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modelIL.py
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modelIL.py
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import numpy as np
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras
import tensorflow as tf
from keras import initializers
from keras import regularizers
def fu(pretrained_weights=None, input_size=(256, 256, 3)):
inputs = Input(input_size)
paddings = tf.constant([[0, 0], [1, 1], [1, 1], [0, 0], [0, 0]]) # only pads dim 2 and 3 (h and w)
[ inputtemp, inputspet,inputsct] = Lambda(tf.split, arguments={'axis': 3, 'num_or_size_splits': 3})(inputs)
inputs_temp = Lambda(tf.add, arguments={'y': (inputsct)})(inputspet)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs_temp)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
up5 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(pool4))
conv5 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up5)
conv5 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
up6 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv5))
conv6 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up6)
conv6 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
up7 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv6))
conv7 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up7)
conv7 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
up8 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv7))
conv8 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(up8)
conv8 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
conv9 = Conv2D(4, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
model = Model(input=inputs, output=conv10)
model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
if (pretrained_weights):
model.load_weights(pretrained_weights)
return model
def expand_dim_backend(x,dim):
xe = K.expand_dims(x, dim)
return xe