TMVA_SOFIE_Models.py File Reference

Detailed Description

Example of inference with SOFIE using a set of models trained with Keras.

This tutorial shows how to store several models in a single header file and the weights in a ROOT binary file. The models are then evaluated using the RDataFrame First, generate the input model by running TMVA_Higgs_Classification.C.

This tutorial parses the input model and runs the inference using ROOT's JITing capability.

 
import os
 
import numpy as np
import ROOT
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
 
## generate and train Keras models with different architectures
 
 
def CreateModel(nlayers = 4, nunits = 64):
   model = Sequential()
   model.add(Dense(nunits, activation='relu',input_dim=7))
   for i in range(1,nlayers) :
      model.add(Dense(nunits, activation='relu'))
 
   model.add(Dense(1, activation='sigmoid'))
   model.compile(loss = 'binary_crossentropy', optimizer = Adam(learning_rate = 0.001), weighted_metrics = ['accuracy'])
   model.summary()
   return model
 
def PrepareData() :
   #get the input data
   inputFile = str(ROOT.gROOT.GetTutorialDir()) + "/machine_learning/data/Higgs_data.root"
 
   df1 = ROOT.RDataFrame("sig_tree", inputFile)
   sigData = df1.AsNumpy(columns=['m_jj', 'm_jjj', 'm_lv', 'm_jlv', 'm_bb', 'm_wbb', 'm_wwbb'])
   #print(sigData)
 
   # stack all the 7 numpy array in a single array (nevents x nvars)
   xsig = np.column_stack(list(sigData.values()))
   data_sig_size = xsig.shape[0]
   print("size of data", data_sig_size)
 
   # make SOFIE inference on background data
   df2 = ROOT.RDataFrame("bkg_tree", inputFile)
   bkgData = df2.AsNumpy(columns=['m_jj', 'm_jjj', 'm_lv', 'm_jlv', 'm_bb', 'm_wbb', 'm_wwbb'])
   xbkg = np.column_stack(list(bkgData.values()))
   data_bkg_size = xbkg.shape[0]
 
   ysig = np.ones(data_sig_size)
   ybkg = np.zeros(data_bkg_size)
   inputs_data = np.concatenate((xsig,xbkg),axis=0)
   inputs_targets = np.concatenate((ysig,ybkg),axis=0)
 
   #split data in training and test data
 
   x_train, x_test, y_train, y_test = train_test_split(
        inputs_data, inputs_targets, test_size=0.50, random_state=1234)
 
   return x_train, y_train, x_test, y_test
 
def TrainModel(model, x, y, name) :
   model.fit(x,y,epochs=10,batch_size=50)
   modelFile = name + '.keras'
   model.save(modelFile)
   return modelFile
 
### run the models
 
x_train, y_train, x_test, y_test = PrepareData()
 
## create models and train them
 
model1 = TrainModel(CreateModel(4,64),x_train, y_train, 'Higgs_Model_4L_50')
model2 = TrainModel(CreateModel(4,64),x_train, y_train, 'Higgs_Model_4L_200')
model3 = TrainModel(CreateModel(4,64),x_train, y_train, 'Higgs_Model_2L_500')
 
#evaluate with SOFIE the 3 trained models
 
 
def GenerateModelCode(modelFile, generatedHeaderFile):
   model = ROOT.TMVA.Experimental.SOFIE.PyKeras.Parse(modelFile)
 
   print("Generating inference code for the Keras model from ",modelFile,"in the header ", generatedHeaderFile)
   #Generating inference code using a ROOT binary file
   model.Generate(ROOT.TMVA.Experimental.SOFIE.Options.kRootBinaryWeightFile)
   # add option to append to the same file the generated headers (pass True for append flag)
   model.OutputGenerated(generatedHeaderFile, True)
   #model.PrintGenerated()
   return generatedHeaderFile
 
 
generatedHeaderFile = "Higgs_Model.hxx"
#need to remove existing header file since we are appending on same one
if (os.path.exists(generatedHeaderFile)):
   weightFile = "Higgs_Model.root"
   print("removing existing files", generatedHeaderFile,weightFile)
   os.remove(generatedHeaderFile)
   os.remove(weightFile)
 
GenerateModelCode(model1, generatedHeaderFile)
GenerateModelCode(model2, generatedHeaderFile)
GenerateModelCode(model3, generatedHeaderFile)
 
#compile the generated code
 
ROOT.gInterpreter.Declare('#include "' + generatedHeaderFile + '"')
 
