ann&&cnn.ipynb

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  "metadata": {
    "colab": {
      "name": "ann&&cnn.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNa3JcYhG2+Umcqp2QM+rnI",
      "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/github/AhmadAlabrash/Deep-Learning/blob/main/ann%26%26cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LyfBCV4zftYx"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test,y_test) = datasets.cifar10.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fNHfTkCxgMXX",
        "outputId": "fec6bfdc-8f8a-4f76-822e-f0fc54e58046"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 4s 0us/step\n",
            "170508288/170498071 [==============================] - 4s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[1]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9g8On77zgWeP",
        "outputId": "3637d658-8ec6-4226-834d-0cc9316a0728"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[154, 177, 187],\n",
              "        [126, 137, 136],\n",
              "        [105, 104,  95],\n",
              "        ...,\n",
              "        [ 91,  95,  71],\n",
              "        [ 87,  90,  71],\n",
              "        [ 79,  81,  70]],\n",
              "\n",
              "       [[140, 160, 169],\n",
              "        [145, 153, 154],\n",
              "        [125, 125, 118],\n",
              "        ...,\n",
              "        [ 96,  99,  78],\n",
              "        [ 77,  80,  62],\n",
              "        [ 71,  73,  61]],\n",
              "\n",
              "       [[140, 155, 164],\n",
              "        [139, 146, 149],\n",
              "        [115, 115, 112],\n",
              "        ...,\n",
              "        [ 79,  82,  64],\n",
              "        [ 68,  70,  55],\n",
              "        [ 67,  69,  55]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[175, 167, 166],\n",
              "        [156, 154, 160],\n",
              "        [154, 160, 170],\n",
              "        ...,\n",
              "        [ 42,  34,  36],\n",
              "        [ 61,  53,  57],\n",
              "        [ 93,  83,  91]],\n",
              "\n",
              "       [[165, 154, 128],\n",
              "        [156, 152, 130],\n",
              "        [159, 161, 142],\n",
              "        ...,\n",
              "        [103,  93,  96],\n",
              "        [123, 114, 120],\n",
              "        [131, 121, 131]],\n",
              "\n",
              "       [[163, 148, 120],\n",
              "        [158, 148, 122],\n",
              "        [163, 156, 133],\n",
              "        ...,\n",
              "        [143, 133, 139],\n",
              "        [143, 134, 142],\n",
              "        [143, 133, 144]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gqDgYkEhgpWB",
        "outputId": "77931857-77db-4391-ef90-ed63a1130525"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4YaBV-3KgxwB",
        "outputId": "69a7d644-3269-41c8-bbcf-ee7f0d9a57ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0ipbLyphGA4",
        "outputId": "91da0200-b73e-489a-aa6a-efd57ccd9bbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6],\n",
              "       [9],\n",
              "       [9],\n",
              "       ...,\n",
              "       [9],\n",
              "       [1],\n",
              "       [1]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rGjVDgiHhOOt",
        "outputId": "9cce10fe-99b3-4611-8b09-d3293dba37fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JkvroS6mhUDK",
        "outputId": "1816187f-b0f3-4b26-d1c8-5809e6e63f1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6],\n",
              "       [9],\n",
              "       [9],\n",
              "       [4],\n",
              "       [1]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "HIUo4WgdjunC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train=y_train.reshape(-1,)"
      ],
      "metadata": {
        "id": "U-bTEDs0i8YF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classes = [\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]"
      ],
      "metadata": {
        "id": "fh29hYwAhUNF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plt_see(i):\n",
        " plt.imshow(X_train[i])\n",
        " plt.xlabel(classes[y_train[i]])"
      ],
      "metadata": {
        "id": "2uHm6V17hny0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sys import platform\n",
        "platform"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Rfff1N4JjoYn",
        "outputId": "89287817-4c04-4cc3-e2a0-4a996c4fbb12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'linux'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt_see(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "-OMuykzkjwf6",
        "outputId": "9bc56840-5384-4278-8267-6f79ec02af41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt_see(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "Qt9J8IoWj4_0",
        "outputId": "37d80176-6c91-4ad6-f4eb-df10389d98b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train / 255.0\n",
        "X_test = X_test / 255.0"
      ],
      "metadata": {
        "id": "YUSCJdJaj_o3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ann = models.Sequential([\n",
        "        layers.Flatten(input_shape=(32,32,3)),\n",
        "        layers.Dense(3000, activation='relu'),\n",
        "        layers.Dense(1000, activation='relu'),\n",
        "        layers.Dense(10, activation='softmax')    \n",
        "    ])\n",
        "\n",
        "ann.compile(optimizer='SGD',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "ann.fit(X_train, y_train, epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFRZoabblmkK",
        "outputId": "4bff3572-6b8b-4355-dad8-3837a3dcca17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1563/1563 [==============================] - 124s 79ms/step - loss: 1.8079 - accuracy: 0.3558\n",
            "Epoch 2/5\n",
            "1563/1563 [==============================] - 124s 79ms/step - loss: 1.6225 - accuracy: 0.4263\n",
            "Epoch 3/5\n",
            "1563/1563 [==============================] - 126s 81ms/step - loss: 1.5399 - accuracy: 0.4599\n",
