f-rumblefish

from keras.datasets import cifar10  
import matplotlib.pyplot as plt  
  
(X_train, y_train), (X_test, y_test) = cifar10.load_data()  
  
plt.figure(facecolor='white')  
  
for i in range(100):  
    plt.subplot(10, 10, i+1)  
    plt.imshow(X_train[i])  

f-rumblefish

# import library ---------------------------------------------------------------
import pygame.midi
import time

# define all the constant values -----------------------------------------------
device = 0     # device number in win10 laptop
instrument = 9 # http://www.ccarh.org/courses/253/handout/gminstruments/
note_Do = 48   # http://www.electronics.dit.ie/staff/tscarff/Music_technology/midi/midi_note_numbers_for_octaves.htm
note_Re = 50
note_Me = 52

f-rumblefish

# Import library in Keras 1.2.0
from keras.layers.recurrent import SimpleRNN, GRU, LSTM
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.callbacks import EarlyStopping
from keras.utils.visualize_util import plot

# Define parameters 
HIDDEN_SIZE = 128
BATCH_SIZE = 10

f-rumblefish

train( [60, 67, 63, 60, 65, 62, 60, 67, 63, 60] ) -> label( 65 )
train( [67, 63, 60, 65, 62, 60, 67, 63, 60, 65] ) -> label( 62 )
train( [63, 60, 65, 62, 60, 67, 63, 60, 65, 62] ) -> label( 60 )
train( [60, 65, 62, 60, 67, 63, 60, 65, 62, 60] ) -> label( 67 )
train( [65, 62, 60, 67, 63, 60, 65, 62, 60, 67] ) -> label( 63 )
train( [62, 60, 67, 63, 60, 65, 62, 60, 67, 63] ) -> label( 60 )
train( [60, 67, 63, 60, 65, 62, 60, 67, 63, 60] ) -> label( 65 )
train( [67, 63, 60, 65, 62, 60, 67, 63, 60, 65] ) -> label( 62 )
train( [63, 60, 65, 62, 60, 67, 63, 60, 65, 62] ) -> label( 60 )
train( [60, 65, 62, 60, 67, 63, 60, 65, 62, 60] ) -> label( 67 )

f-rumblefish

60, 67, 63, 60, 65, 62, 60, 67, 63, 60, 65, 62, 60, 67, 63, 60, 65, 62, 60, 67, ...

f-rumblefish

# import library from Scikit-Learn ---------------------------------------------
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix

# algorithm 1 ------------------------------------------------------------------
print(" Naive Bayes ... ")

start = timeit.default_timer()
from sklearn import naive_bayes
classifier = naive_bayes.GaussianNB()

f-rumblefish

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense_1 (Dense)              (None, 32)                16032
_________________________________________________________________
activation_1 (Activation)    (None, 32)                0
_________________________________________________________________
dense_2 (Dense)              (None, 32)                1056
_________________________________________________________________
activation_2 (Activation)    (None, 32)                0

f-rumblefish

 MLP (3 layers, 32 neurons, 30 epochs) ...
 accuracy =  0.9986  time =  14.8910531305
[[3378    0    0]
 [   0 3407    0]
 [  13    1 3201]]


 Naive Bayes ...
 accuracy =  0.9874  time =  1.30055759897
[[3252    0  126]

f-rumblefish

Train on 9000 samples, validate on 1000 samples
Epoch 1/15
9000/9000 [==============================] - 1865s 207ms/step - loss: 0.6278 - acc: 0.6629 - val_loss: 0.5947 - val_acc: 0.6845
Epoch 2/15
9000/9000 [==============================] - 1810s 201ms/step - loss: 0.5216 - acc: 0.7424 - val_loss: 0.4472 - val_acc: 0.7916
Epoch 3/15
9000/9000 [==============================] - 1789s 199ms/step - loss: 0.4024 - acc: 0.8191 - val_loss: 0.3396 - val_acc: 0.8529
Epoch 4/15
9000/9000 [==============================] - 1817s 202ms/step - loss: 0.3204 - acc: 0.8646 - val_loss: 0.3059 - val_acc: 0.8740
Epoch 5/15

f-rumblefish

file_name   one_hot_encoding                ordered_labels  ordered_categories
----------------------------------------------------------------------------------------------------
0.png       [0, 1, 1, 1, 0, 0, 1, 0, 0, 0]  [6, 3, 2, 1]    [Shirt, Dress, Pullover, Trouser]
1.png       [1, 0, 0, 0, 1, 1, 1, 0, 0, 0]  [5, 4, 0, 6]    [Sandal, Coat, T-shirt/top, Shirt]
2.png       [1, 1, 0, 1, 0, 0, 0, 0, 0, 0]  [1, 3, 0, 3]    [Trouser, Dress, T-shirt/top, Dress]
3.png       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0]  [5, 5, 7, 6]    [Sandal, Sandal, Sneaker, Shirt]