code: from sklearn.model_selection import train_test_split
  import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt
  import tensorflow as tf
  
  def load_compilado(arquivo):
      path = '/home/luiza/UFSM/Myo/myo_project/datasets/oficial/' + arquivo
      df = pd.read_csv(path)
      return df
  
  df = load_compilado('features_balanced.csv')
  df_labels = df['Label']
  df = df.drop(columns=['Label'])
  
  x_train, x_test, y_train, y_test = train_test_split(df.values, df_labels.values, test_size=0.3, random_state=0)
  
  print('All Data:')
  print(y_train)
  print(y_test)
  
  from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense
  from tensorflow.keras.layers import LSTM
  from tensorflow.keras.layers import Dropout
  from tensorflow.keras.layers import Activation
  from tensorflow.keras.optimizers import Adam
  
  input_shape = (x_train.shape[1])
  print(input_shape, y_train.shape, x_train.shape)
  
  optimizer = Adam(lr=0.1, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.01, amsgrad=False)
  
  classifier = Sequential()
  classifier.add(Dense(32, input_dim=x_train.shape[1]))
  classifier.add(Activation('relu'))
  classifier.add(Dense(units = 64))
  classifier.add(Activation('relu'))
  classifier.add(Dense(units = 128))
  classifier.add(Activation('relu'))
  classifier.add(Dense(units = 1, activation="softmax"))
  
  classifier.summary()
  
  x_train = x_train.reshape(x_train.shape[0], x_train.shape[1])
  
  classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics=["accuracy"])
  
  classifier.fit(x_train, y_train, epochs = 10000, batch_size = 43, verbose=1)
  
  # Save
  classifier.save("model_cross_splited_data.h5")
  print("Saved model to disk")
  
  ###############################################
  
  from tensorflow import keras
  
  # # Load Model
  # model = keras.models.load_model('model_cross_splited_data.h5')
  # model.summary()
  
  def evaluateModel(prediction, y):
      good = 0
      for i in range(len(y)):
          if (prediction[i] == np.argmax(y[i])):
              good = good +1
      return (good/len(y)) * 100.0
  
  # result_test = classifier.predict_classes(X_test)
  # print("Correct classification rate on test data")
  # print(evaluateModel(result_test, y_test))
  
  result_train = classifier.predict_classes(x_train)
  print("Correct classification rate on train data")
  print(evaluateModel(result_train, y_train))
  
apis: tensorflow.keras.models.Sequential tensorflow.keras.optimizers.Adam pandas.read_csv tensorflow.keras.layers.Activation numpy.argmax tensorflow.keras.layers.Dense sklearn.model_selection.train_test_split