def load_custom_gru():
  root.update()
  filename = filedialog.askopenfilename(initialdir = os.getcwd(), title = 'Select file', filetypes=[('Numpy Files', '*.npz')])
  if filename != "":
    try:
      model = GRU(dictionary_size)
      log_message = model.load_model_parameters(filename)
      generate_sonnet1_button['state'] = 'normal'
      generate_sonnet2_button['state'] = 'normal'
      bigram_flag = False
      write_to_log(log_message, logger_index)
      current_model.set("Current Model: Custom GRU")
    except(FileNotFoundError):
      write_to_log("No such file or directory: %s" %filename, logger_index)
      return
Exemplo n.º 2
0
def load_custom_gru():
  root.update()
  filename = filedialog.askopenfilename(initialdir = os.getcwd(), title = 'Select file', filetypes=[('Numpy Files', '*.npz')])
  if filename != "":
    try:
      model = GRU(dictionary_size)
      log_message = model.load_model_parameters(filename)
      generate_sonnet1_button['state'] = 'normal'
      generate_sonnet2_button['state'] = 'normal'
      bigram_flag = False
      write_to_log(log_message, logger_index)
      current_model.set("Current Model: Custom GRU")
    except(IOError):
      write_to_log("No such file or directory: %s" %filename, logger_index)
      return
def load_gru():
    iterations = number_iterations.get()
    if iterations == 20:
        filename = "GRUModel20.npz"
    elif iterations == 40:
        filename = "GRUModel40.npz"
    elif iterations == 60:
        filename = "GRUModel60.npz"
    elif iterations == 80:
        filename = "GRUModel80.npz"
    else:
        filename = "GRUModel100.npz"
    model = GRU(dictionary_size)
    log_message = model.load_model_parameters("TrainedModels/" + filename)
    generate_sonnet1_button['state'] = 'normal'
    generate_sonnet2_button['state'] = 'normal'
    generate_sonnet1_image_button['state'] = 'normal'
    generate_sonnet2_image_button['state'] = 'normal'
    bigram_flag = False
    write_to_log(log_message + " iterations=%d" % iterations, logger_index)
    current_model.set("Current Model: GRU %d iterations" % iterations)
def load_gru():
  iterations = number_iterations.get()
  if iterations == 20:
    filename = "GRUModel20.npz"
  elif iterations == 40:
    filename = "GRUModel40.npz"
  elif iterations == 60:
    filename = "GRUModel60.npz"
  elif iterations == 80:
    filename = "GRUModel80.npz"
  else:
    filename = "GRUModel100.npz"
  model = GRU(dictionary_size)
  log_message = model.load_model_parameters("TrainedModels/" + filename)
  generate_sonnet1_button['state'] = 'normal'
  generate_sonnet2_button['state'] = 'normal'
  generate_sonnet1_image_button['state'] = 'normal'
  generate_sonnet2_image_button['state'] = 'normal'
  bigram_flag = False
  write_to_log(log_message + " iterations=%d" %iterations, logger_index)
  current_model.set("Current Model: GRU %d iterations" %iterations)
Exemplo n.º 5
0
    for (num_examples, loss) in losses:
        num_examples_array.append(num_examples)
        loss_array.append(loss)
    plt.plot(num_examples_array, loss_array)
    plt.title('Losses of Trained RNN Model %d iterations' %
              number_of_iterations)
    plt.xlabel('number of examples seen')
    plt.ylabel('losses')
    plt.grid(True)
    plt.show()
    model.save_model_parameters(save_to_file)
    print("Done training model")
# GRU
elif args.mode == "GRU":
    print("Generating GRU Model...")
    model = GRU(dictionary_size)
    print("Training GRU Model with %d iterations..." % number_of_iterations)
    losses = model.train_with_sgd(start_train,
                                  end_train,
                                  nepoch=number_of_iterations)
    for (num_examples, loss) in losses:
        num_examples_array.append(num_examples)
        loss_array.append(loss)
    plt.plot(num_examples_array, loss_array)
    plt.title('Losses of Trained GRU Model %d iterations' %
              number_of_iterations)
    plt.xlabel('number of examples seen')
    plt.ylabel('losses')
    plt.grid(True)
    plt.show()
    model.save_model_parameters(save_to_file)