03_02_multi_class_logistic_regression

import numpy as np

from keras.models import Sequential

from keras.layers import Dense, Activation

from keras.optimizers import SGD

from sklearn.utils import shuffle

np.random.seed( 0)

M = 2

K = 3

n = 100

N = n * K

minibatch_size = 50

X1 = np.random.randn(n, M) + np.array([ 0, 10])

X2 = np.random.randn(n, M) + np.array([ 5, 5])

X3 = np.random.randn(n, M) + np.array([ 10, 0])

Y1 = np.array([[ 1, 0, 0] for i in range(n)])

Y2 = np.array([[ 0, 1, 0] for i in range(n)])

Y3 = np.array([[ 0, 0, 1] for i in range(n)])

X = np.concatenate((X1, X2, X3), axis= 0)

Y = np.concatenate((Y1, Y2, Y3), axis= 0)

model = Sequential()

model.add(Dense( input_dim=M, units=K))

model.add(Activation( 'softmax'))

model.compile( loss= 'categorical_crossentropy', optimizer=SGD( lr= 0.1))

model.fit(X, Y, epochs= 20, batch_size=minibatch_size)

X_, Y_ = shuffle(X, Y)

classes = model.predict_classes(X_[ 0: 10], batch_size=minibatch_size)

prob = model.predict_proba(X_[ 0: 10], batch_size=minibatch_size)

print( 'classified:')

print(np.argmax(model.predict(X_[ 0: 10]), axis= 1) == classes)

print()

print( 'output probability:')

print(prob)

classified:
[ True  True  True  True  True  True  True  True  True  True]


output probability:
[[9.9865520e-01 1.3448129e-03 2.1935644e-10]
 [2.9307455e-03 9.7283429e-01 2.4235010e-02]
 [9.6656960e-01 3.3430368e-02 4.5890197e-08]
 [1.7156685e-02 9.7406363e-01 8.7796366e-03]
 [3.4497321e-09 9.4414754e-03 9.9055851e-01]
 [1.4953143e-02 9.7935200e-01 5.6948471e-03]
 [2.0219375e-07 4.6562523e-02 9.5343727e-01]
 [6.5991614e-08 9.5339743e-03 9.9046600e-01]
 [7.0335742e-12 1.5032224e-04 9.9984968e-01]
 [9.9576533e-01 4.2346525e-03 2.3160656e-09]]