code: import EELSpecNet
  import GenerateData as gene
  import tensorflow as tf
  import numpy as np
  import tensorflow.experimental.numpy as tnp
  tnp.experimental_enable_numpy_behavior()
  
  def main():
  
      model = EELSpecNet.EELSpecNetModel_CNN_10D(2048)
      op = tf.keras.optimizers.Adam(learning_rate=5e-5)
      model.compile(optimizer=op, loss='BinaryCrossentropy', metrics=['mape', 'mse'])
  
      train_target, train_initial = gene.training_signal_set(6000, -2, 0.005, 0.015, 2048, 0.05)
      print("---------------- Training signal generation done !!! ----------------------")
  
      tnp_convolved_loaded = tnp.asarray(train_initial)
      tnp_original_loaded = tnp.asarray(train_target)
      x_dim, e_dim = np.shape(train_initial)
      tnp_original_loaded += 0.001
      tnp_convolved_loaded += 0.001
      tnp_data_original = tnp_original_loaded.reshape((x_dim, 1, e_dim, 1))
      tnp_data_convolved = tnp_convolved_loaded.reshape((x_dim, 1, e_dim, 1))
      tnp_train_original = tnp_data_original[:, :, :, :]
      tnp_train_convolved = tnp_data_convolved[:, :, :, :]
  
      model.fit(tnp_train_convolved, tnp_train_original, validation_split=0.16, batch_size=16, epochs=1000)
      print("------------------------ Training done !!! ------------------------")
  
      eval_target, eval_initial, eval_peaks, eval_psf, eval_metadata = gene.eval_signal_set(2000, -2, 0.005, 0.015, 2048,
                                                                                            0.05)
      print("---------------- Evaluation signal generation done !!! ----------------------")
  
      eval_target += 0.001
      eval_initial += 0.001
      eval_target = eval_target.reshape((2000, 1, 2048, 1))
      eval_initial = eval_initial.reshape((2000, 1, 2048, 1))
  
      model.evaluate(eval_initial, eval_target)
      prediction = model.predict(eval_initial)
      prediction = prediction.reshape((2000, 2048))
      np.save("deconv_evaluation_signal.npy", prediction)
  
  if __name__ == "__main__":
      main()
apis: numpy.save tensorflow.keras.optimizers.Adam tensorflow.experimental.numpy.asarray numpy.shape tensorflow.experimental.numpy.experimental_enable_numpy_behavior