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import os
import tensorflow as tf
import numpy as np
from tsmlstarterbot.common import PLANET_MAX_NUM, PER_PLANET_FEATURES
# We don't want tensorflow to produce any warnings in the standard output, since the bot communicates
# with the game engine through stdout/stdin.
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '99'
# Normalize planet features within each frame.
def normalize_input(input_data):
# Assert the shape is what we expect
shape = input_data.shape
assert len(shape) == 3 and shape[1] == PLANET_MAX_NUM and shape[2] == PER_PLANET_FEATURES
m = np.expand_dims(input_data.mean(axis=1), axis=1)
s = np.expand_dims(input_data.std(axis=1), axis=1)
return (input_data - m) / (s + 1e-6)
class NeuralNet(object):
def __init__(self, cached_model=None, seed=None):
self._graph = tf.Graph()
with self._graph.as_default():
if seed is not None:
self._session = tf.Session()
self._features = tf.placeholder(dtype=tf.float32, name="input_features",
# target_distribution describes what the bot did in a real game.
# For instance, if it sent 20% of the ships to the first planet and 15% of the ships to the second planet,
# then expected_distribution = [0.2, 0.15 ...]
self._target_distribution = tf.placeholder(dtype=tf.float32, name="target_distribution",
shape=(None, PLANET_MAX_NUM))
# Combine all the planets from all the frames together, so it's easier to share
# the weights and biases between them in the network.
flattened_frames = tf.reshape(self._features, [-1, PER_PLANET_FEATURES])
first_layer = tf.contrib.layers.fully_connected(flattened_frames, self.FIRST_LAYER_SIZE)
second_layer = tf.contrib.layers.fully_connected(first_layer, self.SECOND_LAYER_SIZE)
third_layer = tf.contrib.layers.fully_connected(second_layer, 1, activation_fn=None)
# Group the planets back in frames.
logits = tf.reshape(third_layer, [-1, PLANET_MAX_NUM])
self._prediction_normalized = tf.nn.softmax(logits)
self._loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=self._target_distribution))
self._optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(self._loss)
self._saver = tf.train.Saver()
if cached_model is None:
self._saver.restore(self._session, cached_model)
def fit(self, input_data, expected_output_data):
Perform one step of training on the training data.
:param input_data: numpy array of shape (number of frames, PLANET_MAX_NUM, PER_PLANET_FEATURES)
:param expected_output_data: numpy array of shape (number of frames, PLANET_MAX_NUM)
:return: training loss on the input data
loss, _ =[self._loss, self._optimizer],
feed_dict={self._features: normalize_input(input_data),
self._target_distribution: expected_output_data})
return loss
def predict(self, input_data):
Given data from 1 frame, predict where the ships should be sent.
:param input_data: numpy array of shape (PLANET_MAX_NUM, PER_PLANET_FEATURES)
:return: 1-D numpy array of length (PLANET_MAX_NUM) describing percentage of ships
that should be sent to each planet
feed_dict={self._features: normalize_input(np.array([input_data]))})[0]
def compute_loss(self, input_data, expected_output_data):
Compute loss on the input data without running any training.
:param input_data: numpy array of shape (number of frames, PLANET_MAX_NUM, PER_PLANET_FEATURES)
:param expected_output_data: numpy array of shape (number of frames, PLANET_MAX_NUM)
:return: training loss on the input data
feed_dict={self._features: normalize_input(input_data),
self._target_distribution: expected_output_data})
def save(self, path):
Serializes this neural net to given path.
:param path:
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