Einführung

Tensorflow hat CNN einmal implementiert, aber dieses Mal habe ich es mit einem Framework namens Keras implementiert, das am besten bewertet zu sein scheint. ――Wenn ich es versuchte, enthielt es verschiedene Bibliotheken, die bequemer als Tensorflow waren, und es schien einfach zu implementieren zu sein. ――Der Grund dafür ist, dass die detaillierte Berechnungsverarbeitung anscheinend in einer Blackbox ist, sodass Sie sie anscheinend verstehen können, ohne darüber nachzudenken, aber anscheinend keine Feinabstimmung vornehmen können.
Für Anfänger, die sich basierend auf den oben genannten bewegen möchten. ――OpenCV3 ist endlich verfügbar, aber Keras führt auch Bildmanipulationen durch, sodass keine Wende möglich ist. ――Eine Epoche dauert ungefähr 7 Minuten, daher ist es möglicherweise besser, zuerst ungefähr 10 Epochen zu versuchen. Es scheint, dass 100 Epochen etwa 10 bis 12 Stunden dauern.

Referenz

https://github.com/aidiary/keras-examples/blob/master/cnn/cifar10/cifar10.py

Was ist CIFAR-10?

Ein Datensatz mit 32x32 Pixel Farbbildern.
10 Flugzeugtypen, Automobile, Vögel, Katzen, Hirsche, Hunde, Frösche, Pferde, Schiffe, Lastwagen, bestehend aus 50.000 Trainingsdaten und 10.000 Testdaten. ――Es scheint, dass Sie es leicht als Bibliothek aufrufen können, also habe ich versucht, es zu verwenden, nachdem ich es ausprobiert habe.

Umgebung

Nur die wichtigsten sind aufgelistet. --Bitte installieren Sie das fehlende Teil von Pip oder Homebrew.

#	OS/Software/Bibliothek	Ausführung
1	Mac OS X	EI Capitan
2	Python	3.6er Serie
3	NumPy	1.13 Serie
4	Keras	1.2 System

Implementierung

`train.py`


#!/usr/local/bin/python3
#!-*- coding: utf-8 -*-

import os
import numpy as np
from scipy.misc import toimage
import matplotlib.pyplot as plt

from keras.datasets import cifar10
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils.visualize_util import plot

def cnn_model(X_train, nb_classes):

    model = Sequential()

    model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=X_train.shape[1:]))
    model.add(Activation('relu'))
    model.add(Convolution2D(32, 3, 3))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Convolution2D(64, 3, 3, border_mode='same'))
    model.add(Activation('relu'))
    model.add(Convolution2D(64, 3, 3))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))

    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))

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

    return model

def plot_cifar10(X, y, result_dir):
    plt.figure()

    #Zeichne ein Bild
    nclasses = 10
    pos = 1

    for targetClass in range(nclasses):
        targetIdx = []

        #Indexliste der Bilder der Klasse classID abrufen
        for i in range(len(y)):
            if y[i][0] == targetClass:
                targetIdx.append(i)

        #Zeichnen Sie die ersten 10 Bilder, die zufällig aus jeder Klasse ausgewählt wurden
        np.random.shuffle(targetIdx)

        for idx in targetIdx[:10]:
            img = toimage(X[idx])
            plt.subplot(10, 10, pos)
            plt.imshow(img)
            plt.axis('off')
            pos += 1

    plt.savefig(os.path.join(result_dir, 'plot.png'))

def save_history(history, result_file):
    loss = history.history['loss']
    acc = history.history['acc']
    val_loss = history.history['val_loss']
    val_acc = history.history['val_acc']
    nb_epoch = len(acc)

    with open(result_file, "w") as fp:
        fp.write("epoch\tloss\tacc\tval_loss\tval_acc\n")
        for i in range(nb_epoch):
            fp.write("%d\t%f\t%f\t%f\t%f\n" % (i, loss[i], acc[i], val_loss[i], val_acc[i]))

if __name__ == '__main__':

