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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()
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