FruitClassifier.py

# -*- coding: utf-8 -*-
"""
Created on Wed Apr 24 19:56:19 2019

@author: Sahan Dilshan
"""

import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import os
from keras.optimizers import RMSprop, SGD, Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from keras.preprocessing.image import ImageDataGenerator

num_classes = 81
img_rows, img_cols = 32, 32
batch_size = 64 #reduce tha batch size if you train this on cpu



train_data_dir = 'fruits/train'
validation_data_dir = 'fruits/validation'

#data augmentaiton 
train_datagen = ImageDataGenerator(
      rescale=1./255,
      rotation_range=30,
      width_shift_range=0.3,
      height_shift_range=0.3,
      horizontal_flip=True,
      fill_mode='nearest')
 
validation_datagen = ImageDataGenerator(rescale=1./255)
 
train_generator = train_datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_rows, img_cols),
        batch_size=batch_size,
        class_mode='categorical',
        shuffle=True)
 
validation_generator = validation_datagen.flow_from_directory(
        validation_data_dir,
        target_size=(img_rows, img_cols),
        batch_size=batch_size,
        class_mode='categorical',
        shuffle=False)


model = Sequential()

# Padding = 'same'  - output and the input metrix has the same size(Adding the 0 padding)

model.add(Conv2D(32, (3, 3), padding='same',
                 input_shape= (img_rows, img_cols, 3)))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))

#if u wand to view the model,uncomment following line
#print(model.summary())



                     
checkpoint = ModelCheckpoint("Trained Models/fruits_fresh_cnn_1.h5",
                             monitor="val_loss",
                             mode="min",
                             save_best_only = True,
                             verbose=1)

earlystop = EarlyStopping(monitor = 'val_loss', 
                          min_delta = 0, 
                          patience = 3,
                          verbose = 1,
                          restore_best_weights = True)

reduce_lr = ReduceLROnPlateau(monitor = 'val_loss',
                              factor = 0.2,
                              patience = 2,
                              verbose = 1,
                              min_delta = 0.0001)


callbacks = [earlystop, checkpoint, reduce_lr]

#sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)
#rms = RMSprop(lr = 0.0001) 
adam = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)

model.compile(loss = 'mean_squared_error',
              optimizer = adam,
              metrics = ['accuracy'])

nb_train_samples = 41322
nb_validation_samples = 13877
epochs = 10

history = model.fit_generator(
    train_generator,
    steps_per_epoch = nb_train_samples // batch_size,
    epochs = epochs,
    callbacks = callbacks,
    validation_data = validation_generator,
    validation_steps = nb_validation_samples // batch_size)