 
#run the inference on the test data
session1 = ROOT.TMVA_SOFIE_Higgs_Model_4L_50.Session("Higgs_Model.root")
session2 = ROOT.TMVA_SOFIE_Higgs_Model_4L_200.Session("Higgs_Model.root")
session3 = ROOT.TMVA_SOFIE_Higgs_Model_2L_500.Session("Higgs_Model.root")
 
hs1 = ROOT.TH1D("hs1","Signal result 4L 50",100,0,1)
hs2 = ROOT.TH1D("hs2","Signal result 4L 200",100,0,1)
hs3 = ROOT.TH1D("hs3","Signal result 2L 500",100,0,1)
 
hb1 = ROOT.TH1D("hb1","Background result 4L 50",100,0,1)
hb2 = ROOT.TH1D("hb2","Background result 4L 200",100,0,1)
hb3 = ROOT.TH1D("hb3","Background result 2L 500",100,0,1)
 
def EvalModel(session, x) :
   result = session.infer(x)
   return result[0]
 
for i in range(0,x_test.shape[0]):
   result1 = EvalModel(session1, x_test[i,:])
   result2 = EvalModel(session2, x_test[i,:])
   result3 = EvalModel(session3, x_test[i,:])
   if (y_test[i] == 1) :
      hs1.Fill(result1)
      hs2.Fill(result2)
      hs3.Fill(result3)
   else:
      hb1.Fill(result1)
      hb2.Fill(result2)
      hb3.Fill(result3)
 
def PlotHistos(hs,hb):
   hs.SetLineColor("kRed")
   hb.SetLineColor("kBlue")
   hs.Draw()
   hb.Draw("same")
 
c1 = ROOT.TCanvas()
c1.Divide(1,3)
c1.cd(1)
PlotHistos(hs1,hb1)
c1.cd(2)
PlotHistos(hs2,hb2)
c1.cd(3)
PlotHistos(hs3,hb3)
c1.Draw()
 
## draw also ROC curves
 
def GetContent(h) :
   n = h.GetNbinsX()
   x = ROOT.std.vector['float'](n)
   w = ROOT.std.vector['float'](n)
   for  i in range(0,n):
      x[i] = h.GetBinCenter(i+1)
      w[i] = h.GetBinContent(i+1)
   return x,w
 
def MakeROCCurve(hs, hb) :
   xs,ws = GetContent(hs)
   xb,wb = GetContent(hb)
   roc = ROOT.TMVA.ROCCurve(xs,xb,ws,wb)
   print("ROC integral for ",hs.GetName(), roc.GetROCIntegral())
   curve = roc.GetROCCurve()
   curve.SetName(hs.GetName())
   return roc,curve
 
c2 = ROOT.TCanvas()
 
r1,curve1 = MakeROCCurve(hs1,hb1)
curve1.SetLineColor("kRed")
curve1.Draw("AC")
 
r2,curve2 = MakeROCCurve(hs2,hb2)
curve2.SetLineColor("kBlue")
curve2.Draw("C")
 
r3,curve3 = MakeROCCurve(hs3,hb3)
curve3.SetLineColor("kGreen")
curve3.Draw("C")
 
c2.Draw()

