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model.py
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import tensorflow as tf
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Conv2DTranspose
from tensorflow.keras.layers import concatenate
def unet(flags_obj, n_filters=64):
# Contracting Path (encoding)
inputs = Input(flags_obj.input_size)
conv1 = Conv2D(n_filters,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(n_filters,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(n_filters * 2,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(n_filters * 2,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(n_filters * 4,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(n_filters * 4,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(n_filters * 8,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(n_filters * 8,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.3)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(n_filters * 16,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(n_filters * 16,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.3)(conv5)
# Expansive Path (decoding)
up6 = Conv2DTranspose(n_filters * 8, (3, 3), strides=(2, 2),
padding='same')(drop5)
merge6 = concatenate([up6, drop4], axis=3)
conv6 = Conv2D(n_filters * 8,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(n_filters * 8,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv6)
up7 = Conv2DTranspose(n_filters * 4, (3, 3), strides=(2, 2),
padding='same')(conv6)
merge7 = concatenate([up7, conv3], axis=3)
conv7 = Conv2D(n_filters * 4,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(n_filters * 4,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv7)
up8 = Conv2DTranspose(n_filters * 2, (3, 3), strides=(2, 2),
padding='same')(conv7)
merge8 = concatenate([up8, conv2], axis=3)
conv8 = Conv2D(n_filters * 2,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(n_filters * 2,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv8)
up9 = Conv2DTranspose(n_filters, (3, 3), strides=(2, 2),
padding='same')(conv8)
merge9 = concatenate([up9, conv1], axis=3)
conv9 = Conv2D(n_filters,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(n_filters,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(2,
3,
activation='relu',
padding='same',
kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
model = tf.keras.Model(inputs=inputs, outputs=conv10)
return model