[CODE] VGG19

4. VGG19

https://www.researchgate.net/profile/Clifford-Yang/publication/325137356/figure/fig2/AS:670371271413777@1536840374533/llustration-of-the-network-architecture-of-VGG-19-model-conv-means-convolution-FC-means.jpg

## import 
import numpy as np
import keras 
import tensorflow as tf
from tensorflow.keras.datasets import cifar100
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from keras.applications import VGG19
from tensorflow.keras.optimizers import Adam, SGD

## set
batch_size = 128
num_classes = 100
epochs = 50
learn_rate=.001

## splilt
(X_train, y_train), (X_test, y_test) = cifar100.load_data()

## pixel to 0~1
X_train = X_train / 255.0 
X_test = X_test / 255.0 

## Build model
vgg19 = VGG19(include_top=False, weights='imagenet', input_shape=(32,32,3), classes=y_train.shape[1])

model= Sequential()
model.add(vgg19) 
model.add(Flatten()) 
# Dense layers
model.add(Dense(1024,activation=('relu'),input_dim=512))
model.add(Dense(512,activation=('relu'))) 
model.add(Dense(256,activation=('relu'))) 
model.add(Dropout(.3))
model.add(Dense(128,activation=('relu')))
model.add(Dropout(.2))
model.add(Dense(num_classes,activation=('softmax')))

## summary
model_1.summary()

## Train
early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1)
adam=Adam(lr=learn_rate, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)

model_1.compile(optimizer=adam,loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model_1.fit(X_train, y_train, batch_size=batch_size,
                    validation_data=(X_test, y_test),
                    callbacks=[early_stop],
                    epochs=epochs, verbose=1)
                    
## Visualize history
import matplotlib.pyplot as plt
f,ax=plt.subplots(2,1) #Creates 2 subplots under 1 column

ax[0].plot(model_1.history.history['loss'],color='b',label='Training Loss')
ax[0].plot(model_1.history.history['val_loss'],color='r',label='Validation Loss')

ax[1].plot(model_1.history.history['accuracy'],color='b',label='Training  Accuracy')
ax[1].plot(model_1.history.history['val_accuracy'],color='r',label='Validation Accuracy')