    #Anzahl der Trainingsversuche zum Datenlernen
    nb_epoch = 100

    #Speicherverzeichnis für Lernergebnisse
    result_dir = '{Verzeichnispfad speichern}'

    #Wie viele Bilder sollen in einer Studie verwendet werden?
    batch_size = 128

    #Anzahl der Identifikationsetiketten
    nb_classes = 10

    #Geben Sie die Bildabmessungen ein
    img_rows, img_cols = 32, 32

    #Anzahl der Kanäle (3, da es sich um RGB handelt)
    img_channels = 3

    # CIFAR-10 Daten laden
    # (nb_samples, nb_rows, nb_cols, nb_channel) = tf
    (X_train, y_train), (X_test, y_test) = cifar10.load_data()

    #Plotten Sie Bilder nach dem Zufallsprinzip
    plot_cifar10(X_train, y_train, result_dir)

    #Der Pixelwert ist 0-In 1 konvertieren
    X_train = X_train.astype('float32')
    X_test = X_test.astype('float32')
    X_train /= 255.0
    X_test /= 255.0

    #Klassenbezeichnung (0-9) eins-In Hotcodierungsformat konvertieren
    Y_train = np_utils.to_categorical(y_train, nb_classes)
    Y_test = np_utils.to_categorical(y_test, nb_classes)

    #Modell-
    model = cnn_model(X_train, nb_classes)

    #Modellzusammenfassung anzeigen
    model.summary()
    plot(model, show_shapes=True, to_file=os.path.join(result_dir, 'model.png'))

    #Lernen
    history = model.fit(
        X_train,
        Y_train,
        batch_size=batch_size,
        nb_epoch=nb_epoch,
        verbose=1,
        validation_data=(X_test, Y_test),
        shuffle=True
    )

    #Speichern Sie gelernte Modelle, Gewichte und Geschichte
    model_json = model.to_json()

    with open(os.path.join(result_dir, 'model.json'), 'w') as json_file:
        json_file.write(model_json)

    model.save_weights(os.path.join(result_dir, 'model.h5'))
    save_history(history, os.path.join(result_dir, 'history.txt'))

    #Modellbewertung
    loss, acc = model.evaluate(X_test, Y_test, verbose=0)

    print('Test loss:', loss)
    print('Test acc:', acc)

Lernen

Eine wirklich leicht verständliche Benutzeroberfläche.

