Keras VGG16 pretrained model accuracy does not increase - python-3.x

Question

Hi guys i have a problem on VGG16 on Keras.
I am trying to make accuracy higher but did not work.
I only have 46 data training, 12 classes, and 26 data validation.
Currently, the highest accuracy that I can get is 0.18.
I try to change the batch size into 2 but the result is worst than i expected.
I don't think I should set the data training sample should be higher than my actual data.
What should I do to increase the accuracy?
This is my actual code:
from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
from keras.layers import Input, Flatten, Dense, Dropout
from keras.models import Model, Sequential
from keras import backend as K
import numpy as np
import matplotlib.pyplot as plt
# dimensions of our images.
from keras.preprocessing.image import ImageDataGenerator
img_width, img_height = 224, 224
train_data_dir = 'database/train'
validation_data_dir = 'database/validation'
nb_train_samples = 46
nb_validation_samples = 26
epochs = 50
batch_size = 4
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
#Get back the convolutional part of a VGG network trained on ImageNet
vgg_conv = VGG16(weights='imagenet', include_top=True)
vgg_conv.summary()
print('VGG Pretrained Model loaded.')
#Add a layer where input is the output of the second last layer
x = Dense(12, activation='softmax', name='predictions')(vgg_conv.layers[-2].output)
model = Model(input=vgg_conv.input, output=x)
#In the summary, weights and layers from VGG part will be hidden, but they will be fit during the training
model.summary()
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1. / 224,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 224)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
# compile model
# model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizers.RMSprop(lr=2e-4), metrics=['accuracy'])
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
# Train the model
history = model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples / batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples / batch_size)
# Save the model
model.save('vgg16_pretrained_5.h5')
# Check Performance
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'b', label='Training acc')
plt.plot(epochs, val_acc, 'r', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()

Answer 1

Since you have 12 classes and only 46 observations, it roughly becomes 2 observations per class (This is just a guess without even looking at the data set). With this little data, the NN model can not even understand the pattern of the data and eventually will fail to generalize. So you need at least for than 2k observations for better results.