.43M  51.7MB/s    in 1.3s    \n",
            "\n",
            "2019-05-25 14:29:34 (51.7 MB/s) - ‘/tmp/cats_and_dogs_filtered.zip’ saved [68606236/68606236]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ksgxd8kWdDfo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "import tensorflow as tf \n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator \n",
        "from tensorflow.keras.preprocessing import image \n",
        "import matplotlib.pyplot as plt \n",
        "import matplotlib.image as mpimg\n",
        "import requests\n",
        "import zipfile\n",
        "import os "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bRdXx9qe4_k",
        "colab_type": "code",
        "outputId": "56fee857-e34d-40f2-d435-1186f152f0fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "local_zip = '/tmp/cats_and_dogs_filtered.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip,'r')\n",
        "zip_ref.extractall('/tmp/cats_and_dogs_filtered')\n",
        "zip_ref.close()\n",
        "\n",
        "base_dir = '/tmp/cats_and_dogs_filtered'\n",
        "\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "validation_dir = os.path.join(base_dir,'validation')\n",
        "\n",
        "\n",
        "train_cats_dir = os.path.join(train_dir,'cats')\n",
        "train_dogs_dir = os.path.join(train_dir,'dogs')\n",
        "\n",
        "\n",
        "validation_cats_dir = os.path.join(validation_dir,'cats')\n",
        "validation_dogs_dir = os.path.join(validation_dir,'dogs')\n",
        "\n",
        "train_cats_names = os.listdir(train_cats_dir)\n",
        "train_dogs_names = os.listdir(train_dogs_dir)\n",
        "\n",
        "\n",
        "print(train_cats_names[:10])\n",
        "print(train_dogs_names[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['cat.185.jpg', 'cat.910.jpg', 'cat.588.jpg', 'cat.5.jpg', 'cat.702.jpg', 'cat.25.jpg', 'cat.343.jpg', 'cat.335.jpg', 'cat.629.jpg', 'cat.40.jpg']\n",
            "['dog.644.jpg', 'dog.434.jpg', 'dog.105.jpg', 'dog.927.jpg', 'dog.561.jpg', 'dog.805.jpg', 'dog.535.jpg', 'dog.246.jpg', 'dog.532.jpg', 'dog.186.jpg']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k7Ir1J5wnB6h",
        "colab_type": "code",
        "outputId": "bc516f0b-d4d8-4ea9-8b6f-22160c54ce3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "fig , axarr = plt.subplots(2,5)\n",
        "img_cat = [os.path.join(train_cats_dir , fname)\n",
        "          for fname in train_cats_names[:5]\n",
        "          ]\n",
        "img_dog = [os.path.join(train_dogs_dir , fname)\n",
        "          for fname in train_dogs_names[5:10]\n",
        "          ]\n",
        "\n",
        "for img_path in enumerate(img_cat):\n",
        "  i = img_path[0]\n",
        "  x=mpimg.imread(img_path[1])\n",
        "  axarr[0][i].imshow(x)\n",
        "  axarr[0][i].grid=False\n",
        "  \n",
        "for img_path in enumerate(img_dog):\n",
        "  i = img_path[0]\n",
        "  x=mpimg.imread(img_path[1])\n",
        "  axarr[1][i].imshow(x)\n",
        "  axarr[1][i].grid=False\n",
        "  axarr[1][i].axis('Off')\n",
        "  plt.imshow(x)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "print(\"total number of cats :\",len(os.listdir(train_cats_dir)))\n",
        "print(\"total number of dogs:\",len(os.listdir(train_dogs_dir)))\n",
        "\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "     '/tmp/cats_and_dogs_filtered/train',\n",
        "      target_size=(150,150),\n",
        "      batch_size=20,\n",
        "      class_mode='binary')\n",
        "\n",
        "\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "      '/tmp/cats_and_dogs_filtered/validation',\n",
        "      target_size=(150,150),\n",
        "      class_mode='binary',\n",
        "      batch_size=20)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WMo++q8A1TaPRaDA0Mrxy/b6QdDqdh6RgevvqKfZeVHFyVHDytoZhEEURQRBg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\nfOp5NmzYwOT0FPFiEsuyME2TMy+4gIfufwCqTW676Tskk0lqjTqpXBb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gTle59747efVl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\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "total number of cats : 1000\n",
            "total number of dogs: 1000\n",
            "Found 2000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVA9ly2A1sLM",
