1 files changed, 70 insertions, 0 deletions
diff --git a/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py
new file mode 100644
index 000000000..016fee5ea
--- /dev/null
+++ b/examples/pybullet/gym/pybullet_envs/minitaur/actuatornet/actuatornet_keras.py
@@ -0,0 +1,70 @@
+
+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)