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[英]How to calculate dimensions of the dense and output layer in convolutional neural network?
[英]Visualize output of each layer in theano Convolutional MLP
我正在阅读卷积神经网络教程 。 我希望在训练模型后可视化每层的输出。 例如,在函数“evaluate_lenet5”中,我想将一个实例(它是一个图像)传递给网络,并查看每个图层的输出以及为输入设置训练神经网络的类。 我认为在每个图层的图像和权重向量上做点积可能很容易,但它根本不起作用。
我有每个图层的对象:
# Reshape matrix of rasterized images of shape (batch_size, 28 * 28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (28, 28) is the size of MNIST images.
layer0_input = x.reshape((batch_size, 1, 28, 28))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
# maxpooling reduces this further to (24/2, 24/2) = (12, 12)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2)
)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
# maxpooling reduces this further to (8/2, 8/2) = (4, 4)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2)
)
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
# or (500, 50 * 4 * 4) = (500, 800) with the default values.
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(
rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh
)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
因此,您可以建议一种方法来在训练神经网络后逐步可视化处理图像的样本吗?
这不是那么难。 如果你从theano深度学习教程中使用相同的LeNetConvPoolLayer类定义,那么你只需要编译一个函数,其中x
作为输入, [LayerObject].output
作为输出(其中LayerObject可以是任何层对象,如layer0 , layer1等你想要想象的那个层。
vis_layer1 = function([x],[layer1.output])
传递一个(或许多)样本(确切地说,在训练时你如何输入输入张量),你将获得为你的函数编译的特定层的输出。
注意:这样你会得到完全相同的形状模型的计算采用了输出。 然而,你可以重塑它,只要你想通过整形等的输出变量layer1.output.flatten(n)
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