Cifar 10 - Image Classification.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom tensorflow.keras.datasets import cifar10\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/cifar-10labels/trainLabels.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"NUM_CLASSES = 10\nKERNEL_SIZE = (2, 2)\nPOOL_SIZE = (2, 2)\nINPUT_SHAPE = (32, 32, 3)","metadata":{"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"\n(x_train, y_train), (x_test, y_test) = cifar10.load_data()","metadata":{"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n170500096/170498071 [==============================] - 6s 0us/step\n","output_type":"stream"}]},{"cell_type":"code","source":"x_train.shape","metadata":{"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"(50000, 32, 32, 3)"},"metadata":{}}]},{"cell_type":"code","source":"y_train.shape","metadata":{"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"(50000, 1)"},"metadata":{}}]},{"cell_type":"code","source":"y_train[0]","metadata":{"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"array([6], dtype=uint8)"},"metadata":{}}]},{"cell_type":"code","source":"labels = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']","metadata":{"trusted":true},"execution_count":69,"outputs":[]},{"cell_type":"code","source":"labels","metadata":{"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"            label\nid               \n1            frog\n2           truck\n3           truck\n4            deer\n5      automobile\n...           ...\n49996        bird\n49997        frog\n49998       truck\n49999  automobile\n50000  automobile\n\n[50000 rows x 1 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>label</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>frog</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>truck</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>truck</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>deer</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>automobile</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>49996</th>\n      <td>bird</td>\n    </tr>\n    <tr>\n      <th>49997</th>\n      <td>frog</td>\n    </tr>\n    <tr>\n      <th>49998</th>\n      <td>truck</td>\n    </tr>\n    <tr>\n      <th>49999</th>\n      <td>automobile</td>\n    </tr>\n    <tr>\n      <th>50000</th>\n      <td>automobile</td>\n    </tr>\n  </tbody>\n</table>\n<p>50000 rows × 1 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"x_train.max()","metadata":{"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"255"},"metadata":{}}]},{"cell_type":"code","source":"x_test.max()","metadata":{"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"255"},"metadata":{}}]},{"cell_type":"code","source":"# Normalization\nx_train = x_train / 255.0\nx_test = x_test / 255.0","metadata":{"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"plt.imshow(x_test[0])","metadata":{"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f4b0e053750>"},"metadata":{}},{"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":"y_train_cat = tf.keras.utils.to_categorical(y_train, NUM_CLASSES)","metadata":{"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"y_test_cat = tf.keras.utils.to_categorical(y_test, NUM_CLASSES)","metadata":{"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":"### Building model","metadata":{}},{"cell_type":"code","source":"model = tf.keras.models.Sequential([\n    tf.keras.layers.Conv2D(filters=32, kernel_size=KERNEL_SIZE, input_shape=INPUT_SHAPE, activation='relu'),\n    tf.keras.layers.MaxPooling2D(pool_size=POOL_SIZE),\n    tf.keras.layers.Conv2D(filters=64, kernel_size=KERNEL_SIZE, input_shape=INPUT_SHAPE, activation='relu'),\n    tf.keras.layers.MaxPooling2D(pool_size=POOL_SIZE),\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(512, activation='relu'),\n    tf.keras.layers.Dropout(0.25),\n    tf.keras.layers.Dense(256, activation='relu'),\n    tf.keras.layers.Dropout(0.25),\n    tf.keras.layers.Dense(128, activation='relu'),\n    tf.keras.layers.Dropout(0.25),\n    tf.keras.layers.Dense(64, activation='relu'),\n    tf.keras.layers.Dropout(0.25),\n    tf.keras.layers.Dense(32, activation='relu'),\n    tf.keras.layers.Dropout(0.25),\n    tf.keras.layers.Dense(10, activation='softmax'),\n])","metadata":{"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d (Conv2D)              (None, 31, 31, 32)        416       \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 15, 15, 32)        0         \n_________________________________________________________________\nconv2d_1 (Conv2D)            (None, 14, 14, 64)        8256      \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 7, 7, 64)          0         \n_________________________________________________________________\nflatten (Flatten)            (None, 3136)              0         \n_________________________________________________________________\ndense (Dense)                (None, 512)               1606144   \n_________________________________________________________________\ndropout (Dropout)            (None, 512)               0         \n_________________________________________________________________\ndense_1 (Dense)              (None, 256)               131328    \n_________________________________________________________________\ndropout_1 (Dropout)          (None, 256)               0         \n_________________________________________________________________\ndense_2 (Dense)              (None, 128)               32896     \n_________________________________________________________________\ndropout_2 (Dropout)          (None, 128)               0         \n_________________________________________________________________\ndense_3 (Dense)              (None, 64)                8256      \n_________________________________________________________________\ndropout_3 (Dropout)          (None, 64)                0         \n_________________________________________________________________\ndense_4 (Dense)              (None, 32)                2080      \n_________________________________________________________________\ndropout_4 (Dropout)          (None, 32)                0         \n_________________________________________________________________\ndense_5 (Dense)              (None, 10)                330       \n=================================================================\nTotal params: 1,789,706\nTrainable params: 1,789,706\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"model.compile(\n    loss='categorical_crossentropy',\n    optimizer='adam',\n    metrics=['acc']\n)","metadata":{"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"callbacks = [\n    tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=15) \n]","metadata":{"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"hist = model.fit(\n    x_train, y_train_cat, batch_size=64, epochs=25, callbacks=callbacks, validation_data=(x_test, y_test_cat)\n)","metadata":{"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Epoch 1/25\n782/782 [==============================] - 10s 7ms/step - loss: 2.0337 - acc: 0.2138 - val_loss: 1.3994 - val_acc: 0.4973\nEpoch 2/25\n782/782 [==============================] - 5s 6ms/step - loss: 1.4119 - acc: 0.4916 - val_loss: 1.1313 - val_acc: 0.6027\nEpoch 3/25\n782/782 [==============================] - 6s 7ms/step - loss: 1.1697 - acc: 0.5967 - val_loss: 1.0256 - val_acc: 0.6449\nEpoch 4/25\n782/782 [==============================] - 5s 6ms/step - loss: 1.0407 - acc: 0.6489 - val_loss: 1.0058 - val_acc: 0.6569\nEpoch 5/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.9359 - acc: 0.6875 - val_loss: 0.9460 - val_acc: 0.6774\nEpoch 6/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.8437 - acc: 0.7191 - val_loss: 0.9547 - val_acc: 0.6898\nEpoch 7/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.7761 - acc: 0.7432 - val_loss: 0.9257 - val_acc: 0.6996\nEpoch 8/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.7031 - acc: 0.7638 - val_loss: 0.9189 - val_acc: 0.7063\nEpoch 9/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.6500 - acc: 0.7867 - val_loss: 0.9363 - val_acc: 0.7065\nEpoch 10/25\n782/782 [==============================] - 5s 7ms/step - loss: 0.6013 - acc: 0.8032 - val_loss: 0.9596 - val_acc: 0.7110\nEpoch 11/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.5570 - acc: 0.8178 - val_loss: 0.9420 - val_acc: 0.7132\nEpoch 12/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.5077 - acc: 0.8345 - val_loss: 1.0124 - val_acc: 0.7041\nEpoch 13/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.4837 - acc: 0.8439 - val_loss: 1.0707 - val_acc: 0.7084\nEpoch 14/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.4394 - acc: 0.8582 - val_loss: 1.0595 - val_acc: 0.7156\nEpoch 15/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.4150 - acc: 0.8678 - val_loss: 1.0442 - val_acc: 0.7097\nEpoch 16/25\n782/782 [==============================] - 5s 7ms/step - loss: 0.3880 - acc: 0.8779 - val_loss: 1.0935 - val_acc: 0.7157\nEpoch 17/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.3659 - acc: 0.8847 - val_loss: 1.1246 - val_acc: 0.7094\nEpoch 18/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.3443 - acc: 0.8914 - val_loss: 1.1892 - val_acc: 0.7002\nEpoch 19/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.3197 - acc: 0.8985 - val_loss: 1.1740 - val_acc: 0.7122\nEpoch 20/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.3081 - acc: 0.9040 - val_loss: 1.2122 - val_acc: 0.7128\nEpoch 21/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.2887 - acc: 0.9113 - val_loss: 1.2954 - val_acc: 0.7086\nEpoch 22/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.2769 - acc: 0.9172 - val_loss: 1.2975 - val_acc: 0.7127\nEpoch 23/25\n782/782 [==============================] - 5s 6ms/step - loss: 0.2682 - acc: 0.9173 - val_loss: 1.2425 - val_acc: 0.7088\n","output_type":"stream"}]},{"cell_type":"code","source":"plt.plot(hist.history['loss'], label='Loss')\nplt.plot(hist.history['val_loss'], label='Validation Loss')\nplt.legend()\nplt.show()","metadata":{"trusted":true},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"plt.plot(hist.history['acc'], label='Acc')\nplt.plot(hist.history['val_acc'], label='Validation Acc')\nplt.legend()\nplt.show()","metadata":{"trusted":true},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"print(model.metrics_names)\nprint(model.evaluate(x_test,y_test_cat,verbose=0))","metadata":{"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"['loss', 'acc']\n[1.242469072341919, 0.7088000178337097]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Prediction","metadata":{}},{"cell_type":"code","source":"img_pred = x_test[155]\nplt.imshow(img_pred)","metadata":{"trusted":true},"execution_count":79,"outputs":[{"execution_count":79,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7f46347ccbd0>"},"metadata":{}},{"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":"img_pred = img_pred.reshape(1,32,32,3)\n\npredictions = np.argmax(model.predict(img_pred), axis=-1)[0]","metadata":{"trusted":true},"execution_count":80,"outputs":[]},{"cell_type":"code","source":"predictions","metadata":{"trusted":true},"execution_count":81,"outputs":[{"execution_count":81,"output_type":"execute_result","data":{"text/plain":"5"},"metadata":{}}]},{"cell_type":"code","source":"labels[predictions]","metadata":{"trusted":true},"execution_count":82,"outputs":[{"execution_count":82,"output_type":"execute_result","data":{"text/plain":"'dog'"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}