{"cells":[{"metadata":{},"cell_type":"markdown","source":"**>Author: Kazi Amit Hasan**\n\n\n\n\n\nThe documentatiosns will be added soon. Feel free to give me with feedbacks.\n\n\n> Please upvote if you like it.\n\n\n"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport seaborn as sns\n%matplotlib inline\n\nnp.random.seed(2)\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\nimport itertools\n\nfrom keras.utils.np_utils import to_categorical # convert to one-hot-encoding\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\nfrom keras.optimizers import RMSprop\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ReduceLROnPlateau\n\n\nsns.set(style='white', context='notebook', palette='deep')","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"#Loading the dataset\n\ntrain = pd.read_csv(\"../input/digit-recognizer/train.csv\")\ntest = pd.read_csv(\"../input/digit-recognizer/test.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = (train.iloc[:,1:].values).astype('float32') # all pixel values\ny_train = train.iloc[:,0].values.astype('int32') # only labels i.e targets digits\nX_test = test.values.astype('float32')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Convert train datset to (num_images, img_rows, img_cols) format \nX_train = X_train.reshape(X_train.shape[0], 28, 28)\n\nfor i in range(0, 9):\n    plt.subplot(330 + (i+1))\n    plt.imshow(X_train[i], cmap=plt.get_cmap('gray'))\n    plt.title(y_train[i]);","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = X_train.reshape(X_train.shape[0], 28, 28,1)\nX_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_test = X_test.reshape(X_test.shape[0], 28, 28,1)\nX_test.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mean_px = X_train.mean().astype(np.float32)\nstd_px = X_train.std().astype(np.float32)\n\ndef standardize(x): \n    return (x-mean_px)/std_px","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.utils.np_utils import to_categorical\ny_train= to_categorical(y_train)\nnum_classes = y_train.shape[1]\nnum_classes","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.models import  Sequential\nfrom keras.layers.core import  Lambda , Dense, Flatten, Dropout\nfrom keras.callbacks import EarlyStopping\nfrom keras.layers import BatchNormalization, Convolution2D , MaxPooling2D","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model= Sequential()\nmodel.add(Lambda(standardize,input_shape=(28,28,1)))\nmodel.add(Flatten())\nmodel.add(Dense(10, activation='softmax'))\nprint(\"input shape \",model.input_shape)\nprint(\"output shape \",model.output_shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.preprocessing import image\ngen = image.ImageDataGenerator()\nfrom sklearn.model_selection import train_test_split\nX = X_train\ny = y_train\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.10, random_state=42)\nbatches = gen.flow(X_train, y_train, batch_size=64)\nval_batches=gen.flow(X_val, y_val, batch_size=64)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = X_train\ny = y_train\nX_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.10, random_state=42)\nbatches = gen.flow(X_train, y_train, batch_size=64)\nval_batches=gen.flow(X_val, y_val, batch_size=64)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.optimizers import RMSprop\nmodel.compile(optimizer=RMSprop(lr=0.001),\n loss='categorical_crossentropy',\n metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history=model.fit_generator(generator=batches, steps_per_epoch=batches.n, epochs=7, \n                    validation_data=val_batches, validation_steps=val_batches.n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history_dict = history.history\nhistory_dict.keys()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.layers import Convolution2D, MaxPooling2D\n\ndef get_cnn_model():\n    model = Sequential([\n        Lambda(standardize, input_shape=(28,28,1)),\n        Convolution2D(32,(3,3), activation='relu'),\n        Convolution2D(64,(3,3), activation='relu'),\n        MaxPooling2D(),\n        Convolution2D(64,(3,3), activation='relu'),\n        Convolution2D(128,(3,3), activation='relu'),\n        MaxPooling2D(),\n        Flatten(),\n        Dense(512, activation='relu'),\n        Dense(10, activation='softmax')\n        ])\n    model.compile(Adam(), loss='categorical_crossentropy',\n                  metrics=['accuracy'])\n    return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history=model.fit_generator(generator=batches, steps_per_epoch=batches.n, epochs=7, \n                    validation_data=val_batches, validation_steps=val_batches.n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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}