Using TensorFlow backend.
____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
convolution2d_1 (Convolution2D)  (None, 32, 32, 32)    896         convolution2d_input_1[0][0]      
____________________________________________________________________________________________________
activation_1 (Activation)        (None, 32, 32, 32)    0           convolution2d_1[0][0]            
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D)  (None, 30, 30, 32)    9248        activation_1[0][0]               
____________________________________________________________________________________________________
activation_2 (Activation)        (None, 30, 30, 32)    0           convolution2d_2[0][0]            
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D)    (None, 15, 15, 32)    0           activation_2[0][0]               
____________________________________________________________________________________________________
dropout_1 (Dropout)              (None, 15, 15, 32)    0           maxpooling2d_1[0][0]             
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D)  (None, 15, 15, 64)    18496       dropout_1[0][0]                  
____________________________________________________________________________________________________
activation_3 (Activation)        (None, 15, 15, 64)    0           convolution2d_3[0][0]            
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D)  (None, 13, 13, 64)    36928       activation_3[0][0]               
____________________________________________________________________________________________________
activation_4 (Activation)        (None, 13, 13, 64)    0           convolution2d_4[0][0]            
____________________________________________________________________________________________________
maxpooling2d_2 (MaxPooling2D)    (None, 6, 6, 64)      0           activation_4[0][0]               
____________________________________________________________________________________________________
dropout_2 (Dropout)              (None, 6, 6, 64)      0           maxpooling2d_2[0][0]             
____________________________________________________________________________________________________
flatten_1 (Flatten)              (None, 2304)          0           dropout_2[0][0]                  
____________________________________________________________________________________________________
dense_1 (Dense)                  (None, 512)           1180160     flatten_1[0][0]                  
____________________________________________________________________________________________________
activation_5 (Activation)        (None, 512)           0           dense_1[0][0]                    
____________________________________________________________________________________________________
dropout_3 (Dropout)              (None, 512)           0           activation_5[0][0]               
____________________________________________________________________________________________________
dense_2 (Dense)                  (None, 10)            5130        dropout_3[0][0]                  
____________________________________________________________________________________________________
activation_6 (Activation)        (None, 10)            0           dense_2[0][0]                    
====================================================================================================
Total params: 1,250,858
Trainable params: 1,250,858
Non-trainable params: 0
____________________________________________________________________________________________________
Train on 50000 samples, validate on 10000 samples
Epoch 1/100
2017-08-29 11:55:15.047457: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-29 11:55:15.047489: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-08-29 11:55:15.047498: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-08-29 11:55:15.047505: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
50000/50000 [==============================] - 400s - loss: 1.8624 - acc: 0.3123 - val_loss: 1.5157 - val_acc: 0.4537
Epoch 2/100
50000/50000 [==============================] - 455s - loss: 1.4213 - acc: 0.4825 - val_loss: 1.2090 - val_acc: 0.5645
Epoch 3/100
50000/50000 [==============================] - 384s - loss: 1.2453 - acc: 0.5549 - val_loss: 1.1120 - val_acc: 0.6004
Epoch 4/100
50000/50000 [==============================] - 414s - loss: 1.1602 - acc: 0.5869 - val_loss: 1.0376 - val_acc: 0.6304
Epoch 5/100
40960/50000 [=======================>......] - ETA: 64s - loss: 1.0852 - acc: 0.6156

Lernergebnis

―― Schließlich ist es 0,795500, was eine ziemliche Zahl ist.