        "colab_type": "code",
        "outputId": "b0420309-d6ec-4782-8e90-9c33ee6423ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        }
      },
      "source": [
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(16,(3,3),activation='relu',input_shape=(150,150,3)),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(32,(3,3),activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(512,activation='relu'),\n",
        "    tf.keras.layers.Dense(1,activation='sigmoid')\n",
        "\n",
        "])\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer='Adam',metrics=['accuracy'])\n",
        "\n",
        "history = model.fit_generator(\n",
        "\n",
        "   train_generator,\n",
        "\n",
        "   steps_per_epoch=100,\n",
        "\n",
        "   epochs=15,\n",
        "\n",
        "   validation_data = test_generator,\n",
        "\n",
        "   validation_steps=8)\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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/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",
            "Epoch 1/15\n",
            "50/50 [==============================] - 3s 65ms/step - loss: 0.6503 - acc: 0.6260\n",
            "100/100 [==============================] - 11s 114ms/step - loss: 0.7018 - acc: 0.5510 - val_loss: 0.6503 - val_acc: 0.6260\n",
            "Epoch 2/15\n",
            "50/50 [==============================] - 3s 63ms/step - loss: 0.6198 - acc: 0.6610\n",
            "100/100 [==============================] - 9s 88ms/step - loss: 0.6355 - acc: 0.6435 - val_loss: 0.6198 - val_acc: 0.6610\n",
            "Epoch 3/15\n",
            "50/50 [==============================] - 3s 64ms/step - loss: 0.6059 - acc: 0.6570\n",
            "100/100 [==============================] - 9s 88ms/step - loss: 0.5724 - acc: 0.7105 - val_loss: 0.6059 - val_acc: 0.6570\n",
            "Epoch 4/15\n",
            "50/50 [==============================] - 3s 64ms/step - loss: 0.6420 - acc: 0.6850\n",
            "100/100 [==============================] - 9s 88ms/step - loss: 0.5092 - acc: 0.7550 - val_loss: 0.6420 - val_acc: 0.6850\n",
            "Epoch 5/15\n",
            "50/50 [==============================] - 3s 63ms/step - loss: 0.6594 - acc: 0.6870\n",
            "100/100 [==============================] - 9s 88ms/step - loss: 0.4369 - acc: 0.8040 - val_loss: 0.6594 - val_acc: 0.6870\n",
            "Epoch 6/15\n",
            "50/50 [==============================] - 4s 73ms/step - loss: 0.6922 - acc: 0.6890\n",
            "100/100 [==============================] - 9s 94ms/step - loss: 0.3553 - acc: 0.8395 - val_loss: 0.6922 - val_acc: 0.6890\n",
            "Epoch 7/15\n",
            "50/50 [==============================] - 3s 66ms/step - loss: 0.7833 - acc: 0.6920\n",
            "100/100 [==============================] - 10s 95ms/step - loss: 0.2476 - acc: 0.8955 - val_loss: 0.7833 - val_acc: 0.6920\n",
            "Epoch 8/15\n",
            "50/50 [==============================] - 3s 68ms/step - loss: 0.9427 - acc: 0.7010\n",
            "100/100 [==============================] - 9s 92ms/step - loss: 0.1580 - acc: 0.9415 - val_loss: 0.9427 - val_acc: 0.7010\n",
            "Epoch 9/15\n",
            "50/50 [==============================] - 3s 66ms/step - loss: 1.0292 - acc: 0.7070\n",
            "100/100 [==============================] - 9s 89ms/step - loss: 0.1049 - acc: 0.9675 - val_loss: 1.0292 - val_acc: 0.7070\n",
            "Epoch 10/15\n",
            "50/50 [==============================] - 3s 64ms/step - loss: 1.3472 - acc: 0.7020\n",
            "100/100 [==============================] - 9s 93ms/step - loss: 0.0478 - acc: 0.9855 - val_loss: 1.3472 - val_acc: 0.7020\n",
            "Epoch 11/15\n",
            "50/50 [==============================] - 3s 65ms/step - loss: 1.3908 - acc: 0.6890\n",
            "100/100 [==============================] - 9s 88ms/step - loss: 0.0567 - acc: 0.9785 - val_loss: 1.3908 - val_acc: 0.6890\n",
            "Epoch 12/15\n",
            "50/50 [==============================] - 3s 64ms/step - loss: 1.2345 - acc: 0.7110\n",
            "100/100 [==============================] - 9s 88ms/step - loss: 0.0826 - acc: 0.9725 - val_loss: 1.2345 - val_acc: 0.7110\n",
