HousePricePrediction: model class

HousePricePrediction.py

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+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):
+        # plot History
+        plt.plot(Hist.history['loss'])
+        plt.title('model loss')
+        plt.show()
+        plt.plot(Hist.history['mse'])
+        plt.title('model mse')
+        plt.show()