epoch	loss	acc	val_loss	val_acc
0	1.862381	0.312260	1.515693	0.453700
1	1.421265	0.482520	1.209045	0.564500
2	1.245348	0.554880	1.111982	0.600400
3	1.160202	0.586880	1.037605	0.630400
4	1.082496	0.616840	1.002439	0.646900
5	1.014772	0.638600	0.921932	0.676100
6	0.968752	0.657040	0.890589	0.690000
7	0.933635	0.667640	0.854735	0.701200
8	0.893519	0.686920	0.841952	0.705100
9	0.866928	0.692880	0.832813	0.706700
10	0.835548	0.706200	0.808762	0.713600
11	0.811907	0.714380	0.790619	0.721300
12	0.787855	0.723800	0.769006	0.730800
13	0.769309	0.728920	0.757458	0.733200
14	0.749378	0.735600	0.731948	0.745700
15	0.729672	0.741200	0.707380	0.753900
16	0.711307	0.747860	0.720168	0.749600
17	0.694116	0.753920	0.690921	0.760300
18	0.685302	0.756300	0.688788	0.762900
19	0.670361	0.763320	0.699074	0.758100
20	0.655677	0.765700	0.683461	0.762800
21	0.650073	0.772340	0.675608	0.764000
22	0.637535	0.772880	0.705318	0.756400
23	0.627683	0.778300	0.659172	0.769500
24	0.608234	0.784560	0.649584	0.777400
25	0.605251	0.785540	0.670669	0.769400
26	0.604372	0.784440	0.657858	0.771600
27	0.591194	0.790200	0.648238	0.777000
28	0.583359	0.792300	0.644217	0.779500
29	0.579536	0.794680	0.654746	0.779200
30	0.577020	0.796780	0.650456	0.776600
31	0.558436	0.801840	0.654724	0.771200
32	0.551728	0.802580	0.660091	0.776500
33	0.551879	0.806080	0.691178	0.762100
34	0.551185	0.804320	0.643525	0.776900
35	0.537961	0.808680	0.648304	0.776100
36	0.538601	0.809580	0.663515	0.776100
37	0.526395	0.812180	0.667506	0.773200
38	0.520259	0.814020	0.637330	0.780900
39	0.520766	0.813460	0.626052	0.782500
40	0.513462	0.818240	0.637447	0.782200
41	0.508732	0.819540	0.628536	0.784700
42	0.512045	0.816880	0.648108	0.780600
43	0.502967	0.819200	0.681984	0.774500
44	0.502752	0.819600	0.626435	0.788800
45	0.506435	0.818640	0.641397	0.782800
46	0.492950	0.822220	0.643260	0.783700
47	0.490653	0.824700	0.652792	0.782700
48	0.479054	0.828640	0.641824	0.783700
49	0.483628	0.825200	0.636701	0.789600
50	0.483054	0.828940	0.635811	0.786600
51	0.472693	0.831800	0.628785	0.788600
52	0.473315	0.830320	0.630551	0.791700
53	0.466273	0.833520	0.636630	0.787100
54	0.466997	0.834320	0.627516	0.792000
55	0.467793	0.831500	0.641428	0.783800
56	0.456283	0.837320	0.635762	0.789500
57	0.452593	0.837640	0.636226	0.785800
58	0.459387	0.834740	0.628133	0.789200
59	0.450392	0.838600	0.651949	0.786700
60	0.446786	0.840840	0.628699	0.790300
61	0.443291	0.838820	0.635795	0.790300
62	0.445090	0.841860	0.630603	0.790400
63	0.438771	0.842060	0.618292	0.793700
64	0.444951	0.840980	0.631328	0.791900
65	0.443358	0.841860	0.631681	0.784200
66	0.436495	0.842280	0.640644	0.787600
67	0.434661	0.844800	0.625743	0.789400
68	0.431563	0.843140	0.633843	0.790500
69	0.430192	0.847000	0.642871	0.788800
70	0.421411	0.847940	0.624006	0.790100
71	0.423439	0.848720	0.650200	0.786900
72	0.420324	0.848240	0.654864	0.789300
73	0.421919	0.848480	0.627082	0.792700
74	0.410431	0.851180	0.642263	0.790100
75	0.419571	0.850860	0.636891	0.789100
76	0.413792	0.852300	0.653029	0.786400
77	0.415705	0.852520	0.644457	0.787400
78	0.423684	0.850840	0.631995	0.791600
79	0.410283	0.852720	0.652608	0.788600
80	0.411928	0.851480	0.634341	0.793000
81	0.405703	0.855640	0.630345	0.794300
82	0.408496	0.853320	0.652739	0.789500
83	0.402927	0.855320	0.642568	0.793900
84	0.404812	0.854160	0.637601	0.795100
85	0.400238	0.857520	0.633781	0.793700
86	0.403761	0.853880	0.625709	0.794700
87	0.404491	0.856420	0.635942	0.796500
88	0.392304	0.860800	0.636456	0.797500
89	0.398910	0.857180	0.655635	0.787900
90	0.392418	0.858680	0.634033	0.793500
91	0.390591	0.861520	0.627206	0.794700
92	0.385502	0.860740	0.657583	0.787700
93	0.391550	0.860260	0.664002	0.787900
94	0.388638	0.859500	0.648921	0.792000
95	0.385543	0.863360	0.644680	0.793800
96	0.386466	0.862720	0.648885	0.791200
97	0.384559	0.862300	0.651030	0.794400
98	0.384592	0.861200	0.629053	0.794900
99	0.388739	0.860320	0.634723	0.795500

[PYTHON] Ich habe versucht, Bilder mit CIFAR-10 mit Keras-Learning- zu erkennen.

Einführung

Referenz

Was ist CIFAR-10?

Umgebung

Implementierung

train.py

Lernen

Lernergebnis

`train.py`