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solarPV_identification/CNN/final_run.py
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final_run.py
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import data_loaders
import datetime
import losses
import sys
# import cv2
from scipy import ndimage
from matplotlib import pyplot as plt
from PIL import Image
from keras.models import *
from keras.layers import Reshape
from keras.layers.core import *
from keras.layers import *
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import *
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import Adam, SGD
from keras.utils import np_utils
from keras import backend as K
def unet(nClasses, loss, optimizer=None, input_width=360, input_height=480, nChannels=1):
ker = 3
pad = 3
dropout_rate = 0.2
inputs = Input((input_height, input_width, nChannels))
inputs_padded = ZeroPadding2D(padding=pad, input_shape=(input_width, input_height, 3))(inputs)
conv1 = Conv2D(32, kernel_size=ker, activation='relu', padding='same')(inputs_padded)
conv1 = Dropout(dropout_rate)(conv1)
conv1 = Conv2D(32, kernel_size=ker, activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(padding='same')(conv1)
conv2 = Conv2D(64, kernel_size=ker, activation='relu', padding='same')(pool1)
conv2 = Dropout(dropout_rate)(conv2)
conv2 = Conv2D(64, kernel_size=ker, activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(padding='same')(conv2)
conv3 = Conv2D(128, kernel_size=ker, activation='relu', padding='same')(pool2)
conv3 = Dropout(dropout_rate)(conv3)
conv3 = Conv2D(128, kernel_size=ker, activation='relu', padding='same')(conv3)
up1 = concatenate([UpSampling2D(size=(2, 2))(conv3), conv2], axis=3)
conv4 = Conv2D(64, kernel_size=ker, activation='relu', padding='same')(up1)
conv4 = Dropout(dropout_rate)(conv4)
conv4 = Conv2D(64, kernel_size=ker, activation='relu', padding='same')(conv4)
up2 = concatenate([UpSampling2D(size=(2, 2))(conv4), conv1], axis=3)
conv5 = Conv2D(32, kernel_size=ker, activation='relu', padding='same')(up2)
conv5 = Dropout(dropout_rate)(conv5)
conv5 = Conv2D(32, kernel_size=ker, activation='relu', padding='same')(conv5)
conv6 = Conv2D(nClasses, kernel_size=1, activation='relu', padding='same')(conv5)
cropped_conv6 = Cropping2D(cropping=pad)(conv6)
conv6 = core.Reshape((input_height * input_width, nClasses))(cropped_conv6)
conv7 = core.Activation('softmax')(conv6)
model = Model(input=inputs, output=conv7)
if not optimizer is None:
model.compile(loss=loss, optimizer=optimizer, metrics=[losses.dice_coef])
print("Model was compiled.")
return model
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