Keras/MNIST

mnist.py

from keras.datasets import mnist                                                 
from keras.models import Model                                                   
from keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D    
from keras.utils import np_utils                                                 
                                                                                 
(x_train, y_train), (x_test, y_test) = mnist.load_data()                         
                                                                                 
x_train = x_train.reshape(x_train.shape+(1,))                                    
x_test = x_test.reshape(x_test.shape+(1,))                                       
                                                                                 
x_train = x_train.astype('float32')/255                                          
x_test = x_test.astype('float32')/255                                            
y_train = np_utils.to_categorical(y_train, 10)                                   
y_test = np_utils.to_categorical(y_test, 10)                                                                                                                  
                                                                                 
inputs = Input(x_train.shape[1:])                                                
x = inputs                                                                       
x = Conv2D(32, (3, 3), activation='relu')(x)                                     
x = Conv2D(64, (3, 3), activation='relu')(x)                                     
x = MaxPooling2D(pool_size=(2, 2))(x)                                            
x = Dropout(0.25)(x)                                                             
x = Flatten()(x)                                                                 
x = Dense(128, activation='relu')(x)                                             
x = Dropout(0.5)(x)                                                              
x = Dense(10, activation='softmax')(x)                                           
outputs = x                                                                      
                                                                                 
model = Model(inputs=inputs, outputs=outputs)                                    
                                                                                 
model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=['accuracy'])
                                                                                      
model.fit(x_train, y_train, batch_size=128, epochs=10, verbose=1,                      
          validation_data=(x_test, y_test))                                      
score = model.evaluate(x_test, y_test, verbose=0)                                
                                                                                 
print 'loss:', score[0], 'accuracy:', score[1]