VGG.py

#!/usr/bin/env python
# coding: utf-8

# In[ ]:


# VGG (VGG16Net)

from keras.optimizers import Adam
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten,Dropout
from keras.layers import BatchNormalization,AveragePooling2D,concatenate
from keras.layers import Input, concatenate
from keras.layers import Add
from keras.layers import GlobalAveragePooling2D
from keras.models import 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( classes ,activation='softmax'))
    
    return model
  
# VGG16_model = VGG16Net(224,224,3,17)
VGG16_model = VGG16Net(224,224,3,2)

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'])

model = VGG16_model