            "Epoch 13/15\n",
            "50/50 [==============================] - 3s 64ms/step - loss: 1.5808 - acc: 0.6900\n",
            "100/100 [==============================] - 9s 88ms/step - loss: 0.0153 - acc: 0.9990 - val_loss: 1.5808 - val_acc: 0.6900\n",
            "Epoch 14/15\n",
            "50/50 [==============================] - 3s 63ms/step - loss: 1.5890 - acc: 0.7000\n",
            "100/100 [==============================] - 9s 90ms/step - loss: 0.0089 - acc: 0.9980 - val_loss: 1.5890 - val_acc: 0.7000\n",
            "Epoch 15/15\n",
            "50/50 [==============================] - 4s 72ms/step - loss: 1.6759 - acc: 0.6850\n",
            "100/100 [==============================] - 9s 94ms/step - loss: 0.0151 - acc: 0.9960 - val_loss: 1.6759 - val_acc: 0.6850\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMEkfVG53LFd",
        "colab_type": "code",
        "outputId": "cd4523c2-a3ca-4073-ba3d-6a670bb04fa3",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "\n",
        "from google.colab import files\n",
        "from keras.preprocessing import image\n",
        "\n",
        "uploaded=files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        " \n",
        "  # predicting images\n",
        "  path='/content/' + fn\n",
        "  img=image.load_img(path, target_size=(150, 150))\n",
        "  \n",
        "  x=image.img_to_array(img)\n",
        "  x=np.expand_dims(x, axis=0)\n",
        "  images = np.vstack([x])\n",
        "  \n",
        "  classes = model.predict(images, batch_size=10)\n",
        "  \n",
        "  print(classes)\n",
        "  \n",
        "  if classes[0]>0:\n",
        "    print(fn + \" is a dog\")\n",
        "    \n",
        "  else:\n",
        "    print(fn + \" is a cat\")\n",
        "    \n",
        "    \n",
        "    \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d2310e05-323b-468a-9563-32fb213c5726\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-d2310e05-323b-468a-9563-32fb213c5726\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving animal-339400_1280.jpg to animal-339400_1280.jpg\n",
            "[[1.]]\n",
            "animal-339400_1280.jpg is a dog\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fleRfM6NGXVf",
        "colab_type": "code",
        "outputId": "1ac2d96b-8349-491b-9c3c-a1daf5430842",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "source": [
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "\n",
        "loss=history.history['loss']\n",
        "val_loss = history.history['val_acc']\n",
        "\n",
        "epochs= range(len(acc))\n",
        "\n",
        "plt.plot(epochs,acc)\n",
        "plt.plot(epochs,val_acc)\n",
        "plt.title(\"training and validation accuracy\")\n",
        "\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs,loss)\n",
        "plt.plot(epochs,val_loss)\n",
        "plt.title(\"training and testing loss\")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'training and testing loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BLvkDjPqx9RwBep0DF/7OCvfvXoWFM63nmDwTRs+AmE6OPoVAIcaYhmcQCQZ2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\nNNyVUioAabgrpVQA0nBXSqkA9P/h8mW+0pmm3gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I6IGs21nEA+/Y6ZHGBIpf4S4iI0VktYjkishddcy/QUS+EZHFIjJHRHoHvlQT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\nTBiycDfGmDBk4W6MMWHo/wGMD+722mv81QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ah7yICbqVZT-",
        "colab_type": "code",
        "outputId": "0f0dae1d-6f1a-4c59-e884-7bbec31be418",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1058
        }
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "# Updated to do image augmentation\n",
        "train_datagen = ImageDataGenerator(\n",
        "      rescale=1./255,\n",
        "      rotation_range=40,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')\n",
        "     \n",
        " \n",
        "      \n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "     '/tmp/cats_and_dogs_filtered/train',\n",
        "      target_size=(150,150),\n",
        "      batch_size=20,\n",
        "      class_mode='binary')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "      '/tmp/cats_and_dogs_filtered/validation',\n",
        "      target_size=(150,150),\n",
        "      class_mode='binary',\n",
        "      batch_size=20)\n",
        "\n",
        "\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer='Adam',metrics=['accuracy'])\n",
        "\n",
        "history = model.fit_generator(\n",
        "\n",
        "   train_generator,\n",
        "\n",
        "   steps_per_epoch=100,\n",
        "\n",
        "   epochs=20,\n",
        "\n",
        "   validation_data = test_generator,\n",
        "\n",
        "   validation_steps=8)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n",
            "Epoch 1/20\n",
            "50/50 [==============================] - 4s 76ms/step - loss: 0.5164 - acc: 0.7400\n",
            "100/100 [==============================] - 17s 173ms/step - loss: 0.5379 - acc: 0.7270 - val_loss: 0.5164 - val_acc: 0.7400\n",
            "Epoch 2/20\n",
            "50/50 [==============================] - 4s 76ms/step - loss: 0.5108 - acc: 0.7460\n",
            "100/100 [==============================] - 16s 164ms/step - loss: 0.5477 - acc: 0.7275 - val_loss: 0.5108 - val_acc: 0.7460\n",
            "Epoch 3/20\n",
            "50/50 [==============================] - 4s 75ms/step - loss: 0.5512 - acc: 0.7250\n",
            "100/100 [==============================] - 16s 165ms/step - loss: 0.5319 - acc: 0.7365 - val_loss: 0.5512 - val_acc: 0.7250\n",
            "Epoch 4/20\n",
            "50/50 [==============================] - 4s 82ms/step - loss: 0.4894 - acc: 0.7640\n",
            "100/100 [==============================] - 17s 168ms/step - loss: 0.5137 - acc: 0.7450 - val_loss: 0.4894 - val_acc: 0.7640\n",
            "Epoch 5/20\n",
            "50/50 [==============================] - 4s 75ms/step - loss: 0.5554 - acc: 0.7200\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.5087 - acc: 0.7530 - val_loss: 0.5554 - val_acc: 0.7200\n",
            "Epoch 6/20\n",
            "50/50 [==============================] - 4s 77ms/step - loss: 0.4867 - acc: 0.7590\n",
            "100/100 [==============================] - 17s 165ms/step - loss: 0.5142 - acc: 0.7520 - val_loss: 0.4867 - val_acc: 0.7590\n",
            "Epoch 7/20\n",
            "50/50 [==============================] - 4s 88ms/step - loss: 0.4891 - acc: 0.7500\n",
            "100/100 [==============================] - 17s 174ms/step - loss: 0.5133 - acc: 0.7465 - val_loss: 0.4891 - val_acc: 0.7500\n",
            "Epoch 8/20\n",
            "50/50 [==============================] - 4s 76ms/step - loss: 0.5100 - acc: 0.7470\n",
            "100/100 [==============================] - 17s 169ms/step - loss: 0.4951 - acc: 0.7505 - val_loss: 0.5100 - val_acc: 0.7470\n",
            "Epoch 9/20\n",
            "50/50 [==============================] - 4s 87ms/step - loss: 0.4888 - acc: 0.7590\n",
            "100/100 [==============================] - 17s 175ms/step - loss: 0.4943 - acc: 0.7560 - val_loss: 0.4888 - val_acc: 0.7590\n",
            "Epoch 10/20\n",
            "50/50 [==============================] - 4s 76ms/step - loss: 0.4916 - acc: 0.7470\n",
            "100/100 [==============================] - 17s 168ms/step - loss: 0.5072 - acc: 0.7470 - val_loss: 0.4916 - val_acc: 0.7470\n",
            "Epoch 11/20\n",
            "50/50 [==============================] - 4s 76ms/step - loss: 0.4937 - acc: 0.7610\n",
            "100/100 [==============================] - 16s 165ms/step - loss: 0.4916 - acc: 0.7625 - val_loss: 0.4937 - val_acc: 0.7610\n",
            "Epoch 12/20\n",
            "50/50 [==============================] - 4s 75ms/step - loss: 0.4887 - acc: 0.7710\n",
            "100/100 [==============================] - 16s 165ms/step - loss: 0.4848 - acc: 0.7565 - val_loss: 0.4887 - val_acc: 0.7710\n",
            "Epoch 13/20\n",
            "50/50 [==============================] - 4s 75ms/step - loss: 0.4838 - acc: 0.7510\n",
            "100/100 [==============================] - 16s 164ms/step - loss: 0.4915 - acc: 0.7650 - val_loss: 0.4838 - val_acc: 0.7510\n",
            "Epoch 14/20\n",
            "50/50 [==============================] - 4s 81ms/step - loss: 0.5130 - acc: 0.7400\n",
            "100/100 [==============================] - 18s 176ms/step - loss: 0.4886 - acc: 0.7675 - val_loss: 0.5130 - val_acc: 0.7400\n",
            "Epoch 15/20\n",
