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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 <img src="tmva_logo.gif" height="20%" width="20%">
TMVA Classification Example Using a Convolutional Neural Network
Declare Factory
Create the Factory class. Later you can choose the methods
whose performance you'd like to investigate. 
The factory is the major TMVA object you have to interact with. Here is the list of parameters you need to pass
The first argument is the base of the name of all the output
weightfiles in the directory weight/ that will be created with the 
method parameters 
The second argument is the output file for the training results
The third argument is a string option defining some general configuration for the TMVA session. For example all TMVA output can be suppressed by removing the "!" (not) in front of the "Silent" argument in the option string
Step1:  Declare DataLoader(s)
The next step is to declare the DataLoader class that deals with input variables 
Define the input variables that shall be used for the MVA training
note that you may also use variable expressions, which can be parsed by TTree
Step2:  Setup Dataset(s)
Define input data file and signal and background trees
Step3:  Booking Methods
Here we book the TMVA methods. We book a DNN and a CNN
Booking Deep Neural Network
Here we book the new DNN of TMVA. If using master version you can use the new DL method
Step4:  Book Convolutional Neural Network in TMVA
Step5:  Book Convolutional Neural Network in Keras using a generated model
Step6:  Train Methods
Step7:  Test and Evaluate Methods
Step8:  Plot ROC Curve
We enable JavaScript visualisation for the plots
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Python Code:
import ROOT
from ROOT import TMVA
import os 
ROOT.TMVA.Tools.Instance()
TMVA.PyMethodBase.PyInitialize()
## For PYMVA methods
TMVA.PyMethodBase.PyInitialize();
outputFile = ROOT.TFile.Open("CNN_ClassificationOutput.root", "RECREATE")
factory = ROOT.TMVA.Factory("TMVA_CNN_Classification", outputFile,
                      "!V:ROC:!Silent:Color:!DrawProgressBar:AnalysisType=Classification" )
Explanation: <img src="tmva_logo.gif" height="20%" width="20%">
TMVA Classification Example Using a Convolutional Neural Network
Declare Factory
Create the Factory class. Later you can choose the methods
whose performance you'd like to investigate. 
The factory is the major TMVA object you have to interact with. Here is the list of parameters you need to pass
The first argument is the base of the name of all the output
weightfiles in the directory weight/ that will be created with the 
method parameters 
The second argument is the output file for the training results
The third argument is a string option defining some general configuration for the TMVA session. For example all TMVA output can be suppressed by removing the "!" (not) in front of the "Silent" argument in the option string
End of explanation
inputFileName = "images_data.root"
inputFile = ROOT.TFile.Open( inputFileName )
# retrieve input trees
signalTree     = inputFile.Get("sig_tree")
backgroundTree = inputFile.Get("bkg_tree")
signalTree.Print()
loader = ROOT.TMVA.DataLoader("dataset")
### global event weights per tree (see below for setting event-wise weights)
signalWeight     = 1.0
backgroundWeight = 1.0
   
### You can add an arbitrary number of signal or background trees
loader.AddSignalTree    ( signalTree,     signalWeight     )
loader.AddBackgroundTree( backgroundTree, backgroundWeight )
imgSize = 8 * 8; 
for  i in range(0,imgSize):
    varName = "var"+str(i)
    loader.AddVariable(varName,'F');
Explanation: Declare DataLoader(s)
The next step is to declare the DataLoader class that deals with input variables 
Define the input variables that shall be used for the MVA training
note that you may also use variable expressions, which can be parsed by TTree::Draw( "expression" )]
In this case the input data consists of an image of 16x16 pixels. Each single pixel is a branch in a ROOT TTree
End of explanation
## Apply additional cuts on the signal and background samples (can be different)
mycuts = ROOT.TCut("")   ## for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
mycutb = ROOT.TCut("")   ## for example: TCut mycutb = "abs(var1)<0.5";
loader.PrepareTrainingAndTestTree( mycuts, mycutb,
                                  "nTrain_Signal=5000:nTrain_Background=5000:SplitMode=Random:"
                                   "NormMode=NumEvents:!V" )
Explanation: Setup Dataset(s)
Define input data file and signal and background trees
End of explanation
inputLayoutString = "InputLayout=1|1|64"; 
batchLayoutString= "BatchLayout=1|32|64";
layoutString = ("Layout=DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|64|TANH,DENSE|1|LINEAR")
training1  = "Optimizer=ADAM,LearningRate=1e-3,Momentum=0.,Regularization=None,WeightDecay=1e-4,"
training1 += "DropConfig=0.+0.+0.+0.,MaxEpochs=30,ConvergenceSteps=10,BatchSize=32,TestRepetitions=1"
trainingStrategyString = "TrainingStrategy=" + training1
dnnOptions = "!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=G:WeightInitialization=XAVIER::Architecture=CPU"
dnnOptions +=  ":" + inputLayoutString
dnnOptions +=  ":" + batchLayoutString
dnnOptions +=  ":" + layoutString
dnnOptions +=  ":" + trainingStrategyString
#we can now book the method
              
