caffe学习--caffe入门classification00学习--ipython

首先，数据文件和模型文件都已经下载并处理好，不提。

cd "caffe-root-dir "

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# set up Python environment: numpy for numerical routines, and matplotlib for plotting

import numpy as np

import matplotlib.pyplot as plt

# display plots in this notebook

%matplotlib inline

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# set display defaults

plt.rcParams[\'figure.figsize\'] = (10, 10) # large images

plt.rcParams[\'image.interpolation\'] = \'nearest\' # don\'t interpolate: show square pixels

plt.rcParams[\'image.cmap\'] = \'gray\' # use grayscale output rather than a (potentially misleading) color heatmap

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# The caffe module needs to be on the Python path;

# we\'ll add it here explicitly.

import sys

caffe_root = \'./\' # this file should be run from {caffe_root}/examples (otherwise change this line)

sys.path.insert(0, caffe_root + \'build/install/python\')

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import caffe

# If you get "No module named _caffe", either you have not built pycaffe or you have the wrong path.

caffe.set_mode_cpu()

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model_def = caffe_root + \'models/bvlc_reference_caffenet/deploy.prototxt\'

model_weights = caffe_root + \'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel\'

net = caffe.Net(model_def, # defines the structure of the model

model_weights, # contains the trained weights

caffe.TEST) # use test mode (e.g., don\'t perform dropout)

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# load the mean ImageNet image (as distributed with Caffe) for subtraction

mu = np.load(caffe_root + \'build/install/python/caffe/imagenet/ilsvrc_2012_mean.npy\')

mu = mu.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel values

print \'mean-subtracted values:\', zip(\'BGR\', mu)

# create transformer for the input called \'data\'

transformer = caffe.io.Transformer({\'data\': net.blobs[\'data\'].data.shape})

transformer.set_transpose(\'data\', (2,0,1)) # move image channels to outermost dimension

transformer.set_mean(\'data\', mu) # subtract the dataset-mean value in each channel

transformer.set_raw_scale(\'data\', 255) # rescale from [0, 1] to [0, 255]

transformer.set_channel_swap(\'data\', (2,1,0)) # swap channels from RGB to BGR

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# set the size of the input (we can skip this if we\'re happy

# with the default; we can also change it later, e.g., for different batch sizes)

net.blobs[\'data\'].reshape(50, # batch size

3, # 3-channel (BGR) images

227, 227) # image size is 227x227

image = caffe.io.load_image(caffe_root + \'examples/images/cat.jpg\')

transformed_image = transformer.preprocess(\'data\', image)

plt.imshow(image)

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# copy the image data into the memory allocated for the net

net.blobs[\'data\'].data[...] = transformed_image

### perform classification

output = net.forward()

output_prob = output[\'prob\'][0] # the output probability vector for the first image in the batch

print \'predicted class is:\', output_prob.argmax()

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# load ImageNet labels

labels_file = caffe_root + \'data/ilsvrc12/synset_words.txt\'

if not os.path.exists(labels_file):

!../data/ilsvrc12/get_ilsvrc_aux.sh

labels = np.loadtxt(labels_file, str, delimiter=\'\t\')

print \'output label:\', labels[output_prob.argmax()]

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# sort top five predictions from softmax output

top_inds = output_prob.argsort()[::-1][:5] # reverse sort and take five largest items

print \'probabilities and labels:\'

zip(output_prob[top_inds], labels[top_inds])

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%timeit net.forward()

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caffe.set_device(0) # if we have multiple GPUs, pick the first one

caffe.set_mode_gpu()

net.forward() # run once before timing to set up memory

%timeit net.forward()

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# for each layer, show the output shape

for layer_name, blob in net.blobs.iteritems():

print layer_name + \'\t\' + str(blob.data.shape)

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for layer_name, param in net.params.iteritems():

print layer_name + \'\t\' + str(param[0].data.shape), str(param[1].data.shape)

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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\')

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# the parameters are a list of [weights, biases]

filters = net.params[\'conv1\'][0].data

vis_square(filters.transpose(0, 2, 3, 1))

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feat = net.blobs[\'conv1\'].data[0, :36]

vis_square(feat)

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feat = net.blobs[\'pool5\'].data[0]

vis_square(feat)

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feat = net.blobs[\'fc6\'].data[0]

plt.subplot(2, 1, 1)

plt.plot(feat.flat)

plt.subplot(2, 1, 2)

_ = plt.hist(feat.flat[feat.flat > 0], bins=100)

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feat = net.blobs[\'prob\'].data[0]

plt.figure(figsize=(15, 3))

plt.plot(feat.flat)

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# download an image

my_image_url = "..." # paste your URL here

# for example:

# my_image_url = "https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG"

!wget -O image.jpg $my_image_url

# transform it and copy it into the net

image = caffe.io.load_image(\'image.jpg\')

net.blobs[\'data\'].data[...] = transformer.preprocess(\'data\', image)

# perform classification

net.forward()

# obtain the output probabilities

output_prob = net.blobs[\'prob\'].data[0]

# sort top five predictions from softmax output

top_inds = output_prob.argsort()[::-1][:5]

plt.imshow(image)

print \'probabilities and labels:\'

zip(output_prob[top_inds], labels[top_inds])

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