mnist-digit-classification-with-99-7-accuracy.ipynb

{"cells":[{"metadata":{},"cell_type":"markdown","source":"### MNIST Image Classification on Digits\n* In this project I am using MNIST dataset of digits images to classify each image into a class of digits 0-9.\n* KAGGLE LINK: https://www.kaggle.com/c/digit-recognizer"},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n\nimport seaborn as sns\n\nimport matplotlib.pyplot as plt\n%matplotlib inline","execution_count":1,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","execution_count":2,"outputs":[{"output_type":"stream","text":"/kaggle/input/digit-recognizer/sample_submission.csv\n/kaggle/input/digit-recognizer/test.csv\n/kaggle/input/digit-recognizer/train.csv\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"mnist_train = pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")\nmnist_test = pd.read_csv(\"/kaggle/input/digit-recognizer/test.csv\")","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(mnist_train.shape, mnist_test.shape)","execution_count":4,"outputs":[{"output_type":"stream","text":"(42000, 785) (28000, 784)\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"mnist_train.head()","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n0      1       0       0       0       0       0       0       0       0   \n1      0       0       0       0       0       0       0       0       0   \n2      1       0       0       0       0       0       0       0       0   \n3      4       0       0       0       0       0       0       0       0   \n4      0       0       0       0       0       0       0       0       0   \n\n   pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n0       0  ...         0         0         0         0         0         0   \n1       0  ...         0         0         0         0         0         0   \n2       0  ...         0         0         0         0         0         0   \n3       0  ...         0         0         0         0         0         0   \n4       0  ...         0         0         0         0         0         0   \n\n   pixel780  pixel781  pixel782  pixel783  \n0         0         0         0         0  \n1         0         0         0         0  \n2         0         0         0         0  \n3         0         0         0         0  \n4         0         0         0         0  \n\n[5 rows x 785 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      <th>pixel0</th>\n      <th>pixel1</th>\n      <th>pixel2</th>\n      <th>pixel3</th>\n      <th>pixel4</th>\n      <th>pixel5</th>\n      <th>pixel6</th>\n      <th>pixel7</th>\n      <th>pixel8</th>\n      <th>...</th>\n      <th>pixel774</th>\n      <th>pixel775</th>\n      <th>pixel776</th>\n      <th>pixel777</th>\n      <th>pixel778</th>\n      <th>pixel779</th>\n      <th>pixel780</th>\n      <th>pixel781</th>\n      <th>pixel782</th>\n      <th>pixel783</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 785 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"mnist_train.describe()","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"              label   pixel0   pixel1   pixel2   pixel3   pixel4   pixel5  \\\ncount  42000.000000  42000.0  42000.0  42000.0  42000.0  42000.0  42000.0   \nmean       4.456643      0.0      0.0      0.0      0.0      0.0      0.0   \nstd        2.887730      0.0      0.0      0.0      0.0      0.0      0.0   \nmin        0.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n25%        2.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n50%        4.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n75%        7.000000      0.0      0.0      0.0      0.0      0.0      0.0   \nmax        9.000000      0.0      0.0      0.0      0.0      0.0      0.0   \n\n        pixel6   pixel7   pixel8  ...      pixel774      pixel775  \\\ncount  42000.0  42000.0  42000.0  ...  42000.000000  42000.000000   \nmean       0.0      0.0      0.0  ...      0.219286      0.117095   \nstd        0.0      0.0      0.0  ...      6.312890      4.633819   \nmin        0.0      0.0      0.0  ...      0.000000      0.000000   \n25%        0.0      0.0      0.0  ...      0.000000      0.000000   \n50%        0.0      0.0      0.0  ...      0.000000      0.000000   \n75%        0.0      0.0      0.0  ...      0.000000      0.000000   \nmax        0.0      0.0      0.0  ...    254.000000    254.000000   \n\n           pixel776     pixel777      pixel778      pixel779  pixel780  \\\ncount  42000.000000  42000.00000  42000.000000  42000.000000   42000.0   \nmean       0.059024      0.02019      0.017238      0.002857       0.0   \nstd        3.274488      1.75987      1.894498      0.414264       0.0   \nmin        0.000000      0.00000      0.000000      0.000000       0.0   \n25%        0.000000      0.00000      0.000000      0.000000       0.0   \n50%        0.000000      0.00000      0.000000      0.000000       0.0   \n75%        0.000000      0.00000      0.000000      0.000000       0.0   \nmax      253.000000    253.00000    254.000000     62.000000       0.0   \n\n       pixel781  pixel782  pixel783  \ncount   42000.0   42000.0   42000.0  \nmean        0.0       0.0       0.0  \nstd         0.0       0.0       0.0  \nmin         0.0       0.0       0.0  \n25%         0.0       0.0       0.0  \n50%         0.0       0.0       0.0  \n75%         0.0       0.0       0.0  \nmax         0.0       0.0       0.0  \n\n[8 rows x 785 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      <th>pixel0</th>\n      <th>pixel1</th>\n      <th>pixel2</th>\n      <th>pixel3</th>\n      <th>pixel4</th>\n      <th>pixel5</th>\n      <th>pixel6</th>\n      <th>pixel7</th>\n      <th>pixel8</th>\n      <th>...