size of data 10000
Model: "sequential"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense (Dense)                   │ (None, 64)             │           512 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_1 (Dense)                 │ (None, 64)             │         4,160 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_2 (Dense)                 │ (None, 64)             │         4,160 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_3 (Dense)                 │ (None, 64)             │         4,160 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_4 (Dense)                 │ (None, 1)              │            65 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 13,057 (51.00 KB)
 Trainable params: 13,057 (51.00 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2:06␛[0m 635ms/step - accuracy: 0.5800 - loss: 0.6844␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 60/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 856us/step - accuracy: 0.5333 - loss: 0.6851  ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m123/200␛[0m ␛[32m━━━━━━━━━━━━␛[0m␛[37m━━━━━━━━␛[0m ␛[1m0s␛[0m 830us/step - accuracy: 0.5507 - loss: 0.6795␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m190/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━␛[0m␛[37m━␛[0m ␛[1m0s␛[0m 802us/step - accuracy: 0.5612 - loss: 0.6761␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m1s␛[0m 855us/step - accuracy: 0.5891 - loss: 0.6659
Epoch 2/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 12ms/step - accuracy: 0.5000 - loss: 0.6758␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 67/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 770us/step - accuracy: 0.6218 - loss: 0.6466␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m130/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 782us/step - accuracy: 0.6248 - loss: 0.6451␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m192/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━␛[0m␛[37m━␛[0m ␛[1m0s␛[0m 793us/step - accuracy: 0.6264 - loss: 0.6443␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 842us/step - accuracy: 0.6294 - loss: 0.6419
Epoch 3/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 12ms/step - accuracy: 0.6400 - loss: 0.6139␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 63/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 809us/step - accuracy: 0.6424 - loss: 0.6292␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m125/200␛[0m ␛[32m━━━━━━━━━━━━␛[0m␛[37m━━━━━━━━␛[0m ␛[1m0s␛[0m 810us/step - accuracy: 0.6397 - loss: 0.6314␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m189/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━␛[0m␛[37m━━␛[0m ␛[1m0s␛[0m 802us/step - accuracy: 0.6402 - loss: 0.6316␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 853us/step - accuracy: 0.6447 - loss: 0.6302
Epoch 4/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 12ms/step - accuracy: 0.6800 - loss: 0.6179␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 67/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 770us/step - accuracy: 0.6656 - loss: 0.6136␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m139/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 734us/step - accuracy: 0.6598 - loss: 0.6155␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 797us/step - accuracy: 0.6532 - loss: 0.6212
Epoch 5/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.5800 - loss: 0.6339␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 67/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 766us/step - accuracy: 0.6430 - loss: 0.6219␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m139/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 731us/step - accuracy: 0.6481 - loss: 0.6210␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 767us/step - accuracy: 0.6561 - loss: 0.6159
Epoch 6/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.8000 - loss: 0.5666␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 74/200␛[0m ␛[32m━━━━━━━␛[0m␛[37m━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 687us/step - accuracy: 0.6721 - loss: 0.6091␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m144/200␛[0m ␛[32m━━━━━━━━━━━━━━␛[0m␛[37m━━━━━━␛[0m ␛[1m0s␛[0m 706us/step - accuracy: 0.6678 - loss: 0.6086␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 767us/step - accuracy: 0.6658 - loss: 0.6087
Epoch 7/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.6000 - loss: 0.6468␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 71/200␛[0m ␛[32m━━━━━━━␛[0m␛[37m━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 724us/step - accuracy: 0.6526 - loss: 0.6179␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m141/200␛[0m ␛[32m━━━━━━━━━━━━━━␛[0m␛[37m━━━━━━␛[0m ␛[1m0s␛[0m 722us/step - accuracy: 0.6580 - loss: 0.6121␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 780us/step - accuracy: 0.6715 - loss: 0.6003
Epoch 8/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.6600 - loss: 0.6231␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 68/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 747us/step - accuracy: 0.6807 - loss: 0.5937␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m138/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 733us/step - accuracy: 0.6792 - loss: 0.5936␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 783us/step - accuracy: 0.6767 - loss: 0.5964
Epoch 9/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 12ms/step - accuracy: 0.7400 - loss: 0.5821␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 68/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 757us/step - accuracy: 0.6588 - loss: 0.6192␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m133/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 767us/step - accuracy: 0.6650 - loss: 0.6121␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m197/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━␛[0m␛[37m━␛[0m ␛[1m0s␛[0m 772us/step - accuracy: 0.6689 - loss: 0.6079␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 821us/step - accuracy: 0.6773 - loss: 0.5973
Epoch 10/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.7400 - loss: 0.5192␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 68/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 755us/step - accuracy: 0.6778 - loss: 0.5883␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m133/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 763us/step - accuracy: 0.6765 - loss: 0.5908␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m195/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━␛[0m␛[37m━␛[0m ␛[1m0s␛[0m 777us/step - accuracy: 0.6755 - loss: 0.5918␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 844us/step - accuracy: 0.6723 - loss: 0.5955
