Keras model accuracy is not improving - Image Classification

Question

I have 4 classes and building a Keras model for image classification problem. I have tried a couple of adjustments but accuracy is not going beyond 75% and still loss is 64%.

I have 90,400 images as a training set and 20,000 images for testing.

Here is my model.

model = Sequential()
model.add(Conv2D(32, kernel_size = (3, 3),input_shape=(100,100,3),activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(4, activation = 'softmax'))
model.compile(loss = 'sparse_categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

batch_size = 64

train_datagen = ImageDataGenerator (rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
test_datagen=ImageDataGenerator(rescale=1./255)

training_set = train_datagen.flow_from_directory('/dir/training_set', target_size=(100,100),batch_size=batch_size,class_mode='binary')

test_set = test_datagen.flow_from_directory('/dir/test_set',target_size=(100,100), batch_size=batch_size, class_mode='binary')

# 90,400 images I have under the training_set directory and 20,000 under the test directory.

model.fit(training_set, steps_per_epoch=90400//batch_size, epochs=1,validation_data=test_set, validation_steps= 20000//batch_size)

I tried adjusting layers and dropouts but no luck. Any ideas?

Answer 1

I was able to achieve accuracy with transfer learning using the pre-trained MobileNet model.

Attaching my code and confusion metrix here so it may be helpful to someone.

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.applications import MobileNet
from keras.preprocessing import image
from keras.applications.mobilenet import preprocess_input
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Model
from keras.optimizers import Adam

base_model=MobileNet(weights='imagenet',include_top=False) #imports the mobilenet model and discards the last 1000 neuron layer.

x=base_model.output
x=GlobalAveragePooling2D()(x)
x=Dense(1024,activation='relu')(x) #we add dense layers so that the model can learn more complex functions and classify for better results.
x=Dense(1024,activation='relu')(x) #dense layer 2
x=Dense(512,activation='relu')(x) #dense layer 3
preds=Dense(3,activation='softmax')(x) #final layer with softmax activation

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

for layer in model.layers[:20]:
    layer.trainable=False
for layer in model.layers[20:]:
    layer.trainable=True

train_data_path = '../train_dataset_path'

train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input, validation_split=0.2) #included in our dependencies

train_generator=train_datagen.flow_from_directory(train_data_path,
                                                 target_size=(224,224),
                                                 color_mode='rgb',
                                                 batch_size=32,
                                                 class_mode='categorical',
                                                 shuffle=True,
                                                 subset='training')

test_generator=train_datagen.flow_from_directory(train_data_path, 
                                                 target_size=(224,224),
                                                 color_mode='rgb',
                                                 batch_size=32,
                                                 class_mode='categorical',
                                                 shuffle=False,
                                                 subset='validation')

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

step_size_train = train_generator.n//train_generator.batch_size
step_size_test  = test_generator.n//test_generator.batch_size


model_history = model.fit(train_generator,
                          steps_per_epoch=step_size_train,
                          epochs=5,
                          validation_data=test_generator,
                          validation_steps=step_size_test)


model.save('tl_interior_model_2')

#Load the model
model = keras.models.load_model('tl_interior_model_2')