CNN görüntü tanıma modelini bu makaleden(model 1) farklı görüntüler kullanarak yeniden oluşturmaya çalışıyorum. Ancak, modelin takılması bana ilk çağda bir ResourceExhaustedError döndürür. Parti boyutu zaten oldukça küçük, bu yüzden sorunun kağıttan kopyaladığım model tanımımla ilgili olduğunu tahmin ediyorum. Modelle neyin değiştirileceğine dair herhangi bir tavsiye takdir edilecektir. Teşekkürler!
#Load dataset
BATCH_SIZE = 32
IMG_SIZE = (244,244)
train_set = tf.keras.preprocessing.image_dataset_from_directory(
main_dir,
shuffle = True,
image_size = IMG_SIZE,
batch_size = BATCH_SIZE)
val_set = tf.keras.preprocessing.image_dataset_from_directory(
main_dir,
shuffle = True,
image_size = IMG_SIZE,
batch_size = BATCH_SIZE)
class_names = train_set.class_names
print(class_names)
#Augment data by flipping image and random rotation
data_augmentation = tf.keras.Sequential([
tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),
tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
])
#Model definition
model = Sequential([
data_augmentation,
tf.keras.layers.experimental.preprocessing.Rescaling(1./255),
Conv2D(filters=64,kernel_size=(4,4), activation='relu'),
Conv2D(filters=32,kernel_size=(3,3), activation='relu'),
AveragePooling2D(pool_size=(4,4)),
Conv2D(filters=32,kernel_size=(3,3), activation='relu'),
Conv2D(filters=32,kernel_size=(3,3), activation='relu'),
Conv2D(filters=32,kernel_size=(3,3), activation='relu'),
AveragePooling2D(pool_size=(2,2)),
Flatten(),
Dense(256, activation='relu'),
Dense(256, activation='relu'),
Dense(128, activation='relu'),
Dense(128, activation='relu'),
Dense(128, activation='tanh'),
Dense(1, activation='softmax')
])
model.compile(optimizer='RMSprop',
loss=keras.losses.CategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.CategoricalAccuracy()])
history = model.fit(train_set,validation_data=val_set, epochs=150)
Modeli taktıktan sonra hata oluştu:
ResourceExhaustedError: OOM when allocating tensor with shape[32,32,239,239] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node gradient_tape/sequential_1/average_pooling2d/AvgPoolGrad (defined at <ipython-input-10-ef749d320491>:1) ]]
nvıdıa-smı
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.91.03 Driver Version: 460.91.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 GeForce 940MX Off | 00000000:01:00.0 Off | N/A |
| N/A 46C P0 N/A / N/A | 1938MiB / 2004MiB | 2% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 959 G /usr/lib/xorg/Xorg 97MiB |
| 0 N/A N/A 1270 G /usr/bin/gnome-shell 25MiB |
| 0 N/A N/A 4635 G /usr/lib/firefox/firefox 212MiB |
| 0 N/A N/A 5843 C /usr/bin/python3 1595MiB |
+-----------------------------------------------------------------------------+