{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"from keras.datasets import mnist\n(xtrain,ytrain),(xtest,ytest) = mnist.load_data()\n\nxtrain = xtrain.reshape(60000,28,28,1)\nxtest = xtest.reshape(10000,28,28,1)\n\nfrom keras.utils import to_categorical\nytrain = to_categorical(ytrain)\nytest = to_categorical(ytest)\n\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D, Dense , Flatten\nmodel = Sequential()\n\nmodel.add(Conv2D(64, kernel_size = 3 , activation = 'relu', input_shape = (28,28,1)))\nmodel.add(Conv2D(32, kernel_size = 3 , activation = 'relu'))\nmodel.add(Flatten())\nmodel.add(Dense(10, activation = 'sigmoid'))\n\nmodel.compile(optimizer = 'adam',loss = 'categorical_crossentropy' , metrics = ['accuracy'])\n\nmodel.fit(xtrain, ytrain, validation_data = (xtest, ytest), epochs = 3)\n\nmodel.predict(xtest[:4])\n ","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":1}