Model: "sequential_1"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense_5 (Dense)                 │ (None, 64)             │           512 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_6 (Dense)                 │ (None, 64)             │         4,160 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_7 (Dense)                 │ (None, 64)             │         4,160 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_8 (Dense)                 │ (None, 64)             │         4,160 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_9 (Dense)                 │ (None, 1)              │            65 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 13,057 (51.00 KB)
 Trainable params: 13,057 (51.00 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m1:57␛[0m 589ms/step - accuracy: 0.4800 - loss: 0.6898␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 56/200␛[0m ␛[32m━━━━━␛[0m␛[37m━━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 912us/step - accuracy: 0.5338 - loss: 0.6878  ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m117/200␛[0m ␛[32m━━━━━━━━━━━␛[0m␛[37m━━━━━━━━━␛[0m ␛[1m0s␛[0m 866us/step - accuracy: 0.5545 - loss: 0.6809␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m184/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━␛[0m␛[37m━━␛[0m ␛[1m0s␛[0m 823us/step - accuracy: 0.5663 - loss: 0.6758␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m1s␛[0m 868us/step - accuracy: 0.5960 - loss: 0.6628
Epoch 2/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.6400 - loss: 0.6379␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 67/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 764us/step - accuracy: 0.6425 - loss: 0.6359␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m133/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 764us/step - accuracy: 0.6422 - loss: 0.6368␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 762us/step - accuracy: 0.6423 - loss: 0.6367␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 812us/step - accuracy: 0.6422 - loss: 0.6359
Epoch 3/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.6400 - loss: 0.6281␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 65/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 783us/step - accuracy: 0.6590 - loss: 0.6156␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m131/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 774us/step - accuracy: 0.6513 - loss: 0.6222␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m198/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━␛[0m␛[37m━␛[0m ␛[1m0s␛[0m 767us/step - accuracy: 0.6502 - loss: 0.6239␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 816us/step - accuracy: 0.6488 - loss: 0.6261
Epoch 4/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.6000 - loss: 0.6472␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 67/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 762us/step - accuracy: 0.6473 - loss: 0.6266␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m136/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 747us/step - accuracy: 0.6549 - loss: 0.6228␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 803us/step - accuracy: 0.6628 - loss: 0.6187
Epoch 5/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.7200 - loss: 0.6044␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 69/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 745us/step - accuracy: 0.6651 - loss: 0.6155␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m137/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 742us/step - accuracy: 0.6655 - loss: 0.6145␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 795us/step - accuracy: 0.6680 - loss: 0.6115
Epoch 6/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 12ms/step - accuracy: 0.6200 - loss: 0.6255␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 69/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 744us/step - accuracy: 0.6834 - loss: 0.6025␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m135/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 752us/step - accuracy: 0.6795 - loss: 0.6039␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 807us/step - accuracy: 0.6728 - loss: 0.6075
Epoch 7/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 12ms/step - accuracy: 0.5200 - loss: 0.6994␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 67/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 763us/step - accuracy: 0.6442 - loss: 0.6230␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m134/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 759us/step - accuracy: 0.6573 - loss: 0.6139␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m199/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━␛[0m␛[37m━␛[0m ␛[1m0s␛[0m 763us/step - accuracy: 0.6635 - loss: 0.6095␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 815us/step - accuracy: 0.6770 - loss: 0.6003
Epoch 8/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 12ms/step - accuracy: 0.6600 - loss: 0.6243␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 67/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 758us/step - accuracy: 0.6734 - loss: 0.6040␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m136/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 744us/step - accuracy: 0.6720 - loss: 0.6054␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 790us/step - accuracy: 0.6705 - loss: 0.6042
Epoch 9/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.5600 - loss: 0.6769␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 72/200␛[0m ␛[32m━━━━━━━␛[0m␛[37m━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 710us/step - accuracy: 0.6726 - loss: 0.6053␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m141/200␛[0m ␛[32m━━━━━━━━━━━━━━␛[0m␛[37m━━━━━━␛[0m ␛[1m0s␛[0m 721us/step - accuracy: 0.6767 - loss: 0.5998␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 772us/step - accuracy: 0.6792 - loss: 0.5947
Epoch 10/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.7800 - loss: 0.5703␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 70/200␛[0m ␛[32m━━━━━━━␛[0m␛[37m━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 733us/step - accuracy: 0.6892 - loss: 0.5934␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m136/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 748us/step - accuracy: 0.6882 - loss: 0.5908␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 800us/step - accuracy: 0.6796 - loss: 0.5950
