Which of the following statements is false?
A) Convnets often have many convolution and pooling layers.
B) The Keras team's convnets tend to double the number of filters in subsequent convolutional layers to enable the model to learn more relationships between the features.
C) The following snippets add a convolution layer with 128 filters, followed by a pooling layer to reduce the dimensionality by 50%:
Cnn)add(Conv2D(filters=128, kernel_size=(3, 3),
Activation='relu'))
Cnn)add(MaxPooling2D(pool_size=(2, 2)))
D) For odd dimensions like 11-by-11, Keras pooling layers round the dimensions down by default.

Question

Quizplus · Accepted Answer

DC) The provided code snippet correctly adds a convolution layer with 128 filters and a pooling layer, but it does not specify reducing dimensionality by 50%. The MaxPooling2D layer with pool_size=(2, 2) indeed reduces the spatial dimensions (height and width) of the input feature maps by a factor of 2, which can be interpreted as reducing the total area by 50%, but the statement's phrasing might be misleading as it does not explicitly mention spatial dimensions.D) Keras pooling layers do not round dimensions down by default for odd dimensions; instead, they apply padding if necessary to ensure that the pooling operation can be applied evenly across the input. The behavior for handling odd dimensions depends on the padding argument of the pooling layer, which can be set to 'valid' (no padding, possibly discarding some rows/columns) or 'same' (padding added as necessary to process all input units).

Which of the Following Statements Is False