advancedskrald/tmp/tensor.py

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf
import glob
import numpy as np
import cv2

#(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()

#print(train_images[0])

#exit()
training_img = []
training_labels = []

test_img = []
test_labels_ = []


for _ in range(10):
    for filename in glob.glob(f"../training_images/rook/*_square/*.png")[:-50]:
        training_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
        training_labels.append(1)


for _ in range(10):
    for filename in glob.glob(f"../training_images/knight/*_square/*.png")[:-50]:
        training_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
        training_labels.append(0)

for _ in range(10):
    for filename in glob.glob(f"../training_images/bishop/*_square/*.png")[:-50]:
        training_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
        training_labels.append(2)

for _ in range(5):
    for filename in glob.glob(f"../training_images/rook/*_square/*.png")[-50:]:
        test_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
        test_labels_.append(1)

for _ in range(5):
    for filename in glob.glob(f"../training_images/bishop/*_square/*.png")[-50:]:
        test_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
        test_labels_.append(2)

for _ in range(5):
    for filename in glob.glob(f"../training_images/knight/*_square/*.png")[-50:]:
        test_img.append(cv2.cvtColor(cv2.imread(filename), cv2.COLOR_BGR2GRAY))
        test_labels_.append(0)


width, height = training_img[0].shape

training_img = np.array(training_img).reshape((len(training_img), width, height, 1))
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 = 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(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(3, activation='softmax'))

model.summary()

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(train_images, training_labels, epochs=3)

test_loss, test_acc = model.evaluate(test_images, test_labels_)

print(test_acc)

# Save entire model to a HDF5 file
model.save('chess_model_3_pieces.h5')