मैं कोशिश कर रहा हूँ विश्राम करने के लिए सीएनएन छवि मान्यता मॉडल से यह कागज(1 मॉडल) का उपयोग कर विभिन्न छवियों. हालांकि, मॉडल फिटिंग रिटर्न मुझे एक ResourceExhaustedError पर पहली epoch. बैच का आकार पहले से ही काफी छोटे तो मैं अनुमान लगा रहा हूँ के साथ समस्या है मेरे मॉडल परिभाषा जो मैं कॉपी कागज से. किसी भी सलाह के लिए पर परिवर्तन के साथ मॉडल की सराहना की जाएगी । धन्यवाद!
#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)
त्रुटि के बाद फिटिंग मॉडल:
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) ]]
nvidia-smi
+-----------------------------------------------------------------------------+
| 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 |
+-----------------------------------------------------------------------------+