In [0]:

    




    

In [0]:

    

from keras.datasets import mnist


    

In [0]:

    

(train_images,train_labels),(test_images,test_labels)=mnist.load_data()


    

In [28]:

    

train_images.shape


    

    
Out[28]:





(60000, 28, 28)

In [29]:

    

train_images[1,:,:]


    

    
Out[29]:





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          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
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          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
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          0,  48, 238, 252, 252, 252, 237,   0,   0,   0,   0,   0,   0,
          0,   0],
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         54, 227, 253, 252, 239, 233, 252,  57,   6,   0,   0,   0,   0,
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        252, 252, 253, 252, 252,  96, 189, 253, 167,   0,   0,   0,   0,
          0,   0],
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        253, 190, 114, 253, 228,  47,  79, 255, 168,   0,   0,   0,   0,
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       [  0,   0,   0,   0,   0,   0,   0,   0,   0,  48, 238, 252, 252,
        179,  12,  75, 121,  21,   0,   0, 253, 243,  50,   0,   0,   0,
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         28,   0,   0,   0,   0,   0,   0, 253, 252, 195,   0,   0,   0,
          0,   0],
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          0,   0,   0,   0,   0,   0,   0, 255, 253, 196,   0,   0,   0,
          0,   0],
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          0,   0,   0,   0,   0,   0,   0, 253, 252, 148,   0,   0,   0,
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          0,   0,   0,   0,   7, 131, 252, 225,  71,   0,   0,   0,   0,
          0,   0],
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          0,   0,   0,  48, 165, 252, 173,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,  86, 253, 225,   0,   0,   0,   0,
          0,   0, 114, 238, 253, 162,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
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        178, 225, 253, 223, 167,  56,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,  85, 252, 252, 252, 229, 215, 252,
        252, 252, 196, 130,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,  28, 199, 252, 252, 253, 252, 252,
        233, 145,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,   0,  25, 128, 252, 253, 252, 141,
         37,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0],
       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,
          0,   0]], dtype=uint8)

In [0]:

    

from keras import models


    

In [0]:

    

from keras import layers


    

In [0]:

    

network=models.Sequential()


    

In [0]:

    

network.add( layers.Dense(512,activation='relu',input_shape=(28*28,) ) )


    

In [0]:

    

network.add( layers.Dense(10,activation='softmax' ) )


    

In [0]:

    

network.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])


    

In [0]:

    

train_images=train_images.reshape((60000,28*28))
train_images=train_images.astype('float32')/255


    

In [0]:

    

test_images=test_images.reshape((10000,28*28))
test_images=test_images.astype('float32')/255


    

In [0]:

    

from keras.utils import to_categorical


    

In [0]:

    

train_labels=to_categorical(train_labels)


    

In [0]:

    

test_labels=to_categorical(test_labels)


    

In [59]:

    

test_labels.shape


    

    
Out[59]:





(10000, 10)

In [60]:

    

train_labels.shape


    

    
Out[60]:





(60000, 10)

In [61]:

    

network.fit(train_images,train_labels,epochs=5,batch_size=128)


    

    




W0718 05:30:12.568123 139632671053696 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
W0718 05:30:12.719707 139632671053696 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.







    




Epoch 1/5
60000/60000 [==============================] - 6s 96us/step - loss: 0.2570 - acc: 0.9251
Epoch 2/5
60000/60000 [==============================] - 2s 35us/step - loss: 0.1037 - acc: 0.9687
Epoch 3/5
60000/60000 [==============================] - 2s 35us/step - loss: 0.0690 - acc: 0.9795
Epoch 4/5
60000/60000 [==============================] - 2s 35us/step - loss: 0.0507 - acc: 0.9846
Epoch 5/5
60000/60000 [==============================] - 2s 35us/step - loss: 0.0382 - acc: 0.9890






    
Out[61]:





<keras.callbacks.History at 0x7efeaad47438>

In [62]:

    

test_loss,test_acc=network.evaluate(test_images,test_labels)


    

    




10000/10000 [==============================] - 1s 51us/step

In [63]:

    

print('test_acc',test_acc)


    

    




test_acc 0.9791

In [72]:

    

from matplotlib import pyplot as plt
plt.imshow(train_images[0,:].reshape(28,28)*255, interpolation='nearest')
plt.show()


    

In [73]:

    

ls


    

    




sample_data/

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