ei_cifar10_image_classification_nn_classifier_explained.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Edge Impulse: Cifar10 - Image Classification Explained\n",
        " - Using a CNN Classifier"
      ],
      "metadata": {
        "id": "nDXPVliFeOz0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import Main Libraries"
      ],
      "metadata": {
        "id": "YjsRSZfVgbP1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sys, os, random\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, InputLayer, Dropout, Conv1D, Conv2D, Flatten, Reshape, MaxPooling1D, MaxPooling2D, BatchNormalization, TimeDistributed\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "2oazvG6Ogfvh"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ahhVFOSjBSR"
      },
      "source": [
        "## Download the data\n",
        "1. Go to your project or clone this one:\n",
        "    - https://studio.edgeimpulse.com/public/51070/latest\n",
        "2. On `Dashboard` Tab, download the data files:"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "e3f3IWOBfHXS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. On the CoLab Lateral panel, upload the training files"
      ],
      "metadata": {
        "id": "4EhOxlBmfSu4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "id": "xM304cSmgSAW",
        "outputId": "da2b8ab4-5197-4e63-a69e-5a6819b7d90c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ei-cifar10_image_classification-image-X_training.npy  sample_data\n",
            "ei-cifar10_image_classification-image-y_training.npy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a0KMIKTRjBSX"
      },
      "source": [
        "### Load data through Numpy."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define data path\n",
        "X_path = 'ei-cifar10_image_classification-image-X_training.npy'\n",
        "Y_path = 'ei-cifar10_image_classification-image-y_training.npy'\n",
        "\n",
        "# Load the data\n",
        "X = np.load(X_path)\n",
        "Y = np.load(Y_path)[:,0]\n",
        "\n",
        "#data_shape\n",
        "X.shape, Y.shape"
      ],
      "metadata": {
        "id": "iuETblsGkuFv",
        "outputId": "3dab529a-b052-45c2-dff4-464df41d71c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10000, 3072), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "fOOTnIOJlEmE",
        "outputId": "649168b4-1d8a-4a67-ed3c-d9c857ccda11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\t Label Image==>  2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "image = X[0]\n",
        "label = Y[0]\n",
        "reshape_to = (32,32,3)\n",
        "\n",
        "print(\"\\t Label Image==> \",label)\n",
        "img = tf.reshape(image, reshape_to)\n",
        "plt.imshow(img);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bp8KOk2yjBSX"
      },
      "source": [
        "Define our labels and split the data up in a test and training set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8lX-xNAYjBSY",
        "outputId": "da587c87-59b6-4c93-95a2-33360df19761"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# Set random seeds for repeatable results\n",
        "RANDOM_SEED = 3\n",
        "random.seed(RANDOM_SEED)\n",
        "np.random.seed(RANDOM_SEED)\n",
        "tf.random.set_seed(RANDOM_SEED)\n",
        "\n",
        "classes_values = [ \"cat\", \"dog\" ]\n",
        "classes = len(classes_values)\n",
        "classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tTuvSVyJnLgr",
        "outputId": "fe4519e7-27a7-4906-d3c3-e7419e767663"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, ..., 1, 1, 1], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dS4IHm2LnOAv",
        "outputId": "c2d8cb4b-4c72-4382-8696-cb553557a830"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{1, 2}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "set(Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m06dnuc_m9qW",
        "outputId": "31fae3dd-8172-4e17-f9b4-fe8908bc63fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1.],\n",
              "       [0., 1.],\n",
              "       [0., 1.],\n",
              "       ...,\n",
              "       [1., 0.],\n",
              "       [1., 0.],\n",
              "       [1., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "Y = tf.keras.utils.to_categorical(Y - 1, classes)\n",
        "Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGlvRAiGnTaD",
        "outputId": "1caf15d8-8f4b-44e8-acd5-593fd0b09bd3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8000, 3072) (2000, 2)\n",
            "(2000, 3072) (2000, 2)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=1)\n",
        "\n",
        "print(X_train.shape, Y_test.shape)\n",
        "print(X_test.shape, Y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3vo0j4Vni18",
        "outputId": "cc57e799-dc8d-483c-dbcf-1c58389362ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3072"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "input_length = X_train[0].shape[0]\n",
        "input_length"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RAOHANkqouL3"
      },
      "source": [
        "The [tf.data API](https://www.tensorflow.org/guide/data) enables you to build complex input pipelines from simple, reusable pieces. For example, the pipeline for an image model might aggregate data from files in a distributed file system, apply random perturbations to each image, and merge randomly selected images into a batch for training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "7fapeHh6n_vt"
      },
      "outputs": [],
      "source": [
        "train_dataset = tf.data.Dataset.from_tensor_slices((X_train, Y_train))\n",
        "validation_dataset = tf.data.Dataset.from_tensor_slices((X_test, Y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5j2KHvnpRvU",
        "outputId": "a297937b-1063-4b6e-8fe7-d28fd953d37e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<_TensorSliceDataset element_spec=(TensorSpec(shape=(3072,), dtype=tf.float32, name=None), TensorSpec(shape=(2,), dtype=tf.float32, name=None))>\n",
            "<_TensorSliceDataset element_spec=(TensorSpec(shape=(3072,), dtype=tf.float32, name=None), TensorSpec(shape=(2,), dtype=tf.float32, name=None))>\n"
          ]
        }
      ],
      "source": [
        "print(train_dataset)\n",
        "print(validation_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "rNHpZSZ3pO5Q"
      },
      "outputs": [],
      "source": [
        "def get_reshape_function(reshape_to):\n",
        "    def reshape(image, label):\n",
        "        return tf.reshape(image, reshape_to), label\n",
        "    return reshape\n",
        "\n",
        "callbacks = []"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKUaAlurjBSY"
      },
      "source": [
        "Train the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "RO1BxcM_jBSZ"
      },
      "outputs": [],
      "source": [
        "# model architecture\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=3, activation='relu', kernel_constraint=tf.keras.constraints.MaxNorm(1), padding='same'))\n",
        "model.add(MaxPooling2D(pool_size=2, strides=2, padding='same'))\n",
        "model.add(Conv2D(64, kernel_size=3, activation='relu', kernel_constraint=tf.keras.constraints.MaxNorm(1), padding='same'))\n",
        "model.add(MaxPooling2D(pool_size=2, strides=2, padding='same'))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, activation='relu',\n",
        "    activity_regularizer=tf.keras.regularizers.l1(0.00001)))\n",
        "model.add(Dense(classes, activation='softmax', name='y_pred'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "B143Zyqrps4S"
      },
      "outputs": [],
      "source": [
        "# this controls the learning rate\n",
        "opt = Adam(learning_rate=0.0005, beta_1=0.9, beta_2=0.999)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "uSs_E6sMrGcj"
      },
      "outputs": [],
      "source": [
        "# This is only required for some input types, e.g. images\n",
        "SPECIFIC_INPUT_SHAPE = (32, 32, 3)\n",
        "\n",
        "train_dataset = train_dataset.map(get_reshape_function(SPECIFIC_INPUT_SHAPE), tf.data.experimental.AUTOTUNE)\n",
        "validation_dataset = validation_dataset.map(get_reshape_function(SPECIFIC_INPUT_SHAPE), tf.data.experimental.AUTOTUNE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNAwDobWrOS7",
        "outputId": "e8ecc453-fb3e-4c9b-ea85-a9df26e58f5e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<_ParallelMapDataset element_spec=(TensorSpec(shape=(32, 32, 3), dtype=tf.float32, name=None), TensorSpec(shape=(2,), dtype=tf.float32, name=None))>\n",
            "<_ParallelMapDataset element_spec=(TensorSpec(shape=(32, 32, 3), dtype=tf.float32, name=None), TensorSpec(shape=(2,), dtype=tf.float32, name=None))>\n"
          ]
        }
      ],
      "source": [
        "print(train_dataset)\n",
        "print(validation_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "z1dn3oDQquli"
      },
      "outputs": [],
      "source": [
        "# this controls the batch size, or you can manipulate the tf.data.Dataset objects yourself\n",
        "BATCH_SIZE = 32\n",
        "train_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=False)\n",
        "validation_dataset = validation_dataset.batch(BATCH_SIZE, drop_remainder=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z465qlPcra4j",
        "outputId": "7a9485f1-6bcc-444f-f5d3-93a5becf4cae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<_BatchDataset element_spec=(TensorSpec(shape=(None, 32, 32, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 2), dtype=tf.float32, name=None))>\n",
            "<_BatchDataset element_spec=(TensorSpec(shape=(None, 32, 32, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 2), dtype=tf.float32, name=None))>\n"
          ]
        }
      ],
      "source": [
        "print(train_dataset)\n",
        "print(validation_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "1nbXZ6z2rYVG"
      },
      "outputs": [],
      "source": [
        "# compile the model\n",
        "model.compile(\n",
        "    loss='categorical_crossentropy',\n",
        "    optimizer=opt,\n",
        "    metrics=['accuracy']\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fb7sqqIlrrkd",
        "outputId": "c6ee42d3-b2fc-4b6c-a613-67aef1a26aa5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "250/250 - 15s - loss: 0.6639 - accuracy: 0.5946 - val_loss: 0.6872 - val_accuracy: 0.5855 - 15s/epoch - 59ms/step\n",
            "Epoch 2/10\n",
            "250/250 - 13s - loss: 0.6066 - accuracy: 0.6658 - val_loss: 0.6444 - val_accuracy: 0.6370 - 13s/epoch - 52ms/step\n",
            "Epoch 3/10\n",
            "250/250 - 15s - loss: 0.5659 - accuracy: 0.7031 - val_loss: 0.6169 - val_accuracy: 0.6655 - 15s/epoch - 59ms/step\n",
            "Epoch 4/10\n",
            "250/250 - 13s - loss: 0.5329 - accuracy: 0.7281 - val_loss: 0.6003 - val_accuracy: 0.6850 - 13s/epoch - 53ms/step\n",
            "Epoch 5/10\n",
            "250/250 - 13s - loss: 0.5020 - accuracy: 0.7535 - val_loss: 0.5826 - val_accuracy: 0.7000 - 13s/epoch - 53ms/step\n",
            "Epoch 6/10\n",
