text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl import app
  from absl import flags
  from keras.callbacks import ModelCheckpoint
  from keras.layers import Dense
  from keras.models import Sequential
  from keras import optimizers
  import numpy
  
  FLAGS = flags.FLAGS
  
  flags.DEFINE_string(
      "input_filename", "data/minitaur_log_latency_0.01.csv", "The name of the input CSV file."
      "Each line in the CSV file will contain the motor position, the "
      "motor speed, action and torques.")
  
  
  def main(unused_argv):
    # fix random seed for reproducibility
    numpy.random.seed(7)
    # load pima indians dataset
    dataset = numpy.loadtxt(FLAGS.input_filename, delimiter=",")
    # split into input (X) and output (Y) variables
    x = dataset[:, 0:3]
    y = dataset[:, 3]
    print("x=", x)
    print("y=", y)
  
    # create model
    model = Sequential()
    model.add(Dense(12, input_dim=3, activation="relu"))
    model.add(Dense(8, activation="sigmoid"))
    model.add(Dense(1, activation="linear"))
  
    # Compile model (use adam or sgd)
    model.compile(loss="mean_squared_error", optimizer="adam", metrics=["mean_squared_error"])
  
    # checkpoint
    filepath = "/tmp/keras/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5"
    checkpoint = ModelCheckpoint(filepath,
                                 monitor="val_loss",
                                 verbose=1,
                                 save_best_only=True,
                                 mode="min")
    callbacks_list = [checkpoint]
  
    # Fit the model
    # model.fit(X, Y, epochs=150, batch_size=10)
    # model.fit(X, Y, epochs=150, batch_size=10, callbacks=callbacks_list)
    model.fit(x,
              y,
              validation_split=0.34,
              epochs=4500,
              batch_size=1024,
              callbacks=callbacks_list,
              verbose=0)
  
    # evaluate the model
    scores = model.evaluate(x, y)
    print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
  
  
  if __name__ == "__main__":
    app.run(main)
  