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[Deep Learning by Keras] 03. Multinominal Classification of MNIST for 5 minutes

Hello. I'm DVM who is talk about deep learning for 5 minutes using keras.

Today I'm going to talk about Multinomial classification using mnist example

Let's classify digit numbers using mnist example.

wiki pedia saids Multinomial classification

is the problem of classifying instances into one of three or more classes. 

I introduce 5 technique.

First, softmax activation funtion.

In order to classify three or more classes, we have to use softmax activation function.

Second,  One hot encoding.

In order to classify three or more classes, It is better to use one hot encoding.

The largest value is represented by 1.

Third, Standardization.

If the datas are spread, normalization gather the datas.

Fourth, Cross entropy cost function.

In order to classify three or more classes, 

we have to use cross entropy cost function and softmax activation function.

Fifth Stochastic gradient descent.

If learning rate is big, cost funtion become divergence

if learning rate is too small, cost function fall into local minimum which is not optimization.

we adjust learning rate untill cost values is small.

Let`s practice coding using keras

Let's define the library¶

In [3]:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras import utils
from tensorflow.keras.datasets import mnist
import numpy as np
import matplotlib.pyplot as plt

Let's define how many things you will classify¶

In [5]:

nb_classes = 10

Let's receive the train and test datas¶

In [16]:

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

Let's check the data using graph¶

In [9]:

plt.imshow(x_train[0].reshape(28,28),cmap='Greys')

Out[9]:

<matplotlib.image.AxesImage at 0x206b06df988>

In [11]:

x_train.shape

Out[11]:

(60000, 28, 28)

In [17]:

x_train = x_train.reshape(x_train.shape[0],
                          x_train.shape[1]*x_train.shape[2])

In [19]:

x_train.shape

Out[19]:

(60000, 784)

In [20]:

x_train.max()

Out[20]:

255

Let's normalize for learning well¶

Let's change integer to deciaml and normalization¶

In [22]:

x_train = x_train.astype('float32')/x_train.max()

In [23]:

y_train

Out[23]:

array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)

Let's make one-hot encoding¶

In [24]:

y_train = utils.to_categorical(y_train,nb_classes)

In [25]:

y_train

Out[25]:

array([[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.]], dtype=float32)

Let's change the test datas like train datas¶

In [26]:

x_test = x_test.reshape(x_test.shape[0],
                       x_test.shape[1]*x_test.shape[2])

In [27]:

x_test = x_test.astype('float32')/255

In [30]:

y_test = utils.to_categorical(y_test,nb_classes)

Let's make 1 layer sequence model¶

Multinominal classification problem use softmax and cross entropy¶

In [33]:

model = Sequential()
model.add(Dense(nb_classes,input_dim=784))
model.add(Activation('softmax'))

In [34]:

model.summary()

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_1 (Dense)              (None, 10)                7850      
_________________________________________________________________
activation_1 (Activation)    (None, 10)                0         
=================================================================
Total params: 7,850
Trainable params: 7,850
Non-trainable params: 0
_________________________________________________________________

Let's use categorical cross entropy¶

In [36]:

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

In [37]:

history = model.fit(x_train,y_train,epochs =15)

Train on 60000 samples
Epoch 1/15
60000/60000 [==============================] - 8s 127us/sample - loss: 0.7791 - accuracy: 0.8162
Epoch 2/15
60000/60000 [==============================] - 7s 112us/sample - loss: 0.4569 - accuracy: 0.8804
Epoch 3/15
60000/60000 [==============================] - 6s 96us/sample - loss: 0.4038 - accuracy: 0.8909
Epoch 4/15
60000/60000 [==============================] - 8s 135us/sample - loss: 0.3772 - accuracy: 0.8969
Epoch 5/15
60000/60000 [==============================] - 5s 89us/sample - loss: 0.3605 - accuracy: 0.9010
Epoch 6/15
60000/60000 [==============================] - 5s 90us/sample - loss: 0.3486 - accuracy: 0.9039
Epoch 7/15
60000/60000 [==============================] - 5s 89us/sample - loss: 0.3396 - accuracy: 0.9057
Epoch 8/15
60000/60000 [==============================] - 5s 89us/sample - loss: 0.3323 - accuracy: 0.9078
Epoch 9/15
60000/60000 [==============================] - 5s 90us/sample - loss: 0.3265 - accuracy: 0.9095
Epoch 10/15
60000/60000 [==============================] - 5s 88us/sample - loss: 0.3215 - accuracy: 0.9104
Epoch 11/15
60000/60000 [==============================] - 5s 90us/sample - loss: 0.3173 - accuracy: 0.9116
Epoch 12/15
60000/60000 [==============================] - 5s 88us/sample - loss: 0.3137 - accuracy: 0.9130
Epoch 13/15
60000/60000 [==============================] - 6s 104us/sample - loss: 0.3105 - accuracy: 0.9139
Epoch 14/15
60000/60000 [==============================] - 6s 98us/sample - loss: 0.3076 - accuracy: 0.9146
Epoch 15/15
60000/60000 [==============================] - 7s 110us/sample - loss: 0.3050 - accuracy: 0.9153

Let's evaluate¶

In [38]:

score = model.evaluate(x_test,y_test)
print(score)

10000/10000 [==============================] - 1s 91us/sample - loss: 0.2944 - accuracy: 0.9190
[0.2944114541530609, 0.919]

Total Codes¶

In [ ]:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation
import numpy as np
from tensorflow.keras import utils
from tensorflow.keras.datasets import mnist
import matplotlib.pyplot as plt
nb_classes = 10
(x_train,y_train),(x_test,y_test) = mnist.load_data()
plt.imshow(x_train[0].reshape(28,28),cmap='Greys',interpolation='nearest')
x_train = x_train.reshape(x_train.shape[0],x_train.shape[1]
                          *x_train.shape[2])
x_train = x_train.astype('float32')/x_train.max()
y_train = utils.to_categorical(y_train,nb_classes)
x_test = x_test.reshape(x_test.shape[0],x_test.shape[1]*x_test.shape[2])
x_test = x_test.astype('float32')/255
y_test = utils.to_categorical(y_test,nb_classes)
model = Sequential()
model.add(Dense(nb_classes, input_dim=784))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',optimizer='sgd',
              metrics=['accuracy'])
history = model.fit(x_train,y_train,epochs=15)
score = model.evaluate(x_test,y_test)
print(score[1])