            "250/250 - 13s - loss: 0.4702 - accuracy: 0.7774 - val_loss: 0.5695 - val_accuracy: 0.7145 - 13s/epoch - 51ms/step\n",
            "Epoch 7/10\n",
            "250/250 - 13s - loss: 0.4376 - accuracy: 0.7979 - val_loss: 0.5767 - val_accuracy: 0.7225 - 13s/epoch - 53ms/step\n",
            "Epoch 8/10\n",
            "250/250 - 13s - loss: 0.4026 - accuracy: 0.8192 - val_loss: 0.5915 - val_accuracy: 0.7285 - 13s/epoch - 51ms/step\n",
            "Epoch 9/10\n",
            "250/250 - 13s - loss: 0.3665 - accuracy: 0.8428 - val_loss: 0.6149 - val_accuracy: 0.7205 - 13s/epoch - 52ms/step\n",
            "Epoch 10/10\n",
            "250/250 - 14s - loss: 0.3321 - accuracy: 0.8561 - val_loss: 0.6451 - val_accuracy: 0.7230 - 14s/epoch - 57ms/step\n"
          ]
        }
      ],
      "source": [
        "# train the neural network\n",
        "history = model.fit(\n",
        "    train_dataset,\n",
        "    epochs=10,\n",
        "    validation_data=validation_dataset,\n",
        "    verbose=2,\n",
        "    callbacks=callbacks\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "lAYilIQytAQP",
        "outputId": "c8f302b6-cbf5-438c-c718-0f710509d5a2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "1WS3xIsUjBSa"
      },
      "outputs": [],
      "source": [
        "# Save the model to disk\n",
        "model.save('saved_model')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IsEK3peJ9PJ5"
      },
      "source": [
        "# Test Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_85CHmOdr2O-"
      },
      "source": [
        "### Single image test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWgC_9wAqBRg",
        "outputId": "069a257b-9db6-41cd-c933-a38fd0bef5d2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3072,)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "my_image = X_test[1]\n",
        "my_image.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDH3EUtVqNwh",
        "outputId": "85f1169c-82a3-4e0a-801c-42e5b2059ad9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "my_image = my_image.reshape(1,32,32,3)\n",
        "my_image.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "e8feLEEwq1oC",
        "outputId": "bbd4980f-f5e1-45cf-d0da-4b57acc39db4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.imshow(my_image.reshape(32,32,3));"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "p01CckW2o2zm",
        "outputId": "3ff0ad13-f94e-4d9d-8074-a92bb0efc44b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 115ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'dog'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "img_pred = np.argmax(model.predict(my_image))\n",
        "pred_label = classes_values[img_pred]\n",
        "pred_label"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VuctnTdhr8RA"
      },
      "source": [
        "### Matriz de Confusão"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sONmeJ_Y1bY8",
        "outputId": "91b7da63-2a07-45ea-f678-97220696bc55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "Y_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHcwGHbJ20rr",
        "outputId": "73f770b5-e07a-467a-9ea0-8124e387bcf3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 2s 27ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000,)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "images = X_test.reshape((2000,32,32,3))\n",
        "predictions = np.argmax(model.predict(images), axis=-1)\n",
        "predictions.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pvXszzFr10i8",
        "outputId": "964e6720-caf5-4344-9a0d-953e5e3562b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000,)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "test_labels = np.argmax(Y_test, axis=-1)\n",
        "test_labels.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E5zOOmwE5rkp",
        "outputId": "e9c16ce5-de0b-4bb4-faa2-c285a08ca23b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0, 1}"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "set(list(predictions))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "OOP1ytNXoKpB"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCdeOcKooS0o",
        "outputId": "3566856c-e1b3-49cd-e7a1-4d3a0a5aff1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         cat       0.78      0.61      0.68       979\n",
            "         dog       0.69      0.83      0.75      1021\n",
            "\n",
            "    accuracy                           0.72      2000\n",
            "   macro avg       0.73      0.72      0.72      2000\n",
            "weighted avg       0.73      0.72      0.72      2000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(test_labels, predictions, target_names=classes_values))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DiAQINRH6S80",
        "outputId": "8af7970e-fb0a-4425-d755-5b91b2981901"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[598, 381],\n",
              "       [173, 848]])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "confusion_matrix(test_labels,predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "K7IUBbcI6VRB",
        "outputId": "91b8a27e-c9f4-4a38-e308-61805a9fdf03"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "plt.figure(figsize=(15,8))\n",
        "sns.heatmap(confusion_matrix(test_labels,predictions), cmap='Blues', annot=True, fmt='g', xticklabels=classes_values, yticklabels=classes_values);"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "ei_cifar10_image_classification_nn_classifier_explained.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}