| import os |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' |
| import tensorflow as tf |
| from matplotlib import pyplot as plt |
| from tensorflow.keras import layers as ksl |
| import numpy as np |
| import pandas as pd |
|
|
| class Model(): |
| def __init__(self,loadWeights = False,loadModel = False,weightAddr = None,modelAddr=None): |
| if loadModel: |
| self.net = self.loadModel(modelAddr) |
| elif not(loadModel and loadWeights): |
| self.net = self.buildModel() |
| elif loadWeights: |
| self.net = self.loadWeights() |
| self.loadWeights(weightAddr) |
| else : |
| print('[Error] incompatible inputs !') |
|
|
| self.const = None |
| |
|
|
|
|
|
|
| def myLoss(y_true,y_pred): |
| return tf.reduce_mean(tf.square(y_true - y_pred),axis = -1) |
| def compile(self,loss,metrics,optim): |
| self.net.compile(loss = loss,optimizer = optim ,metrics = metrics) |
|
|
| def train(self,e,b,Data,label): |
| hist = self.net.fit(Data,label,epochs = e,batch_size = b) |
| return hist |
|
|
| def buildModel(self): |
| inpIm1 = ksl.Input((128,128,3),name = 'image1Input') |
| inpIm2 = ksl.Input((128,128,3),name = 'image2Input') |
| inpIm3 = ksl.Input((128,128,3),name = 'image3Input') |
| inpIm4 = ksl.Input((128,128,3),name = 'image4Input') |
| |
| x = ksl.Conv2D(32,kernel_size=3,strides=1,padding='same')(inpIm1) |
| x = ksl.Lambda(self.relu)(x) |
| x = ksl.MaxPool2D([2,2])(x) |
| x = ksl.Conv2D(64,kernel_size=3,padding='same')(x) |
| x = ksl.Lambda(self.relu)(x) |
| x = ksl.MaxPool2D([2,2])(x) |
| x = ksl.Conv2D(128,kernel_size=3,padding='same')(x) |
| x = ksl.Lambda(self.relu)(x) |
| x = ksl.MaxPool2D([2,2])(x) |
|
|
| w = ksl.Conv2D(32,kernel_size=3,strides=1,padding='same')(inpIm2) |
| w = ksl.Lambda(self.relu)(w) |
| w = ksl.MaxPool2D([2,2])(w) |
| w = ksl.Conv2D(64,kernel_size=3,padding='same')(w) |
| w = ksl.Lambda(self.relu)(w) |
| w = ksl.MaxPool2D([2,2])(w) |
| w = ksl.Conv2D(128,kernel_size=3,padding='same')(w) |
| w = ksl.Lambda(self.relu)(w) |
| w = ksl.MaxPool2D([2,2])(w) |
|
|
| y = ksl.Conv2D(32,kernel_size=3,strides=1,padding='same')(inpIm3) |
| y=ksl.Lambda(self.relu)(y) |
| y = ksl.MaxPool2D([2,2])(y) |
| y = ksl.Conv2D(64,kernel_size=3,padding='same')(y) |
| y = ksl.Lambda(self.relu)(y) |
| y = ksl.MaxPool2D([2,2])(y) |
| y = ksl.Conv2D(128,kernel_size=3,padding='same')(y) |
| y = ksl.Lambda(self.relu)(y) |
| y = ksl.MaxPool2D([2,2])(y) |
|
|
| z = ksl.Conv2D(32,kernel_size=3,strides=1,padding='same')(inpIm4) |
| z = ksl.MaxPool2D([2,2])(z) |
| z = ksl.Conv2D(64,kernel_size=3,padding='same')(z) |
| z = ksl.MaxPool2D([2,2])(z) |
| z = ksl.Conv2D(64,kernel_size=3,padding='same')(z) |
| z = ksl.MaxPool2D([2,2])(z) |
| |
|
|
| inpTxtFeatures = ksl.Input((4,),name = 'numFeatureInput') |
| t = ksl.Dense(1024,'relu')(inpTxtFeatures) |
| t = ksl.Dense(512,'relu')(t) |
| t = ksl.Dense(256,'relu')(t) |
| t = ksl.Reshape((16,16,1))(t) |
|
|
|
|
|
|
| concatInput = ksl.concatenate([w,x,y,z,t],axis=3) |
| out = ksl.Conv2D(128,kernel_size=3,padding='same')(concatInput) |
| out = ksl.MaxPool2D([2,2])(out) |
| out = ksl.Conv2D(256,kernel_size=3,padding='same')(out) |
| out = ksl.MaxPool2D([2,2])(out) |
| out = ksl.Flatten()(out) |
| out = ksl.Dense(128,'relu')(out) |
| out = ksl.Dense(64,'relu')(out) |
| out = ksl.Dense(1,'linear')(out) |
| net = tf.keras.Model(inputs = [inpIm1,inpIm2,inpIm3,inpIm4,inpTxtFeatures],outputs = out) |
| return net |
| def predict(self,inputList,const): |
| numericFeatur = inputList[4:] |
| numericFeatur = pd.DataFrame(numericFeatur) |
| max = numericFeatur.max() |
| numericFeatur = numericFeatur/max |
| images = inputList[:4] |
| images = [self.preProcess(im) for im in images] |
| pred = self.net.predict(np.concatenate((images,numericFeatur.to_numpy()))) |
| return pred*const |
|
|
| def saveModel(self): |
| self.net.save('./Models/model.h5') |
| self.net.save_weights('./Models/modelWeights.h5') |
| def loadWeights(self,weightAddr): |
| net = self.buildModel() |
| net.load_weghts(weightAddr) |
| return net |
| @staticmethod |
| def loadModel(modelAddr): |
| net = tf.kerasl.models.load_model(modelAddr) |
| return net |
|
|
| |
| return image |
| @staticmethod |
| def relu(x): |
| return tf.maximum( 0.0,x) |
| |
| @staticmethod |
| def plotHistory(Hist): |
| |
| plt.plot(Hist.history['loss']) |
| plt.title('model loss') |
| plt.show() |
| plt.plot(Hist.history['mse']) |
| plt.title('model mse') |
| plt.show() |
|
|