            "50/50 [==============================] - 4s 75ms/step - loss: 0.5329 - acc: 0.7480\n",
            "100/100 [==============================] - 16s 164ms/step - loss: 0.4813 - acc: 0.7620 - val_loss: 0.5329 - val_acc: 0.7480\n",
            "Epoch 16/20\n",
            "50/50 [==============================] - 4s 74ms/step - loss: 0.5145 - acc: 0.7360\n",
            "100/100 [==============================] - 16s 163ms/step - loss: 0.4729 - acc: 0.7705 - val_loss: 0.5145 - val_acc: 0.7360\n",
            "Epoch 17/20\n",
            "50/50 [==============================] - 4s 75ms/step - loss: 0.4985 - acc: 0.7600\n",
            "100/100 [==============================] - 16s 163ms/step - loss: 0.4710 - acc: 0.7765 - val_loss: 0.4985 - val_acc: 0.7600\n",
            "Epoch 18/20\n",
            "50/50 [==============================] - 4s 75ms/step - loss: 0.4796 - acc: 0.7640\n",
            "100/100 [==============================] - 17s 170ms/step - loss: 0.4645 - acc: 0.7745 - val_loss: 0.4796 - val_acc: 0.7640\n",
            "Epoch 19/20\n",
            "50/50 [==============================] - 4s 75ms/step - loss: 0.4776 - acc: 0.7560\n",
            "100/100 [==============================] - 18s 177ms/step - loss: 0.4614 - acc: 0.7800 - val_loss: 0.4776 - val_acc: 0.7560\n",
            "Epoch 20/20\n",
            "50/50 [==============================] - 4s 74ms/step - loss: 0.5235 - acc: 0.7580\n",
            "100/100 [==============================] - 16s 163ms/step - loss: 0.4595 - acc: 0.7770 - val_loss: 0.5235 - val_acc: 0.7580\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HY3P_qXIZA4N",
        "colab_type": "code",
        "outputId": "997b0f32-f019-4589-a12b-13fd2c2bd8b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "source": [
        " \n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "\n",
        "loss=history.history['loss']\n",
        "val_loss = history.history['val_acc']\n",
        "\n",
        "epochs= range(len(acc))\n",
        "\n",
        "plt.plot(epochs,acc)\n",
        "plt.plot(epochs,val_acc)\n",
        "plt.title(\"training and validation accuracy\")\n",
        "\n",
        "\n",
        "plt.figure()\n",
        "\n",
        "plt.plot(epochs,loss)\n",
        "plt.plot(epochs,val_loss)\n",
        "plt.title(\"training and testing loss\")\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'training and testing loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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luhGdiOvQRof1GtE/k4Js7SaNv0jfMbuC85/Wn/NPf6+xvkFDxx7RPwjYXjY7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\npIMo6YuIdBAlfRGRDvJ/BHVKSr9GcgUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IzBeRGaq6tFLRV1V1ShVvsUdVh9Y/VGOMMfXlz5H+cCBDVVerajEwHZjYsGEZ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\n4sYsDtIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HevCaLjGn4Qk",
        "colab_type": "code",
        "outputId": "4081e85a-b8ec-4803-f013-4ace4b586a57",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 108
        }
      },
      "source": [
        "from google.colab import files\n",
        "from keras.preprocessing import image\n",
        "\n",
        "uploaded=files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        " \n",
        "  # predicting images\n",
        "  path='/content/' + fn\n",
        "  img=image.load_img(path, target_size=(150, 150))\n",
        "  \n",
        "  x=image.img_to_array(img)\n",
        "  x=np.expand_dims(x, axis=0)\n",
        "  images = np.vstack([x])\n",
        "  \n",
        "  classes = model.predict(images, batch_size=10)\n",
        "  \n",
        "  print(classes)\n",
        "  \n",
        "  if classes[0]>0:\n",
        "    print(fn + \" is a dog\")\n",
        "    \n",
        "  else:\n",
        "    print(fn + \" is a cat\")\n",
        "    \n",
        "    \n",
        "    \n",
        "    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f6f68d8b-eec7-49a4-9848-36568e609199\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-f6f68d8b-eec7-49a4-9848-36568e609199\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving animal-339400_1280.jpg to animal-339400_1280 (1).jpg\n",
            "[[1.]]\n",
            "animal-339400_1280.jpg is a dog\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}