VGG16_oxflower17.py

import numpy as np
import keras
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten,Dropout
from keras.optimizers import Adam

#Load oxflower17 dataset
import tflearn.datasets.oxflower17 as oxflower17
from sklearn.model_selection import train_test_split
x, y = oxflower17.load_data(one_hot=True)

#Split train and test data
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2,shuffle = True)

#Data augumentation with Keras tools
from keras.preprocessing.image import ImageDataGenerator
img_gen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
    )

#Build VGG16Net model
def VGG16Net(width, height, depth, classes):
    
    model = Sequential()
    
    model.add(Conv2D(64,(3,3),strides=(1,1),input_shape=(224,224,3),padding='same',activation='relu'))
    model.add(Conv2D(64,(3,3),strides=(1,1),padding='same',activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Conv2D(128,(3,2),strides=(1,1),padding='same',activation='relu'))
    model.add(Conv2D(128,(3,3),strides=(1,1),padding='same',activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Conv2D(256,(3,3),strides=(1,1),padding='same',activation='relu'))
    model.add(Conv2D(256,(3,3),strides=(1,1),padding='same',activation='relu'))
    model.add(Conv2D(256,(3,3),strides=(1,1),padding='same',activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu'))
    model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu'))
    model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu'))
    model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu'))
    model.add(Conv2D(512,(3,3),strides=(1,1),padding='same',activation='relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Flatten())
    model.add(Dense(4096,activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(4096,activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1000,activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(17,activation='softmax'))
    
    return model
  
VGG16_model = VGG16Net(224,224,3,17)
VGG16_model.summary()
VGG16_model.compile(optimizer=Adam(lr=0.00001, beta_1=0.9, beta_2=0.999, epsilon=1e-08),loss = 'categorical_crossentropy',metrics=['accuracy'])

#Start training using dataaugumentation generator
History = VGG16_model.fit_generator(img_gen.flow(X_train*255, y_train, batch_size = 16),
                                      steps_per_epoch = len(X_train)/16, validation_data = (X_test,y_test), epochs = 30 )

#Plot Loss and Accuracy
import matplotlib.pyplot as plt
plt.figure(figsize = (15,5))
plt.subplot(1,2,1)
plt.plot(History.history['acc'])
plt.plot(History.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')

plt.subplot(1,2,2)
plt.plot(History.history['loss'])
plt.plot(History.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
plt.show()