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build_model.py
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build_model.py
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from keras import models
from keras.layers.core import Activation, Reshape, Permute
from keras.layers.convolutional import Convolution2D, MaxPooling2D, UpSampling2D
from keras.layers.normalization import BatchNormalization
import json
img_w = 256
img_h = 256
n_labels = 2
kernel = 3
encoding_layers = [
Convolution2D(64, kernel, border_mode='same', input_shape=( img_h, img_w,1)),
BatchNormalization(),
Activation('relu'),
Convolution2D(64, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(),
Convolution2D(128, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(128, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
MaxPooling2D(),
]
autoencoder = models.Sequential()
autoencoder.encoding_layers = encoding_layers
for l in autoencoder.encoding_layers:
autoencoder.add(l)
print(l.input_shape,l.output_shape,l)
decoding_layers = [
UpSampling2D(),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
UpSampling2D(),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(512, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
UpSampling2D(),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(256, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(128, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
UpSampling2D(),
Convolution2D(128, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(64, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
UpSampling2D(),
Convolution2D(64, kernel, kernel, border_mode='same'),
BatchNormalization(),
Activation('relu'),
Convolution2D(n_labels, 1, 1, border_mode='valid'),
BatchNormalization(),
]
autoencoder.decoding_layers = decoding_layers
for l in autoencoder.decoding_layers:
autoencoder.add(l)
autoencoder.add(Reshape((n_labels, img_h * img_w)))
autoencoder.add(Permute((2, 1)))
autoencoder.add(Activation('softmax'))
with open('model_5l.json', 'w') as outfile:
outfile.write(json.dumps(json.loads(autoencoder.to_json()), indent=2))