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import os
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import pathlib
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
data_dir = "C:/Users/jilek/Downloads/AAT+"
data_dir = pathlib.Path(data_dir).with_suffix('')
data_dir_test = "C:/Users/jilek/Downloads/AAT+_TEST"
data_dir_test = pathlib.Path(data_dir_test).with_suffix('')
image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)
batch_size = 1
img_height = 1024
img_width = 1024
train_ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
validation_split=0.0,
#subset="training",
seed=123,
labels='inferred',
label_mode='categorical',
class_names=["C100", "C095", "C090", "C085", "C080", "C070", "C060", "C040", "C020"],
color_mode="grayscale", #grayscale
shuffle=True,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.utils.image_dataset_from_directory(
data_dir_test,
validation_split=0.0,
#subset="validation",
seed=123,
labels='inferred',
label_mode='categorical',
class_names=["C100", "C095", "C090", "C085", "C080", "C070", "C060", "C040", "C020"],
color_mode="grayscale",
image_size=(img_height, img_width),
batch_size=batch_size)
class_names = train_ds.class_names
print(class_names)
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
AUTOTUNE = tf.data.AUTOTUNE
data_augmentation = keras.Sequential(
[
layers.RandomFlip("horizontal_and_vertical",
input_shape=(img_height,
img_width,
1)), #rgb
#layers.RandomRotation(0.5),
#layers.RandomZoom(0.5),
]
)
train_ds = train_ds.shuffle(buffer_size=900).prefetch(buffer_size=AUTOTUNE) #.cache()
val_ds = val_ds.prefetch(buffer_size=AUTOTUNE) #.cache()
num_classes = len(class_names)
print(str(num_classes))
model = Sequential([
layers.Rescaling(1.0/255, input_shape=(img_height, img_width, 1)), #rgb
#layers.Dropout(0.0),
#layers.MaxPooling2D(pool_size=(8, 8)),
layers.Conv2D(4, (4, 4), strides=(2, 2), padding='valid', dilation_rate=(1, 1), groups=1, input_shape=(1024, 1024, 1), activation='relu'),
layers.Conv2D(8, (4, 4), strides=(2, 2), padding='valid', dilation_rate=(1, 1), groups=1, input_shape=(512, 512, 4), activation='relu'),
layers.Conv2D(16, (4, 4), strides=(4, 4), padding='valid', dilation_rate=(1, 1), groups=1, input_shape=(256, 256, 8), activation='relu'),
layers.Conv2D(32, (4, 4), strides=(4, 4), padding='valid', dilation_rate=(1, 1), groups=1, input_shape=(64, 64, 16), activation='relu'),
layers.Conv2D(64, (4, 4), strides=(4, 4), padding='valid', dilation_rate=(1, 1), groups=1, input_shape=(16, 16, 32), activation='relu'),
#layers.Conv2D(128, (4, 4), strides=(1, 1), padding='valid', dilation_rate=(1, 1), groups=1, input_shape=(8, 8, 64), activation='relu'),
#layers.Dropout(0.1),
layers.Flatten(),
layers.Dense(32, activation='relu'),
layers.Dense(num_classes, activation='softmax')
])
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-5),
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=['accuracy'])
model.summary()
model.save("./model/AAT+")
epochs = 130
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(epochs)
plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
test_dir = "C:/Users/jilek/Downloads/AAT_T/"
for file_name in os.listdir(test_dir):
file_path = os.path.join(test_dir, file_name)
img = tf.keras.utils.load_img(
file_path, target_size=(img_height, img_width), color_mode="grayscale" #grayscale
)
img_array = tf.keras.utils.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
print(file_name)
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)
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