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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)
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