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model.py
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model.py
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import keras
from keras.callbacks import CSVLogger
from keras.models import Model
from keras.layers import (Input, Conv2D, MaxPooling2D, UpSampling2D, concatenate, Dropout)
import tensorflow as tf
import numpy as np
np.set_printoptions(precision=3, suppress=True) # Make numpy printouts easier to read.
def build_unet(inputs, ker_init, dropout):
conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(inputs)
conv1 = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv1)
pool = MaxPooling2D(pool_size=(2, 2))(conv1)
conv = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool)
conv = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv3)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(pool4)
conv5 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv5)
drop5 = Dropout(dropout)(conv5)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = 2)(drop5))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = 2)(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = 2)(conv8))
merge9 = concatenate([conv,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv9)
up = Conv2D(32, 2, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(UpSampling2D(size = 2)(conv9))
merge = concatenate([conv1,up], axis = 3)
conv = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(merge)
conv = Conv2D(32, 3, activation = 'relu', padding = 'same', kernel_initializer = ker_init)(conv)
conv10 = Conv2D(4, 1, activation = 'softmax')(conv)
return Model(inputs = inputs, outputs = conv10)