I use two different data sets of 1200 images each. The first data set has 4 classes, and the second data set has 6 classes.
This is a simple image classification problem. But during training in each era, I get the same value of verification accuracy for both data sets.
I resized all images of both datasets to 100x100 using imagemagick.
I do not know where I am making a mistake. thank you in advance
output terminal:
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])
the code:
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' )
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)