Neural network 获取所有参数值（）如何读取lasagne.layer

我正在运行千层面和Theano来创建我的卷积神经网络。我目前由

l_shape = lasagne.layers.ReshapeLayer(l_in, (-1, 3,130, 130))
l_conv1 = lasagne.layers.Conv2DLayer(l_shape, num_filters=32, filter_size=3, pad=1)
l_conv1_1 = lasagne.layers.Conv2DLayer(l_conv1, num_filters=32, filter_size=3, pad=1)
l_pool1 = lasagne.layers.MaxPool2DLayer(l_conv1_1, 2)
l_conv2 = lasagne.layers.Conv2DLayer(l_pool1, num_filters=64, filter_size=3, pad=1)
l_conv2_2 = lasagne.layers.Conv2DLayer(l_conv2, num_filters=64, filter_size=3, pad=1)
l_pool2 = lasagne.layers.MaxPool2DLayer(l_conv2_2, 2)
l_conv3 = lasagne.layers.Conv2DLayer(l_pool2, num_filters=64, filter_size=3, pad=1)
l_conv3_2 = lasagne.layers.Conv2DLayer(l_conv3, num_filters=64, filter_size=3, pad=1)
l_pool3 = lasagne.layers.MaxPool2DLayer(l_conv3_2, 2)
l_conv4 = lasagne.layers.Conv2DLayer(l_pool3, num_filters=64, filter_size=3, pad=1)
l_conv4_2 = lasagne.layers.Conv2DLayer(l_conv4, num_filters=64, filter_size=3, pad=1)
l_pool4 = lasagne.layers.MaxPool2DLayer(l_conv4_2, 2)
l_conv5 = lasagne.layers.Conv2DLayer(l_pool4, num_filters=64, filter_size=3, pad=1)
l_conv5_2 = lasagne.layers.Conv2DLayer(l_conv5, num_filters=64, filter_size=3, pad=1)
l_pool5 = lasagne.layers.MaxPool2DLayer(l_conv5_2, 2)
l_out = lasagne.layers.DenseLayer(l_pool5, num_units=2, nonlinearity=lasagne.nonlinearities.softmax)

我的最后一层是denselayer，它使用softmax输出我的分类。我的最终目标是检索概率，而不是分类（0或1）

当我调用get_all_param_values（）时，它为我提供了一个扩展数组。我只想要最后一个密集层的权重和偏差。你是怎么做的？我已经尝试了l_out.W和l_out.b并获得了值（）

---

提前谢谢

您可以使用

get_params

获取单个层的参数。这在中进行了解释。

我修改了您的代码，因为您粘贴的内容引用了一个l_in，但您的代码中没有包含一个l_in。我定义了以下网络：

l_shape = lasagne.layers.InputLayer(shape = (None, 3, 130, 130))
l_conv1 = lasagne.layers.Conv2DLayer(l_shape, num_filters=32, filter_size=3, pad=1)
l_conv1_1 = lasagne.layers.Conv2DLayer(l_conv1, num_filters=32, filter_size=3, pad=1)
l_pool1 = lasagne.layers.MaxPool2DLayer(l_conv1_1, 2)
l_conv2 = lasagne.layers.Conv2DLayer(l_pool1, num_filters=64, filter_size=3, pad=1)
l_conv2_2 = lasagne.layers.Conv2DLayer(l_conv2, num_filters=64, filter_size=3, pad=1)
l_pool2 = lasagne.layers.MaxPool2DLayer(l_conv2_2, 2)
l_conv3 = lasagne.layers.Conv2DLayer(l_pool2, num_filters=64, filter_size=3, pad=1)
l_conv3_2 = lasagne.layers.Conv2DLayer(l_conv3, num_filters=64, filter_size=3, pad=1)
l_pool3 = lasagne.layers.MaxPool2DLayer(l_conv3_2, 2)
l_conv4 = lasagne.layers.Conv2DLayer(l_pool3, num_filters=64, filter_size=3, pad=1)
l_conv4_2 = lasagne.layers.Conv2DLayer(l_conv4, num_filters=64, filter_size=3, pad=1)
l_pool4 = lasagne.layers.MaxPool2DLayer(l_conv4_2, 2)
l_conv5 = lasagne.layers.Conv2DLayer(l_pool4, num_filters=64, filter_size=3, pad=1)
l_conv5_2 = lasagne.layers.Conv2DLayer(l_conv5, num_filters=64, filter_size=3, pad=1)
l_pool5 = lasagne.layers.MaxPool2DLayer(l_conv5_2, 2)
l_out = lasagne.layers.DenseLayer(l_pool5, num_units=2, nonlinearity=lasagne.nonlinearities.softmax)

为了实现Daniel Renshaw的答案：

params = l_out.get_params()
W = params[0].get_value()

打印参数时，您将看到l_out的所有参数：

[W, b] 

因此，params、params[0]和params[1]的每个元素都是一个Theano共享变量，您可以通过params[i]获取数值。get_value（）