问题描述
我在Keras中有卷积神经网络.我需要知道每一层中要素地图的尺寸.我的输入是28 x 28像素的图像.我知道有一种方法可以计算这个，但我不确定.以下是我使用Keras的代码段.
I have convolutional neural network in Keras.I need to know the dimensions of the feature maps in each layer.My input is 28 by 28 pixel image. I know theres a way to calculate this I not sure how. Below is my code snippet using Keras.
img_rows, img_cols = 28, 28
nb_filters = 32
nb_pool = 2
nb_conv = 3
model = Sequential()
model.add(Convolution2D(nb_filters, nb_conv, nb_conv, border_mode='valid', input_shape=(1, img_rows, img_cols)))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.25))
model.add(Convolution2D(64, nb_conv, nb_conv, border_mode='valid'))
model.add(Activation('relu'))
model.add(Convolution2D(64, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))
最后，这就是我要画的.谢谢.
At the end of the day, this is what i want to draw. Thank you.
推荐答案
检查此文章.
输出体积的空间大小的公式: K *((WF + 2P)/S + 1)，其中 W -输入体积大小， F 的Conv层神经元的感受野大小， S -对其应用的步幅， P -在其上使用的零填充量边界 K -转换层的深度.
Formula for spatial size of the output volume: K*((W−F+2P)/S+1), where W - input volume size, F the receptive field size of the Conv Layer neurons, S - the stride with which they are applied, P - the amount of zero padding used on the border, K - the depth of conv layer.
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