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unet.py
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unet.py
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import tensorflow as tf
from tensorflow import keras
def model(num_classes=19, input_size=(1024,1024,3)):
### Beginning of encoder ###
inputs = keras.Input(input_size)
#
conv1 = keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = keras.layers.MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = keras.layers.MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = keras.layers.MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = keras.layers.Dropout(0.5)(conv4)
pool4 = keras.layers.MaxPooling2D(pool_size=(2, 2))(drop4)
#
conv5 = keras.layers.Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = keras.layers.Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = keras.layers.Dropout(0.5)(conv5)
up6 = keras.layers.Conv2DTranspose(512, (2, 2), strides=(2,2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(drop5)
merge6 = keras.layers.concatenate([drop4,up6], axis = 3)
conv6 = keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = keras.layers.Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = keras.layers.Conv2DTranspose(256, (2, 2), strides=(2,2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
merge7 = keras.layers.concatenate([conv3,up7], axis = 3)
conv7 = keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = keras.layers.Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = keras.layers.Conv2DTranspose(128, (2, 2), strides=(2,2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
merge8 = keras.layers.concatenate([conv2,up8], axis = 3)
conv8 = keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = keras.layers.Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
merge9 = keras.layers.concatenate([conv1,up9], axis = 3)
conv9 = keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = keras.layers.Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = keras.layers.Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = keras.layers.Conv2D(num_classes, kernel_size=(1,1), strides=(1,1), activation = 'softmax', dtype=tf.float32)(conv9)
model = keras.Model(inputs = inputs, outputs = conv10)
return model