</th>\n      <th>pixel774</th>\n      <th>pixel775</th>\n      <th>pixel776</th>\n      <th>pixel777</th>\n      <th>pixel778</th>\n      <th>pixel779</th>\n      <th>pixel780</th>\n      <th>pixel781</th>\n      <th>pixel782</th>\n      <th>pixel783</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>42000.000000</td>\n      <td>42000.0</td>\n      <td>42000.0</td>\n      <td>42000.0</td>\n      <td>42000.0</td>\n      <td>42000.0</td>\n      <td>42000.0</td>\n      <td>42000.0</td>\n      <td>42000.0</td>\n      <td>42000.0</td>\n      <td>...</td>\n      <td>42000.000000</td>\n      <td>42000.000000</td>\n      <td>42000.000000</td>\n      <td>42000.00000</td>\n      <td>42000.000000</td>\n      <td>42000.000000</td>\n      <td>42000.0</td>\n      <td>42000.0</td>\n      <td>42000.0</td>\n      <td>42000.0</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>4.456643</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.219286</td>\n      <td>0.117095</td>\n      <td>0.059024</td>\n      <td>0.02019</td>\n      <td>0.017238</td>\n      <td>0.002857</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.887730</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>6.312890</td>\n      <td>4.633819</td>\n      <td>3.274488</td>\n      <td>1.75987</td>\n      <td>1.894498</td>\n      <td>0.414264</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>4.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>7.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>9.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>254.000000</td>\n      <td>254.000000</td>\n      <td>253.000000</td>\n      <td>253.00000</td>\n      <td>254.000000</td>\n      <td>62.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 785 columns</p>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Data Cleaning and Normalization\nData is totally clean in this case."},{"metadata":{"trusted":true},"cell_type":"code","source":"mnist_train.isna().any().any()\n# There is no empty field. Data is clean already.","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"False"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nmnist_train_data = mnist_train.loc[:, \"pixel0\":]\nmnist_train_label = mnist_train.loc[:, \"label\"]\n\n# Data Normalization\nmnist_train_data = mnist_train_data/255.0\nmnist_test = mnist_test/255.0","execution_count":8,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Visulaize a single digit"},{"metadata":{"trusted":true},"cell_type":"code","source":"digit_array = mnist_train.loc[3, \"pixel0\":]\narr = np.array(digit_array) #.reshape(a, (28,28))\nimage_array = np.reshape(arr, (28,28))","execution_count":9,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"digit_img = plt.imshow(image_array, cmap=plt.cm.binary)\nplt.colorbar(digit_img)\nprint(\"IMAGE LABEL: {}\".format(mnist_train.loc[3, \"label\"]))","execution_count":10,"outputs":[{"output_type":"stream","text":"IMAGE LABEL: 4\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 2 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.countplot(mnist_train.label)\nprint(list(mnist_train.label.value_counts().sort_index()))","execution_count":11,"outputs":[{"output_type":"stream","text":"[4132, 4684, 4177, 4351, 4072, 3795, 4137, 4401, 4063, 4188]\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Converting dataframe into arrays\nmnist_train_data = np.array(mnist_train_data)\nmnist_train_label = np.array(mnist_train_label)","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mnist_train_data = mnist_train_data.reshape(mnist_train_data.shape[0], 28, 28, 1)\nprint(mnist_train_data.shape, mnist_train_label.shape)","execution_count":13,"outputs":[{"output_type":"stream","text":"(42000, 28, 28, 1) (42000,)\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Training data\nTraining a neural network with one input layer, one hidden layer and one output layer for learning the digits in images."},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow import keras\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Lambda, Flatten, BatchNormalization\nfrom tensorflow.keras.layers import Conv2D, MaxPool2D, AvgPool2D\nfrom tensorflow.keras.optimizers import Adadelta\nfrom keras.utils.np_utils import to_categorical\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau\nfrom tensorflow.keras.callbacks import LearningRateScheduler","execution_count":14,"outputs":[{"output_type":"stream","text":"Using TensorFlow backend.