In [5]:

    

import numpy as np
np.random.seed(123)  # for reproducibility
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, pooling
from keras.utils import np_utils
from keras.datasets import mnist


    

In [6]:

    

(X_train, y_train), (X_test, y_test) = mnist.load_data()
print(X_train.shape)


    

    




(60000, 28, 28)

In [7]:

    

X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
print(X_train.shape)


    

    




(60000, 28, 28, 1)

In [8]:

    

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
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)))
print(model.output_shape)


    

    




(None, 26, 26, 32)






    




/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:10: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation="relu", input_shape=(28, 28, 1...)`
  # Remove the CWD from sys.path while we load stuff.

In [9]:

    

model.add(Conv2D(32, 3, 3, activation='relu'))
model.add(pooling.MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
              
model.fit(X_train, Y_train, batch_size=32, nb_epoch=10, verbose=1)

score = model.evaluate(X_test, Y_test, verbose=0)
print(model.metrics_names)
print(score)


    

    




/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), activation="relu")`
  """Entry point for launching an IPython kernel.
/usr/local/lib/python3.6/site-packages/ipykernel_launcher.py:12: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.
  if sys.path[0] == '':






    




Epoch 1/10
60000/60000 [==============================] - 70s 1ms/step - loss: 0.2088 - acc: 0.9363
Epoch 2/10
60000/60000 [==============================] - 70s 1ms/step - loss: 0.0876 - acc: 0.9734
Epoch 3/10
60000/60000 [==============================] - 69s 1ms/step - loss: 0.0652 - acc: 0.9801
Epoch 4/10
60000/60000 [==============================] - 65s 1ms/step - loss: 0.0551 - acc: 0.9834
Epoch 5/10
60000/60000 [==============================] - 67s 1ms/step - loss: 0.0467 - acc: 0.9863
Epoch 6/10
60000/60000 [==============================] - 62s 1ms/step - loss: 0.0411 - acc: 0.9878
Epoch 7/10
60000/60000 [==============================] - 62s 1ms/step - loss: 0.0373 - acc: 0.9879
Epoch 8/10
60000/60000 [==============================] - 62s 1ms/step - loss: 0.0348 - acc: 0.9885
Epoch 9/10
60000/60000 [==============================] - 64s 1ms/step - loss: 0.0317 - acc: 0.9903
Epoch 10/10
60000/60000 [==============================] - 66s 1ms/step - loss: 0.0276 - acc: 0.9911
['loss', 'acc']
[0.026656976491195382, 0.9917]

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