Gfyuki

First post here. I'm working on a project about multi-class image classification and created a python script using Keras to train a model with transfer learning. To my dismay the model has always predicted the same class, I've simplified the model down to 3 image classes (I'm using a kaggle food image stock with 800 training samples and 800 validation samples per class plus image reformatting) and tried different optimizers, yet it still comes down to the same class while the model also apparently only has an accuracy of ~0.2563 at 25 epochs of training. I've posted the code below, how can I improve the accuracy of this script and solve the same predicted class problem?

import pandas as pd

import numpy as np

import os

import keras

import matplotlib.pyplot as plt

from keras.layers import Dense, GlobalAveragePooling2D

from keras.preprocessing import image

from keras.preprocessing.image import ImageDataGenerator

from keras.models import Model

from keras import optimizers

from keras import applications

from keras.applications.vgg16 import preprocess_input



img_classes = 3



base_model = applications.VGG16(weights='imagenet', include_top=False)



x = base_model.output

x = GlobalAveragePooling2D()(x)

x = Dense(1024, activation='relu')(x)

x = Dense(1024, activation='relu')(x)

x = Dense(512, activation='relu')(x)

preds = Dense(img_classes, activation='softmax')(x)



model = Model(inputs=base_model.input, outputs=preds)



for i, layer in enumerate(model.layers):

    print(i, layer.name)



for layer in model.layers[:25]:

    layer.trainable = False



train_datagen = ImageDataGenerator(rescale=1./255,

                                   rotation_range=40,

                                   width_shift_range=0.2,

                                   height_shift_range=0.2,

                                   shear_range=0.2,

                                   zoom_range=0.2,

                                   horizontal_flip=True,

                                   fill_mode='nearest',

                                   preprocessing_function=preprocess_input)



train_generator = train_datagen.flow_from_directory('./food-101/bigtrain',

                                                    target_size=(128, 128),

                                                    color_mode='rgb',

                                                    classes=['apple_pie', 'churros', 'miso_soup'],

                                                    batch_size=1,

                                                    class_mode='categorical',

                                                    shuffle=True)



val_datagen = ImageDataGenerator(

    rescale=1./255,

    rotation_range=40,

    width_shift_range=0.2,

    height_shift_range=0.2,

    shear_range=0.2,

    zoom_range=0.2,

    horizontal_flip=True,

    fill_mode='nearest',

    preprocessing_function=preprocess_input,)



val_generator = val_datagen.flow_from_directory(

    './food-101/bigval',

    target_size=(128, 128),

    classes=['apple_pie', 'churros', 'miso_soup'],

    batch_size=1,

    class_mode='categorical',

    shuffle=True)



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



model.compile(optimizer=optimizers.SGD(lr=0.00001,

                                       momentum=0.9,

                                       decay=0.0001,

                                       nesterov=True), loss='categorical_crossentropy', metrics=['accuracy'])



batch_size = 1





validation_steps = 64 // batch_size

step_size_train = train_generator.n//train_generator.batch_size



model.fit_generator(generator=train_generator,

                    steps_per_epoch=step_size_train,

                    epochs=25,

                    validation_data=val_generator,

                    validation_steps=validation_steps)



model.save('./test_try_vgg_9.h5')

Example prediction results:

classes: apple_pie, churros, miso_soup

miso soup

[0.3202575  0.48074356 0.19899891] rmsprop 

[0.45246536 0.4505403  0.09699439] sgd



churros

[0.37473327 0.35784692 0.2674198 ] rmsprop

[0.4145825  0.465228   0.12018944] sgd

This is the prediction script:

from keras.models import load_model

from keras import optimizers

from keras.preprocessing import image

import numpy as np

from keras.applications.vgg16 import preprocess_input



# dimensions of our images

img_width, img_height = 512, 512



# load model

model = load_model('./test_try_vgg_9.h5')



# predicting images

img = image.load_img('./food-101/training/apple_pie/551535.jpg')

x = image.img_to_array(img)

x = np.expand_dims(x, axis=0)

x = preprocess_input(x)



pred = model.predict(x)

print("Probability: ")

print(pred[0])