factory.BookMethod(loader, ROOT.TMVA.Types.kDL, "DL_DENSE", dnnOptions)
Explanation: Booking Methods
Here we book the TMVA methods. We book a DNN and a CNN
Booking Deep Neural Network
Here we book the new DNN of TMVA. If using master version you can use the new DL method
End of explanation
#input layout 
inputLayoutString = "InputLayout=1|8|8"
                                                                                                
## Batch Layout                                                                                                                                     
batchLayoutString = "BatchLayout=128|1|64"
                                                   
layoutString = ("Layout=CONV|10|3|3|1|1|1|1|RELU,CONV|10|3|3|1|1|1|1|RELU,MAXPOOL|2|2|1|1,"
            "RESHAPE|FLAT,DENSE|64|TANH,DENSE|1|LINEAR")
                                                                                                                                              
##Training strategies.                                                                                                                          
training1 = ("LearningRate=1e-3,Momentum=0.9,Repetitions=1,"
                     "ConvergenceSteps=10,BatchSize=128,TestRepetitions=1,"
                     "MaxEpochs=20,WeightDecay=1e-4,Regularization=None,"
                     "Optimizer=ADAM,DropConfig=0.0+0.0+0.0+0.0")
 
trainingStrategyString = "TrainingStrategy=" + training1
    
## General Options.                                                                                                                              
cnnOptions = ("!H:V:ErrorStrategy=CROSSENTROPY:VarTransform=None:"
                       "WeightInitialization=XAVIERUNIFORM");
cnnOptions +=  ":" + inputLayoutString
cnnOptions +=  ":" + batchLayoutString
cnnOptions +=  ":" + layoutString
cnnOptions +=  ":" + trainingStrategyString
cnnOptions +=  ":Architecture=CPU"
##book CNN
factory.BookMethod(loader, ROOT.TMVA.Types.kDL, "DL_CNN", cnnOptions);
Explanation: Book Convolutional Neural Network in TMVA
End of explanation
## to use tensorflow backend
import os
##os.environ["KERAS_BACKEND"] = "tensorflow"
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam, SGD
from tensorflow.keras.initializers import TruncatedNormal
from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Reshape, BatchNormalization
model = Sequential()
model.add(Reshape((8,8, 1), input_shape=(64,)))
model.add(Conv2D(10, kernel_size=(3,3), kernel_initializer='TruncatedNormal', activation='relu', padding='same' ) )
model.add(Conv2D(10, kernel_size=(3,3), kernel_initializer='TruncatedNormal', activation='relu', padding='same' ) )
#stride for maxpool is equal to pool size
model.add(MaxPooling2D(pool_size=(2, 2) ))
model.add(Flatten())
model.add(Dense(64, activation='tanh'))
#model.add(Dropout(0.2))
model.add(Dense(2, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy'])
model.save('model_cnn.h5')
model.summary()
factory.BookMethod(loader, ROOT.TMVA.Types.kPyKeras, 
                       "PyKeras","H:!V:VarTransform=None:FilenameModel=model_cnn.h5:"
                       "FilenameTrainedModel=trained_model_cnn.h5:NumEpochs=20:BatchSize=128");
Explanation: Book Convolutional Neural Network in Keras using a generated model
End of explanation
factory.TrainAllMethods();
Explanation: Train Methods
End of explanation
factory.TestAllMethods();
factory.EvaluateAllMethods();    
Explanation: Test and Evaluate Methods
End of explanation
//%jsroot on
c1 = factory.GetROCCurve(loader)
c1.Draw()
## close outputfile to save output file
outputFile.Close()
Explanation: Plot ROC Curve
We enable JavaScript visualisation for the plots
End of explanation
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