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from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, Activation, BatchNormalization, Dropout, MaxPool2D
from tensorflow.keras.layers import Flatten, Dense
from tensorflow import nn as tfn
import tensorflow.keras.backend as K
class MiniVgg:
@staticmethod
def build(width,height,depth,classes):
model=Sequential()
inputShape=(height,width,depth)
chanDim=-1
if K.image_data_format()=="channel_first":
inputShape=(depth,height,width)
chanDim=1
model.add(Conv2D(32,(5,5),input_shape=inputShape))
model.add(Activation(tfn.relu))
model.add(BatchNormalization(chanDim))
model.add(Conv2D(32, (5, 5)))
model.add(Activation(tfn.relu))
model.add(BatchNormalization(chanDim))
model.add(Conv2D(32, (5, 5)))
model.add(Activation(tfn.relu))
model.add(BatchNormalization(chanDim))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))
#-----------------------------------#
model.add(Conv2D(32, (5, 5), input_shape=inputShape))
model.add(Activation(tfn.relu))
model.add(BatchNormalization(chanDim))
model.add(Conv2D(32, (5, 5)))
model.add(Activation(tfn.relu))
model.add(BatchNormalization(chanDim))
model.add(Conv2D(64, (5, 5)))
model.add(Activation(tfn.relu))
model.add(BatchNormalization(chanDim))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
#-----------------------------#
model.add(Conv2D(64, (5, 5), input_shape=inputShape))
model.add(Activation(tfn.relu))
model.add(BatchNormalization(chanDim))
model.add(Conv2D(64, (5, 5)))
model.add(Activation(tfn.relu))
model.add(BatchNormalization(chanDim))
model.add(Conv2D(64, (5, 5)))
model.add(Activation(tfn.relu))
model.add(BatchNormalization(chanDim))
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
#-----------------------------#
model.add(Conv2D(64, (5, 5)))
model.add(Activation(tfn.relu))
model.add(BatchNormalization(chanDim))
model.add(Conv2D(64, (5, 5)))
model.add(Activation(tfn.relu))
model.add(BatchNormalization(chanDim))
model.add(Flatten())
model.add(Dense(1024))
model.add(Activation(tfn.relu))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(classes))
model.add(Activation(tfn.relu))
return model
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