pip install cython

pip install h5py

pip install ipython

pip install leveldb

pip install matplotlib

pip install networkx

pip install nose

pip install numpy

pip install pandas

pip install protobuf

pip install python-gflags

pip install scikit-image

pip install scikit-learn

pip install scipy

# 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

# 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

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

# we'll add it here explicitly.

import sys

caffe_root = '/home/wangshuo/caffe/' # this file should be run from {caffe_root}/examples (otherwise change this line)

sys.path.insert(0, caffe_root + '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.

import os

if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):

print 'CaffeNet found.'

else:

print 'Downloading pre-trained CaffeNet model...'

!../scripts/download_model_binary.py ../models/bvlc_reference_caffenet

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'

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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)

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

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

mu = mu.mean(1).mean(1) # 獲取BGR像素均值

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

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# create transformer for the input called 'data'

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

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

transformer.set_mean('data', mu) # 各個(gè)通道的像素值減去圖像均值

transformer.set_raw_scale('data', 255) # 設(shè)置灰度級(jí)為[0,255]而不是[0,1]

transformer.set_channel_swap('data', (2,1,0)) # 把RGB轉(zhuǎn)為BGR

# 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, # 批次大小

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)

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

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

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### perform classification

output = net.forward()

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

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print 'predicted class is:', output_prob.argmax()#分類輸出最大概率的類別

# 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

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

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

# sort top five predictions from softmax output

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

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print 'probabilities and labels:'

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

%timeit net.forward()

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()

# for each layer, show the output shape

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

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

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

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

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)"""

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# normalize data for display

data = (data - data.min()) / (data.max() - data.min())

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# 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)

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# 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:])

ipt.show()

plt.imshow(data); plt.axis('off')

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

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

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

import pylab as ipt

ipt.show()

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

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

ipt.show()

plt.imshow(image)