\n","name":"stderr"}]},{"metadata":{},"cell_type":"markdown","source":"### Encoding train labels"},{"metadata":{"trusted":true},"cell_type":"code","source":"nclasses = mnist_train_label.max() - mnist_train_label.min() + 1\nmnist_train_label = to_categorical(mnist_train_label, num_classes = nclasses)\nprint(\"Shape of ytrain after encoding: \", mnist_train_label.shape)","execution_count":15,"outputs":[{"output_type":"stream","text":"Shape of ytrain after encoding:  (42000, 10)\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Building Model"},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv2D(32, kernel_size = 3, activation='relu', input_shape = (28, 28, 1)))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(32, kernel_size = 3, activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(32, kernel_size = 5, strides=2, padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.4))\n\nmodel.add(Conv2D(64, kernel_size = 3, activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(64, kernel_size = 3, activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(64, kernel_size = 5, strides=2, padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.4))\n\nmodel.add(Conv2D(128, kernel_size = 4, activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Flatten())\nmodel.add(Dropout(0.4))\nmodel.add(Dense(10, activation='softmax'))\nmodel.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_history = model.fit(mnist_train_data, mnist_train_label, shuffle=True, epochs=50, validation_split=0.2)","execution_count":17,"outputs":[{"output_type":"stream","text":"Epoch 1/50\n1050/1050 [==============================] - 7s 7ms/step - loss: 0.2534 - accuracy: 0.9208 - val_loss: 0.0929 - val_accuracy: 0.9721\nEpoch 2/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0850 - accuracy: 0.9745 - val_loss: 0.0461 - val_accuracy: 0.9846\nEpoch 3/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0658 - accuracy: 0.9798 - val_loss: 0.0340 - val_accuracy: 0.9896\nEpoch 4/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0583 - accuracy: 0.9819 - val_loss: 0.0417 - val_accuracy: 0.9885\nEpoch 5/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0523 - accuracy: 0.9841 - val_loss: 0.0337 - val_accuracy: 0.9901\nEpoch 6/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0459 - accuracy: 0.9860 - val_loss: 0.0270 - val_accuracy: 0.9919\nEpoch 7/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0401 - accuracy: 0.9882 - val_loss: 0.0282 - val_accuracy: 0.9914\nEpoch 8/50\n1050/1050 [==============================] - 7s 7ms/step - loss: 0.0375 - accuracy: 0.9889 - val_loss: 0.0307 - val_accuracy: 0.9924\nEpoch 9/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0358 - accuracy: 0.9887 - val_loss: 0.0357 - val_accuracy: 0.9899\nEpoch 10/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0332 - accuracy: 0.9896 - val_loss: 0.0309 - val_accuracy: 0.9919\nEpoch 11/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0313 - accuracy: 0.9903 - val_loss: 0.0223 - val_accuracy: 0.9942\nEpoch 12/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0273 - accuracy: 0.9913 - val_loss: 0.0258 - val_accuracy: 0.9936\nEpoch 13/50\n1050/1050 [==============================] - 7s 7ms/step - loss: 0.0254 - accuracy: 0.9927 - val_loss: 0.0237 - val_accuracy: 0.9949\nEpoch 14/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0218 - accuracy: 0.9932 - val_loss: 0.0269 - val_accuracy: 0.9936\nEpoch 15/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0227 - accuracy: 0.9921 - val_loss: 0.0226 - val_accuracy: 0.9952\nEpoch 16/50\n1050/1050 [==============================] - 7s 7ms/step - loss: 0.0211 - accuracy: 0.9934 - val_loss: 0.0259 - val_accuracy: 0.9936\nEpoch 17/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0190 - accuracy: 0.9943 - val_loss: 0.0210 - val_accuracy: 0.9944\nEpoch 18/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0153 - accuracy: 0.9947 - val_loss: 0.0295 - val_accuracy: 0.9927\nEpoch 19/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0193 - accuracy: 0.9938 - val_loss: 0.0258 - val_accuracy: 0.9943\nEpoch 20/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0154 - accuracy: 0.9950 - val_loss: 0.0271 - val_accuracy: 0.9942\nEpoch 21/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0160 - accuracy: 0.9951 - val_loss: 0.0300 - val_accuracy: 0.9930\nEpoch 22/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0170 - accuracy: 0.9944 - val_loss: 0.0275 - val_accuracy: 0.9942\nEpoch 23/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0130 - accuracy: 0.9957 - val_loss: 0.0326 - val_accuracy: 0.9931\nEpoch 24/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0145 - accuracy: 0.9953 - val_loss: 0.0267 - val_accuracy: 0.9944\nEpoch 25/50\n1050/1050 [==============================] - 7s 7ms/step - loss: 0.0146 - accuracy: 0.9954 - val_loss: 0.0269 - val_accuracy: 0.9950\nEpoch 26/50\n1050/1050 [==============================] - 7s 7ms/step - loss: 0.0134 - accuracy: 0.9960 - val_loss: 0.0238 - val_accuracy: 0.9950\nEpoch 27/50\n1050/1050 [==============================] - 7s 7ms/step - loss: 0.0115 - accuracy: 0.9965 - val_loss: 0.0271 - val_accuracy: 0.9949\nEpoch 28/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0116 - accuracy: 0.9962 - val_loss: 0.0276 - val_accuracy: 0.9946\nEpoch 29/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0118 - accuracy: 0.9962 - val_loss: 0.0266 - val_accuracy: 0.9939\nEpoch 30/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0111 - accuracy: 0.9968 - val_loss: 0.0270 - val_accuracy: 0.9945\nEpoch 31/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0090 - accuracy: 0.9971 - val_loss: 0.0274 - val_accuracy: 0.9943\nEpoch 32/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0091 - accuracy: 0.9973 - val_loss: 0.0264 - val_accuracy: 0.9951\nEpoch 33/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0084 - accuracy: 0.9976 - val_loss: 0.0215 - val_accuracy: 0.9961\nEpoch 34/50\n1050/1050 [==============================] - 7s 7ms/step - loss: 0.0093 - accuracy: 0.9972 - val_loss: 0.0288 - val_accuracy: 0.9944\nEpoch 35/50\n1050/1050 [==============================] - 7s 7ms/step - loss: 0.0102 - accuracy: 0.9968 - val_loss: 0.0293 - val_accuracy: 0.9942\nEpoch 36/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0100 - accuracy: 0.9967 - val_loss: 0.0296 - val_accuracy: 0.9943\nEpoch 37/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0083 - accuracy: 0.9970 - val_loss: 0.0249 - val_accuracy: 0.9949\nEpoch 38/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0093 - accuracy: 0.9971 - val_loss: 0.0326 - val_accuracy: 0.9927\nEpoch 39/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0081 - accuracy: 0.9975 - val_loss: 0.0272 - val_accuracy: 0.9952\nEpoch 40/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0065 - accuracy: 0.9977 - val_loss: 0.0273 - val_accuracy: 0.9946\nEpoch 41/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0069 - accuracy: 0.9979 - val_loss: 0.0307 - val_accuracy: 0.9944\nEpoch 42/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0074 - accuracy: 0.9976 - val_loss: 0.0291 - val_accuracy: 0.9950\nEpoch 43/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0060 - accuracy: 0.9979 - val_loss: 0.0308 - val_accuracy: 0.9945\nEpoch 44/50\n1050/1050 [==============================] - 7s 7ms/step - loss: 0.0112 - accuracy: 0.9965 - val_loss: 0.0273 - val_accuracy: 0.9945\nEpoch 45/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0057 - accuracy: 0.9982 - val_loss: 0.0274 - val_accuracy: 0.9949\nEpoch 46/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0059 - accuracy: 0.9980 - val_loss: 0.0312 - val_accuracy: 0.9942\nEpoch 47/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0070 - accuracy: 0.9975 - val_loss: 0.0255 - val_accuracy: 0.9957\nEpoch 48/50\n1050/1050 [==============================] - 6s 6ms/step - loss: 0.0068 - accuracy: 0.9979 - val_loss: 0.0305 - val_accuracy: 0.9944\nEpoch 49/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0046 - accuracy: 0.9985 - val_loss: 0.0292 - val_accuracy: 0.9954\nEpoch 50/50\n1050/1050 [==============================] - 7s 6ms/step - loss: 0.0064 - accuracy: 0.9979 - val_loss: 0.0322 - val_accuracy: 0.9938\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Model Performance Plots"},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(model_history.history['accuracy'],label = 'ACCURACY')\nplt.plot(model_history.history['val_accuracy'],label = 'VALIDATION ACCURACY')\nplt.legend()","execution_count":24,"outputs":[{"output_type":"execute_result","execution_count":24,"data":{"text/plain":"<matplotlib.legend.Legend at 0x7ff8304a6e10>"},"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"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(model_history.history['loss'],label = 'TRAINING LOSS')\nplt.plot(model_history.history['val_loss'],label = 'VALIDATION LOSS')\nplt.legend()","execution_count":25,"outputs":[{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"<matplotlib.legend.Legend at 0x7ff85c084910>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"### Transforming testing data"},{"metadata":{"trusted":true},"cell_type":"code","source":"mnist_test_arr = np.array(mnist_test)\nmnist_test_arr = mnist_test_arr.reshape(mnist_test_arr.shape[0], 28, 28, 1)\nprint(mnist_test_arr.shape)","execution_count":26,"outputs":[{"output_type":"stream","text":"(28000, 28, 28, 1)\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Prediction & Submission"},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions = model.predict(mnist_test_arr)","execution_count":27,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions_test = []\n\nfor i in predictions:\n    predictions_test.append(np.argmax(i))","execution_count":28,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission =  pd.DataFrame({\n        \"ImageId\": mnist_test.index+1,\n        \"Label\": predictions_test\n    })\n\nsubmission.to_csv('my_submission.csv', index=False)","execution_count":29,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Please upvote the notebook if you like this simple approach :)"}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}