Model: "sequential_2"
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ dense_10 (Dense)                │ (None, 64)             │           512 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_11 (Dense)                │ (None, 64)             │         4,160 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_12 (Dense)                │ (None, 64)             │         4,160 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_13 (Dense)                │ (None, 64)             │         4,160 │
├─────────────────────────────────┼────────────────────────┼───────────────┤
│ dense_14 (Dense)                │ (None, 1)              │            65 │
└─────────────────────────────────┴────────────────────────┴───────────────┘
 Total params: 13,057 (51.00 KB)
 Trainable params: 13,057 (51.00 KB)
 Non-trainable params: 0 (0.00 B)
Epoch 1/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m1:55␛[0m 583ms/step - accuracy: 0.5600 - loss: 0.6863␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 61/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 841us/step - accuracy: 0.5446 - loss: 0.6866  ␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m125/200␛[0m ␛[32m━━━━━━━━━━━━␛[0m␛[37m━━━━━━━━␛[0m ␛[1m0s␛[0m 814us/step - accuracy: 0.5568 - loss: 0.6816␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m192/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━␛[0m␛[37m━␛[0m ␛[1m0s␛[0m 792us/step - accuracy: 0.5657 - loss: 0.6784␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m1s␛[0m 841us/step - accuracy: 0.5881 - loss: 0.6696
Epoch 2/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.6400 - loss: 0.6106␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 68/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 758us/step - accuracy: 0.6252 - loss: 0.6473␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m134/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 761us/step - accuracy: 0.6281 - loss: 0.6462␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 798us/step - accuracy: 0.6344 - loss: 0.6439
Epoch 3/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.7600 - loss: 0.5702␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 65/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 784us/step - accuracy: 0.6579 - loss: 0.6269␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m134/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 757us/step - accuracy: 0.6521 - loss: 0.6290␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 792us/step - accuracy: 0.6422 - loss: 0.6329
Epoch 4/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.6200 - loss: 0.6194␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 68/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 751us/step - accuracy: 0.6503 - loss: 0.6261␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m136/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 744us/step - accuracy: 0.6528 - loss: 0.6244␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 792us/step - accuracy: 0.6528 - loss: 0.6244
Epoch 5/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.7000 - loss: 0.5892␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 70/200␛[0m ␛[32m━━━━━━━␛[0m␛[37m━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 731us/step - accuracy: 0.6544 - loss: 0.6160␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m138/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 736us/step - accuracy: 0.6558 - loss: 0.6162␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 786us/step - accuracy: 0.6614 - loss: 0.6151
Epoch 6/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.6600 - loss: 0.6373␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 69/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 741us/step - accuracy: 0.6570 - loss: 0.6125␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m138/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 734us/step - accuracy: 0.6591 - loss: 0.6126␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 793us/step - accuracy: 0.6667 - loss: 0.6110
Epoch 7/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 12ms/step - accuracy: 0.6800 - loss: 0.6113␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 65/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 787us/step - accuracy: 0.6712 - loss: 0.5985␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m131/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 776us/step - accuracy: 0.6649 - loss: 0.6048␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m196/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━␛[0m␛[37m━␛[0m ␛[1m0s␛[0m 775us/step - accuracy: 0.6638 - loss: 0.6064␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 825us/step - accuracy: 0.6628 - loss: 0.6077
Epoch 8/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.5200 - loss: 0.6201␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 68/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 747us/step - accuracy: 0.6557 - loss: 0.6099␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m136/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 743us/step - accuracy: 0.6641 - loss: 0.6041␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 784us/step - accuracy: 0.6713 - loss: 0.6006
Epoch 9/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.7400 - loss: 0.5689␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 71/200␛[0m ␛[32m━━━━━━━␛[0m␛[37m━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 724us/step - accuracy: 0.6834 - loss: 0.5980␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m143/200␛[0m ␛[32m━━━━━━━━━━━━━━␛[0m␛[37m━━━━━━␛[0m ␛[1m0s␛[0m 712us/step - accuracy: 0.6761 - loss: 0.6004␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 766us/step - accuracy: 0.6670 - loss: 0.6027
Epoch 10/10
 
␛[1m  1/200␛[0m ␛[37m━━━━━━━━━━━━━━━━━━━━␛[0m ␛[1m2s␛[0m 11ms/step - accuracy: 0.7000 - loss: 0.6003␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m 69/200␛[0m ␛[32m━━━━━━␛[0m␛[37m━━━━━━━━━━━━━━␛[0m ␛[1m0s␛[0m 739us/step - accuracy: 0.6900 - loss: 0.5870␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m138/200␛[0m ␛[32m━━━━━━━━━━━━━␛[0m␛[37m━━━━━━━␛[0m ␛[1m0s␛[0m 732us/step - accuracy: 0.6858 - loss: 0.5886␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈␈
␛[1m200/200␛[0m ␛[32m━━━━━━━━━━━━━━━━━━━━␛[0m␛[37m␛[0m ␛[1m0s␛[0m 772us/step - accuracy: 0.6788 - loss: 0.5953
 
Python error message:

Author

Lorenzo Moneta

Definition in file TMVA_SOFIE_Models.py.