Model builder

tmp/tensor.py

@@ -0,0 +1,77 @@
+from __future__ import absolute_import, division, print_function, unicode_literals
+
+import tensorflow as tf
+import tensorflow_datasets as tfds
+from tensorflow.python.keras import datasets, layers, models
+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(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/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 = models.Sequential()
+model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(width, height, 1)))
+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.add(layers.Flatten())
+model.add(layers.Dense(64, activation='relu'))
+model.add(layers.Dense(2, activation='softmax'))
+
+model.summary()
+
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.fit(train_images, training_labels, epochs=5)
+
+test_loss, test_acc = model.evaluate(test_images, test_labels_)
+
+print(test_acc)
+
+# Save entire model to a HDF5 file
+model.save('test_chess_model.h5')
+