            "Epoch 4/5\n",
            "1563/1563 [==============================] - 123s 79ms/step - loss: 1.4790 - accuracy: 0.4786\n",
            "Epoch 5/5\n",
            "1563/1563 [==============================] - 121s 77ms/step - loss: 1.4286 - accuracy: 0.4966\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f87efee4610>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix , classification_report\n",
        "ann.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D8AEptDToJiz",
        "outputId": "d9db22fb-68a2-4119-902c-daaa9362e856"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 10s 31ms/step - loss: 1.4929 - accuracy: 0.4669\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.4928940534591675, 0.4668999910354614]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pr =ann.predict(X_test)\n",
        "y_pred_classes = [np.argmax(element) for element in pr]\n",
        "print(\"Classification Report: \\n\", classification_report(y_test, y_pred_classes))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezKMMQAUogi_",
        "outputId": "e9bc70f9-fdaf-4b2d-9584-3713fdc6af5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      0.48      0.55      1000\n",
            "           1       0.58      0.62      0.60      1000\n",
            "           2       0.47      0.19      0.27      1000\n",
            "           3       0.48      0.13      0.20      1000\n",
            "           4       0.29      0.69      0.40      1000\n",
            "           5       0.47      0.29      0.36      1000\n",
            "           6       0.57      0.40      0.47      1000\n",
            "           7       0.48      0.59      0.53      1000\n",
            "           8       0.63      0.59      0.61      1000\n",
            "           9       0.43      0.70      0.53      1000\n",
            "\n",
            "    accuracy                           0.47     10000\n",
            "   macro avg       0.50      0.47      0.45     10000\n",
            "weighted avg       0.50      0.47      0.45     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn = models.Sequential([\n",
        "    layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    \n",
        "    layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    \n",
        "    layers.Flatten(),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(10, activation='softmax')\n",
        "])\n",
        "cnn.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "7LxzogbxqJ0s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.fit(X_train, y_train, epochs=10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5taxe0eCqbT-",
        "outputId": "bc21bf3d-0244-43bf-a3e9-cfdbd7930452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1563/1563 [==============================] - 72s 46ms/step - loss: 1.4521 - accuracy: 0.4779\n",
            "Epoch 2/10\n",
            "1563/1563 [==============================] - 72s 46ms/step - loss: 1.0922 - accuracy: 0.6190\n",
            "Epoch 3/10\n",
            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.9537 - accuracy: 0.6690\n",
            "Epoch 4/10\n",
            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.8575 - accuracy: 0.7019\n",
            "Epoch 5/10\n",
            "1563/1563 [==============================] - 70s 45ms/step - loss: 0.7816 - accuracy: 0.7286\n",
            "Epoch 6/10\n",
            "1563/1563 [==============================] - 70s 45ms/step - loss: 0.7184 - accuracy: 0.7487\n",
            "Epoch 7/10\n",
            "1563/1563 [==============================] - 70s 45ms/step - loss: 0.6627 - accuracy: 0.7701\n",
            "Epoch 8/10\n",
            "1563/1563 [==============================] - 71s 45ms/step - loss: 0.6079 - accuracy: 0.7867\n",
            "Epoch 9/10\n",
            "1563/1563 [==============================] - 69s 44ms/step - loss: 0.5606 - accuracy: 0.8050\n",
            "Epoch 10/10\n",
            "1563/1563 [==============================] - 70s 45ms/step - loss: 0.5116 - accuracy: 0.8182\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f87eb493fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pp=cnn.predict(X_test)\n",
        "y_classes = [np.argmax(element) for element in pp]\n",
        "\n"
      ],
      "metadata": {
        "id": "e3LF2rk2tblB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Classification Report: \\n\", classification_report(y_test, y_classes))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUDnCvo-t3He",
        "outputId": "fa275ce6-d425-460f-8dcf-71e08dd41f0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.80      0.74      1000\n",
            "           1       0.80      0.82      0.81      1000\n",
            "           2       0.55      0.64      0.59      1000\n",
            "           3       0.49      0.50      0.50      1000\n",
            "           4       0.70      0.62      0.66      1000\n",
            "           5       0.68      0.48      0.56      1000\n",
            "           6       0.76      0.78      0.77      1000\n",
            "           7       0.72      0.78      0.75      1000\n",
            "           8       0.85      0.77      0.81      1000\n",
            "           9       0.77      0.79      0.78      1000\n",
            "\n",
            "    accuracy                           0.70     10000\n",
            "   macro avg       0.70      0.70      0.70     10000\n",
            "weighted avg       0.70      0.70      0.70     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_classes[1]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PLAteox6uBUq",
        "outputId": "0dc04574-69af-4559-ae56-ca7701d10c78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tyf46mhyuVhk",
        "outputId": "837ad9c9-ef8d-4597-817f-fbd0f6b93cc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_classes[8]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5oIOn_Runvv",
        "outputId": "e1b445f8-64bf-4e55-ace2-af730c9ec625"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[8]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eSsCc4M6ugRZ",
        "outputId": "dd390cec-147f-4bc7-b34e-d6050a15f6dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    }
  ]
}