In [2]:

    

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop
from keras.callbacks import EarlyStopping, CSVLogger

import matplotlib.pyplot as plt


    

    




Using TensorFlow backend.

In [2]:

    

batch_size = 128
num_classes = 10
epochs = 20


    

In [3]:

    

(x_train, y_train), (x_test, y_test) = mnist.load_data()


    

In [4]:

    

x_train = x_train.reshape(60000, 784).astype('float32')
x_test = x_test.reshape(10000, 784).astype('float32')
x_train /= 255
x_test /= 255


    

In [5]:

    

print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)


    

    




(60000, 784)
(60000,)
(10000, 784)
(10000,)

In [6]:

    

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)


    

In [7]:

    

print(y_train.shape)
print(y_test.shape)


    

    




(60000, 10)
(10000, 10)

In [20]:

    

model = Sequential()
model.add(Dense(512, input_shape=(784, )))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))


    

In [9]:

    

model.summary()


    

    




_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 512)               401920    
_________________________________________________________________
activation_1 (Activation)    (None, 512)               0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 512)               262656    
_________________________________________________________________
activation_2 (Activation)    (None, 512)               0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_3 (Dense)              (None, 10)                5130      
_________________________________________________________________
activation_3 (Activation)    (None, 10)                0         
=================================================================
Total params: 669,706
Trainable params: 669,706
Non-trainable params: 0
_________________________________________________________________

In [21]:

    

model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])


    

In [30]:

    

es = EarlyStopping(monitor='val_loss', patience=2)
cvs_logger = CSVLogger('training.log')
hist = model.fit(x_train, y_train,
                 batch_size=batch_size,
                 epochs=epochs,
                 verbose=1,
                 validation_split=0.1,
                 callbacks=[es, cvs_logger])


    

    




Train on 54000 samples, validate on 6000 samples
Epoch 1/20
54000/54000 [==============================] - 7s - loss: 0.0402 - acc: 0.9879 - val_loss: 0.0785 - val_acc: 0.9813
Epoch 2/20
54000/54000 [==============================] - 8s - loss: 0.0339 - acc: 0.9899 - val_loss: 0.0811 - val_acc: 0.9837
Epoch 3/20
54000/54000 [==============================] - 8s - loss: 0.0314 - acc: 0.9911 - val_loss: 0.0789 - val_acc: 0.9817
Epoch 4/20
54000/54000 [==============================] - 8s - loss: 0.0286 - acc: 0.9920 - val_loss: 0.0939 - val_acc: 0.9822

In [31]:

    

%cat training.log


    

In [40]:

    

score = model.evaluate(x_test, y_test, verbose=0)

print('test loss:', score[0])
print('test acc:', score[1])


    

    




test loss: 0.0982232531704
test acc: 0.9819

In [39]:

    

loss = hist.history['loss']
val_loss = hist.history['val_loss']

epochs = len(loss)
plt.plot(range(epochs), loss, marker='.', label='acc')
plt.plot(range(epochs), val_loss, marker='.', label='val_acc')
plt.legend(loc='best')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('acc')
plt.show()


    

In [42]:

    

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import keras.utils


    

In [6]:

    

batch_size = 128
num_classes = 10
epochs = 12


    

In [7]:

    

img_rows, img_cols = 28, 28


    

In [8]:

    

(x_train, y_train), (x_test, y_test) = mnist.load_data()


    

In [9]:

    

print(x_train.shape)


    

    




(60000, 28, 28)

In [10]:

    

x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)


    

In [11]:

    

x_train.shape


    

    
Out[11]:





(60000, 28, 28, 1)

In [12]:

    

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')


    

In [13]:

    

x_train /= 255
x_test /= 255


    

In [41]:

    

x_test.shape


    

    
Out[41]:





(10000, 28, 28, 1)

In [43]:

    

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)


    

In [34]:

    

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(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(num_classes, activation='softmax'))


    

In [35]:

    

model.summary()


    

    




_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_11 (Conv2D)           (None, 26, 26, 32)        320       
_________________________________________________________________
conv2d_12 (Conv2D)           (None, 24, 24, 64)        18496     
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 12, 12, 64)        0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 12, 12, 64)        0         
_________________________________________________________________
flatten_2 (Flatten)          (None, 9216)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 128)               1179776   
_________________________________________________________________
dropout_3 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 10)                1290      
=================================================================
Total params: 1,199,882
Trainable params: 1,199,882
Non-trainable params: 0
_________________________________________________________________

In [36]:

    

model.layers


    

    
Out[36]:





[<keras.layers.convolutional.Conv2D at 0x12eb6c0b8>,
 <keras.layers.convolutional.Conv2D at 0x12eb7eda0>,
 <keras.layers.pooling.MaxPooling2D at 0x12eb7ec18>,
 <keras.layers.core.Dropout at 0x12eb6c7f0>,
 <keras.layers.core.Flatten at 0x12eb99ba8>,
 <keras.layers.core.Dense at 0x12ebad9b0>,
 <keras.layers.core.Dropout at 0x12ebadbe0>,
 <keras.layers.core.Dense at 0x12ebbed30>]

In [38]:

    

model.layers[0].name


    

    
Out[38]:





'conv2d_11'

In [39]:

    

model.compile(loss=keras.losses.categorical_crossentropy,
             optimizer=keras.optimizers.Adadelta(),
             metrics=['accuracy'])


    

In [45]:

    

model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=1,
          verbose=1,
          validation_data=(x_test, y_test))


    

    




Train on 60000 samples, validate on 10000 samples
Epoch 1/1
60000/60000 [==============================] - 334s - loss: 0.2622 - acc: 0.9215 - val_loss: 0.0754 - val_acc: 0.9757






    
Out[45]:





<keras.callbacks.History at 0x12f66e9b0>

In [ ]: