それぞれ 1200 枚の画像を持つ 2 つの異なるデータセットを使用しています。最初のデータセットには 4 つのクラスがあり、2 番目のデータセットには 6 つのクラスがあります。
これは単純な画像分類問題です。しかし、トレーニング中、各エポックで、両方のデータセットの検証精度について同じ値を取得しています。
imagemagick を使用して、両方のデータセットのすべての画像のサイズを 100x100 に変更しました。
どこを間違えているのかわかりません。前もって感謝します
端末出力:
Using Theano backend.
Couldn't import dot_parser, loading of dot files will not be possible.
X_train shape: (880, 3, 100, 100)
880 train samples
220 test samples
train:
0 418
3 179
2 174
1 109
dtype: int64
test:
0 98
3 55
2 43
1 24
dtype: int64
Train on 880 samples, validate on 220 samples
Epoch 1/5
880/880 [==============================] - 582s - loss: 1.3444 - acc: 0.4500 - val_loss: 1.2752 - val_acc: 0.4455
Epoch 2/5
880/880 [==============================] - 540s - loss: 1.2624 - acc: 0.4750 - val_loss: 1.2802 - val_acc: 0.4455
Epoch 3/5
880/880 [==============================] - 540s - loss: 1.2637 - acc: 0.4750 - val_loss: 1.2712 - val_acc: 0.4455
Epoch 4/5
880/880 [==============================] - 538s - loss: 1.2484 - acc: 0.4750 - val_loss: 1.2623 - val_acc: 0.4455
Epoch 5/5
880/880 [==============================] - 537s - loss: 1.2375 - acc: 0.4750 - val_loss: 1.2486 - val_acc: 0.4455
prediction on test data:
In [26]: model.predict_classes(X_test)
220/220 [==============================] - 37s
Out[26]:
array([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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0])
コード：
from __future__ import print_function
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten, Reshape
from keras.layers.convolutional import Convolution2D, MaxPooling2D, Convolution1D, MaxPooling1D
from keras.optimizers import SGD
from keras.utils import np_utils, generic_utils
import numpy as np
from sklearn.cross_validation import train_test_split
import pandas as pd
batch_size = 30
nb_classes = 4
nb_epoch = 10
img_rows, img_cols = 100, 100
img_channels = 3
X = np.load( 'image-data.npy' )
y = np.load( 'image-class.npy' )
# the data, shuffled and split between train and test sets
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=100 )
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')
print("train:\n ",pd.value_counts(y_train))
print("test:\n",pd.value_counts(y_test))
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(Convolution2D(32, 3, 3, border_mode='same', input_shape=(img_channels, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(1,1) ))
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), strides=(1,1) ))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
model.fit(X_train, Y_train , batch_size = batch_size, nb_epoch = nb_epoch,shuffle=True, show_accuracy=True,validation_data=(X_test,Y_test) )
out = model.predict_classes(X_test)