In [1]:

    

import h5py


    

In [3]:

    

import pydot


    

In [4]:

    

print(pydot.__version__)


    

    




1.2.4

In [15]:

    

from keras.utils import np_utils
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Activation
import numpy as np


    

In [22]:

    

(X_train, Y_train),(X_test, Y_test) = mnist.load_data()


    

In [42]:

    

type(X_test)


    

    
Out[42]:





numpy.ndarray

In [24]:

    

X_train = X_train.reshape(60000, 784).astype('float32') / 255.0
X_test = X_test.reshape(10000, 784).astype('float32') / 255.0


    

In [40]:

    

np.array(tuple([[4,5]])).shape


    

    
Out[40]:





(1, 2)

In [44]:

    

type(np.array(tuple([[4,5]])))


    

    
Out[44]:





numpy.ndarray

In [47]:

    

print(Y_train)


    

    




[[ 0.  0.  0. ...,  0.  0.  0.]
 [ 1.  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.  1.  0.]]

In [56]:

    

Y_train = np_utils.to_categorical(Y_train)
Y_test = np_utils.to_categorical(Y_test)


    

In [50]:

    

print(Y_train)


    

    




[[ 0.  0.  0. ...,  0.  0.  0.]
 [ 1.  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.  1.  0.]]

In [52]:

    

model = Sequential()
model.add(Dense(units=64, input_dim = 28*28, activation='relu'))
model.add(Dense(units=10, activation='softmax'))


    

In [53]:

    

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


    

In [54]:

    

model.fit(X_train, Y_train, epochs=5, batch_size=32)


    

    




Epoch 1/5
60000/60000 [==============================] - 6s 101us/step - loss: 2.3006 - acc: 0.1154
Epoch 2/5
60000/60000 [==============================] - 5s 90us/step - loss: 2.3000 - acc: 0.1124
Epoch 3/5
60000/60000 [==============================] - 5s 91us/step - loss: 2.2997 - acc: 0.1124
Epoch 4/5
60000/60000 [==============================] - 5s 91us/step - loss: 2.2993 - acc: 0.1124
Epoch 5/5
60000/60000 [==============================] - 5s 91us/step - loss: 2.2989 - acc: 0.1124






    
Out[54]:





<keras.callbacks.History at 0x104f78e48>

In [57]:

    

loss_and_metrics = model.evaluate(X_test, Y_test, batch_size=32)


    

    




10000/10000 [==============================] - 0s 41us/step

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print('loss_and_met : ', str(loss_and_metrics))


    

    




loss_and_met :  [2.2248213020324705, 0.1711]

In [65]:

    

from IPython.display import SVG
from keras.utils.vis_utils import model_to_dot


    

In [66]:

    

%matplotlib inline


    

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SVG(model_to_dot(model, show_shapes=True).create(prog='dot', format='svg'))


    

    
Out[67]:

In [68]:

    

from keras.models import load_model


    

In [69]:

    

model.save('mnist_mlp_model.h5')


    

In [71]:

    

model = load_model('mnist_mlp_model.h5')


    

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