neuralmate_full.py

import tensorflow as tf
from skimage.io import imread, imsave
import neuralmate
import numpy as np

image = imread('boards/board3.png')
image = neuralmate.preprocess_image(image)

model = tf.keras.models.load_model('model/')
model(image)
orfen = "r4rk1/ppp2ppp/8/8/8/1P6/PQ3PPP/R4RK1"
fens = []
PIECE_MAX = 11
PIECE_MIN = 0
# choose cell
# image = np.expand_dims(image[27], 0)
print(image.shape)
image_tensor = tf.convert_to_tensor(image.astype('float64'))
image_tensor = tf.cast(image_tensor, dtype=tf.float64)

cnt = 0
while neuralmate.difference(image, image_tensor.numpy()) < 0.06:
    with tf.GradientTape(persistent=True) as tape:
        tape.watch(image_tensor)
        new_labels = model(image_tensor)
        diff = neuralmate.difference(image, image_tensor.numpy())
        print(cnt, 'diff', diff)
        labels_min = tf.slice(new_labels, [0, PIECE_MIN], [-1, 1])
        labels_max = tf.slice(new_labels, [0, PIECE_MAX], [-1, 1])

        grads_min = tape.gradient(labels_min, image_tensor)
        grads_max = tape.gradient(labels_max, image_tensor)

        mult = 5

        # image_tensor = image_tensor + 0.0001 * np.sign(grads_max)
        image_tensor = image_tensor + 0.001 * np.sign(grads_max)
        # image_tensor = image_tensor - 0.0001 * np.sign(grads_min)

        image_tensor = tf.clip_by_value(image_tensor, image-0.058, image+0.058)
        image_tensor = tf.clip_by_value(image_tensor, 0, 1)
    # print('certainty min', labels_min.numpy().mean(), labels_min.numpy().max())
    print('certainty max', labels_max.numpy().mean(), labels_max.numpy().max())
    # print(new_labels.numpy()[:, 3])
    # piece = new_labels.numpy().argmax()
    # print(piece)
    new_labels = new_labels.numpy().argmax(axis=1).reshape(8, 8)
    new_fen = neuralmate.labels2fen(new_labels)
    print('fen', new_fen)
    print(' ')

    cnt += 1
    if cnt % 100 == 0:
        full_image = neuralmate.reconstruct_from_blocks(image_tensor)
        imsave('saved/board3_full_queen.png', full_image)
    if new_fen not in fens:
        fens.append(new_fen)
print(fens)