Pownie/advancedskrald
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= 2019-04-11 09:41:37 +02:00
parent deb083224f
commit 2c94c9a2d5
3 changed files with 16 additions and 17 deletions
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chess_model_3_pieces.h5 Normal file
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tensor_classifier.py
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@ -1,4 +1,4 @@
from __future__ import absolute_import, division, print_function, unicode_literals
#from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow.python.keras import datasets, layers, models
@ -10,7 +10,7 @@ import runner
from main import find_occupied_squares
from util import POSITION
new_model = models.load_model('test_chess_model.h5')
new_model = models.load_model('chess_model_3_pieces.h5')
new_model.summary()
#board = cv2.imread("whole_boards/boards_for_empty/board_1554286488.605142_rank_3.png")
@ -44,7 +44,7 @@ square_3 = square_3 / 255.0
square_4 = square_4 / 255.0
pieces = ['knight', 'rook']
pieces = ['knight', 'rook', 'bishop']
for pos, square in occupied:
square = cv2.cvtColor(square, cv2.COLOR_BGR2GRAY)
@ -54,7 +54,7 @@ for pos, square in occupied:
text_color = 255
cv2.putText(square, f"{pos} {pieces[int(np.argmax(test))]}", (0, 50), cv2.FONT_HERSHEY_SIMPLEX, fontScale=1,
color=(text_color,) * 3, thickness=3)
cv2.imshow(f"{pos}", square)
cv2.imwrite("lel", square)
cv2.waitKey(0)

25
tmp/tensor.py
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@ -1,7 +1,6 @@
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
from tensorflow.python.keras import datasets, layers, models
import glob
import numpy as np
import cv2
@ -58,18 +57,18 @@ test_img = np.array(test_img).reshape((len(test_img),width, height, 1))
# Normalize pixel values to be between 0 and 1
train_images, test_images = training_img / 255.0, test_img / 255.0
model = models.Sequential()
model.add(layers.Conv2D(256, (3, 3), activation='relu', input_shape=(width, height, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Conv2D(256, (3, 3), activation='relu', input_shape=(width, height, 1)))
model.add(tf.keras.layers.MaxPooling2D((2, 2)))
model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))
model.add(tf.keras.layers.MaxPooling2D((2, 2)))
model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(tf.keras.layers.MaxPooling2D((2, 2)))
model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(3, activation='softmax'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(3, activation='softmax'))
model.summary()
@ -77,7 +76,7 @@ model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, training_labels, epochs=10)
model.fit(train_images, training_labels, epochs=3)
test_loss, test_acc = model.evaluate(test_images, test_labels_)