I am trying to train a small network for detecting JPEG artefacts, I have 7 classes with each 1k of the same images just with different level of artefacts. My code looks like this:
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('')
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=0.2,
subset="training",
seed=123,
color_mode="grayscale",
shuffle=True,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.utils.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
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",
input_shape=(img_height,
img_width,
1)),
layers.RandomRotation(0.5),
layers.RandomZoom(0.2),
]
)
train_ds = train_ds.shuffle(buffer_size=1000).prefetch(buffer_size=AUTOTUNE) #.cache()
val_ds = val_ds.prefetch(buffer_size=AUTOTUNE) #.cache()
num_classes = len(class_names)
model = Sequential([
layers.Rescaling(1.0/255, input_shape=(img_height, img_width, 1)),
layers.Conv2D(2, (4, 4), strides=(4, 4), padding='valid', dilation_rate=(1, 1), groups=1, input_shape=(1024, 1024, 1), activation='relu'),
layers.Conv2D(4, (4, 4), strides=(4, 4), padding='valid', dilation_rate=(1, 1), groups=1, input_shape=(256, 256, 2), activation='relu'),
layers.Conv2D(8, (4, 4), strides=(4, 4), padding='valid', dilation_rate=(1, 1), groups=1, input_shape=(64, 64, 4), activation='relu'),
layers.Conv2D(16, (4, 4), strides=(4, 4), padding='valid', dilation_rate=(1, 1), groups=1, input_shape=(16, 16, 8), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(16, activation='relu'),
layers.Dense(num_classes, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.summary()
model.save("./model/AAT")
epochs = 100
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"
)
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))
)
I tried playing with layers, optimizers even loss lossfunctions but all the time with the exact same result or error. The log looks like this:
5768/5768 [==============================] - 40s 6ms/step - loss: 1.9472 - accuracy: 0.1954 - val_loss: 1.9467 - val_accuracy: 0.0000e+00
Epoch 2/100
5768/5768 [==============================] - 34s 6ms/step - loss: 1.9468 - accuracy: 0.0381 - val_loss: 1.9473 - val_accuracy: 0.0000e+00
Epoch 3/100
5768/5768 [==============================] - 35s 6ms/step - loss: 1.9468 - accuracy: 0.3358 - val_loss: 1.9469 - val_accuracy: 0.0000e+00
Epoch 4/100
5768/5768 [==============================] - 32s 5ms/step - loss: 1.9468 - accuracy: 0.1555 - val_loss: 1.9468 - val_accuracy: 0.0000e+00
Epoch 5/100
5768/5768 [==============================] - 32s 5ms/step - loss: 1.9468 - accuracy: 0.1130 - val_loss: 1.9466 - val_accuracy: 0.0000e+00
Epoch 6/100
5768/5768 [==============================] - 34s 6ms/step - loss: 1.9467 - accuracy: 0.2715 - val_loss: 1.9468 - val_accuracy: 0.0000e+00
Epoch 7/100
5768/5768 [==============================] - 31s 5ms/step - loss: 1.9469 - accuracy: 0.2384 - val_loss: 1.9471 - val_accuracy: 0.0000e+00
Epoch 8/100
5768/5768 [==============================] - 32s 5ms/step - loss: 1.9469 - accuracy: 0.0836 - val_loss: 1.9465 - val_accuracy: 0.0000e+00
and after it finishes, here are tests on images that was not included in the dataset:
831180_005.jpg
This image most likely belongs to C060 with a 14.34 percent confidence.
1/1 [==============================] - 0s 17ms/step
831180_010.jpg
This image most likely belongs to C060 with a 14.34 percent confidence.
1/1 [==============================] - 0s 19ms/step
831180_020.jpg
This image most likely belongs to C060 with a 14.34 percent confidence.
1/1 [==============================] - 0s 17ms/step
831180_040.jpg
This image most likely belongs to C060 with a 14.34 percent confidence.
1/1 [==============================] - 0s 21ms/step
831180_060.jpg
This image most likely belongs to C060 with a 14.34 percent confidence.
1/1 [==============================] - 0s 19ms/step
831180_080.jpg
This image most likely belongs to C060 with a 14.34 percent confidence.
1/1 [==============================] - 0s 17ms/step
831180_100.jpg
This image most likely belongs to C060 with a 14.34 percent confidence.
I believe the loss function you are using is inappropriate for your problem. From what I understand, your output is in a one-hot encoding format, not a sparse format. I believe a simple "CrossEntropyLoss" should work fine