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- import numpy as np
- from keras.layers import MaxPool2D,Conv2D,Dense,Flatten,Dropout
- from keras.models import Sequential
- from keras.utils import np_utils
- from keras.layers import activations
- from keras.datasets import mnist
- np.random.seed(1)
- (x_train,y_train),(x_test,y_test)=mnist.load_data()
- x_train = x_train.reshape(x_train.shape[0], 28,28,1).astype('float32')
- x_test = x_test.reshape(x_test.shape[0], 28, 28,1).astype('float32')
- x_train=x_train.astype('float32')
- x_tset=x_test.astype('float32')
- x_train=x_train/255
- x_test=x_test/255
- y_train=np_utils.to_categorical(y_train,10)
- y_test=np_utils.to_categorical(y_test,10)
- model=Sequential()
- model.add(Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)))
- model.add(MaxPool2D(pool_size= (2,2)))
- model.add(Dropout(0.25))
- model.add(Conv2D(32,(3,3), activation='relu',input_shape=(28,28,1)))
- model.add(MaxPool2D(pool_size=(2,2)))
- model.add(Dropout(0.25))
- model.add(Flatten())
- model.add(Dense(units=128,activation='relu'))
- model.add(Dropout(0.5))
- model.add(Dense(units=10,activation='softmax'))
- model.compile(optimizer='adam',loss='binary_crossentropy',metrics= ['accuracy'])
- model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, batch_size=200, verbose=2)
- model.evaluate(x_test,y_test,verbose=0)
- model.save('hand_written.h5')
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