neural network - get_all_param_values() how to read lasagne.layer -


i running lasagne , theano create convolutional neural network. consist of 

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)

my last layer denselayer uses softmax output classification. ultimate goal retrieve probability , not classification (0 or 1).

when call get_all_param_values(), provides me extensive array. want weights , bias last dense layer. how go this? have tried l_out.w , l_out.b , get_values().

thanks in advance!

you can parameters single layer using get_params. explained in documentation.


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