def main(unused_argv):
global labels
print('Saving results to %s' % FLAGS.output_dir)
labels, _ = input.load_labels()
engines = init_engines(3)
#_process_dataset('validation', FLAGS.validation_dir, engines)
_process_dataset('train', FLAGS.train_dir, engines)
import tensorflow as tf
import numpy as np
import chess
import model
import input
FLAGS = tf.app.flags.FLAGS
label_strings, _ = input.load_labels()
with tf.device('/cpu:0'):
board = tf.placeholder(tf.float32, shape=[1, 8, 8, 6])
turn = tf.placeholder(tf.float32, shape=[1])
#player = tf.placeholder(tf.float32, shape=[1])
label = tf.placeholder(tf.int64, shape=[1])
example = [board, turn]
features, _ = model.feature_extractor([board])
logits, _ = model.model(example, features)
onehot_labels = tf.one_hot(label, model.NUM_LABELS, dtype=tf.float32)
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits,
labels=onehot_labels))
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
checkpoint = tf.train.get_checkpoint_state(FLAGS.logdir)
if op.name.find("Relu") != -1:
tensor_to_vis = tf.get_default_graph().get_tensor_by_name(op.name + ":0")
if len(tensor_to_vis.shape) == 4:
print(op.name)
units = sess.run(tensor_to_vis, feed_dict=feed_dict)
filters = units.shape[3]
plt.figure(1, figsize=(40,40))
n_columns = 16
n_rows = math.ceil(filters / n_columns) + 1
for i in range(filters):
plt.subplot(n_rows, n_columns, i+1)
plt.imshow(units[0,:,:,i], interpolation="nearest", cmap="gray")
plt.savefig(op.name + ".png")
plt.clf()
label_strings, switch_indexer = input.load_labels()
def main(unused_argv):
pass
#preds, result_pred = predict("r4r1R/pb2bkp1/4p3/3p1p1q/1ppPnB2/2P1P3/PPQ2PP1/2K4R w - - 0 22")
#preds, result_pred = predict("r4r1R/pb2bkp1/4p3/3p1p1R/1ppPnB2/2P1P3/PPQ2PP1/2K5 b - - 0 22")
#result_pred = predict_result(chess.Board("r1bqkbB1/2ppn1p1/ppP2p1p/4p3/4P3/2N2N2/PPP2PPP/R1BQK2R b KQq - 0 9"))
#print("Result:", result_pred)
#visualize_layer(chess.Board("r1bqkbB1/2ppn1p1/ppP2p1p/4p3/4P3/2N2N2/PPP2PPP/R1BQK2R b KQq - 0 9"))
preds = predict_move("rnbqkbnr/ppppp1pp/5p2/8/4P3/8/PPPP1PPP/RNBQKBNR w KQkq - 0 2")
print(preds)
# argmax = np.argmax(preds, 0)
# print("Best: " + label_strings[argmax] + " " + str(preds[argmax]))
# candidates = np.argpartition(preds, -20)[-20:]
# candidates = candidates[np.argsort(-preds[candidates])]