Question

如何使用pycaffe接口#之显示每一层的学习参数

Answer 1

for layer_name, param in net.params.iteritems(): print layer_name + '\t' + str(param[0].data.shape), str(param[1].data.shape)
conv1 (96, 3, 11, 11) (96,) conv2 (256, 48, 5, 5) (256,) conv3 (384, 256, 3, 3) (384,) conv4 (384, 192, 3, 3) (384,) conv5 (256, 192, 3, 3) (256,) fc6 (4096, 9216) (4096,) fc7 (4096, 4096) (4096,) fc8 (1000, 4096) (1000,)

Since we're dealing with four-dimensional data here, we'll define a helper function for visualizing sets of rectangular heatmaps.
In [15]:
def vis_square(data): """Take an array of shape (n, height, width) or (n, height, width, 3) and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)""" # normalize data for display data = (data - data.min()) / (data.max() -data.min()) # force the number of filters to be square n = int(np.ceil(np.sqrt(data.shape[0]))) padding = (((0, n ** 2 -data.shape[0]), (0, 1), (0, 1)) # add some space between filters + ((0, 0),) * (data.ndim - 3)) # don't pad the last dimension (if there is one) data = np.pad(data, padding, mode='constant', constant_values=1) # pad with ones (white) # tile the filters into an image data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1))) data = data.reshape((n* data.shape[1], n * data.shape[3]) + data.shape[4:]) plt.imshow(data); plt.axis('off')

First we'll look at the first layer filters, conv1
In [16]: