如何计算培训Keras模型所需的RAM内存?我想计算这个,因为在训练模型时有时会遇到超过系统内存的错误。这是我的模型,例如:
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 30, 30, 32) 320
_________________________________________________________________
conv2d_2 (Conv2D) (None, 28, 28, 32) 9248
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 14, 14, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 12, 12, 64) 18496
_________________________________________________________________
conv2d_4 (Conv2D) (None, 10, 10, 64) 36928
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 5, 5, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 1600) 0
_________________________________________________________________
dense_1 (Dense) (None, 128) 204928
_________________________________________________________________
dropout_3 (Dropout) (None, 128) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 1290
=================================================================
Total params: 271,210
Trainable params: 271,210
Non-trainable params: 0
