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smallvggnet.py
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smallvggnet.py
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from keras.models import Sequential
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dropout
from keras.layers.core import Dense
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras import backend as K
class SmallVGGNet:
@staticmethod
def build(width, height, depth, classes):
# initialize the model
model = Sequential()
inputShape = (height, width, depth)
channelOrder = -1
# if we are using "channels first", update the input shape
# and channels dimension
if K.image_data_format() == "channels_first":
inputShape = (depth, height, width)
channelOrder = 1
#first conv-relu-conv-relu-pool
model.add(Conv2D(32, (3, 3), padding="same",
input_shape=inputShape))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=channelOrder))
model.add(Conv2D(32, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=channelOrder))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
#second conv-relu-conv-relu-pool
model.add(Conv2D(64, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=channelOrder))
model.add(Conv2D(64, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=channelOrder))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
#fc-relu
model.add(Flatten())
model.add(Dense(512))
model.add(Activation("relu"))
model.add(BatchNormalization())
model.add(Dropout(0.5))
#softmax classifier
model.add(Dense(classes))
model.add(Activation("softmax"))
# return network
return model