In [1]:

    

# set up Python environment: numpy for numerical routines, and matplotlib for plotting
import numpy as np
import cv2
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/mckc/caffe/'  # this file should be run from {caffe_root}/examples (otherwise change this line)
sys.path.insert(0, caffe_root + 'python')

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


    

In [2]:

    

caffe.set_mode_cpu()

model_def =  '/home/mckc/Downloads/vgg_face_caffe/VGG_FACE_deploy.prototxt'
model_weights = '/home/mckc/Downloads/vgg_face_caffe/VGG_FACE.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)


    

In [4]:

    

im = cv2.resize(cv2.imread('/home/mckc/Downloads/vgg_face_caffe/ak.png'), (224, 224)).astype(np.float32)
plt.imshow(im.astype(np.uint8))
im[:,:,0] -= 103.939
im[:,:,1] -= 116.779
im[:,:,2] -= 123.68
im = im.transpose((2,0,1))
im = np.expand_dims(im, axis=0)

# copy the image data into the memory allocated for the net
net.blobs['data'].data[...] = im

### perform classification
output = net.forward()
labels = np.loadtxt('/home/mckc/Downloads/vgg_face_caffe/names.txt', str, delimiter='\t')
output_prob = output['prob'][0]  # the output probability vector for the first image in the batch

print 'predicted class is:', output_prob.argmax(),labels[output_prob.argmax()]


    

    




predicted class is: 2 Aamir_Khan






    

In [3]:

    

model_def =  '/home/mckc/Downloads/faces/faces_train.prototxt'
model_weights = '/home/mckc/Downloads/vgg_face_caffe/VGG_FACE.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)


    

In [5]:

    

np.load('/home/mckc/map.npy')


    

    
Out[5]:





array(['Abhay', 'Arun', 'Gopika', 'Kinkar', 'Omar', 'Pandian', 'Sharath'], 
      dtype='|S32')

In [4]:

    

caffe.set_mode_gpu()

### load the solver and create train and test nets
solver = None  # ignore this workaround for lmdb data (can't instantiate two solvers on the same data)
solver = caffe.SGDSolver('/home/mckc/Downloads/faces/vgg_solver.prototxt')


    

In [5]:

    

solver.net.forward()  # train net
solver.test_nets[0].forward()  # test net (there can be more than one)


    

    
Out[5]:





{'accuracy': array(0.0, dtype=float32),
 'loss': array(1.945910096168518, dtype=float32)}

In [6]:

    

solver.step(1)


    

In [7]:

    

solver.test_nets[0].forward()


    

    
Out[7]:





{'accuracy': array(1.0, dtype=float32),
 'loss': array(1.9450531005859375, dtype=float32)}

In [40]:

    

imshow(solver.test_nets[0].blobs['data'].data[0].transpose((2,0,0)))


    

    




---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-40-68727c64a1db> in <module>()
----> 1 imshow(solver.test_nets[0].blobs['data'].data[0].transpose((2,0,1)))

/home/mckc/anaconda/lib/python2.7/site-packages/matplotlib/pyplot.pyc in imshow(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, hold, data, **kwargs)
   3020                         filternorm=filternorm, filterrad=filterrad,
   3021                         imlim=imlim, resample=resample, url=url, data=data,
-> 3022                         **kwargs)
   3023     finally:
   3024         ax.hold(washold)

/home/mckc/anaconda/lib/python2.7/site-packages/matplotlib/__init__.pyc in inner(ax, *args, **kwargs)
   1810                     warnings.warn(msg % (label_namer, func.__name__),
   1811                                   RuntimeWarning, stacklevel=2)
-> 1812             return func(ax, *args, **kwargs)
   1813         pre_doc = inner.__doc__
   1814         if pre_doc is None:

/home/mckc/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc in imshow(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, shape, filternorm, filterrad, imlim, resample, url, **kwargs)
   4945                               resample=resample, **kwargs)
   4946 
-> 4947         im.set_data(X)
   4948         im.set_alpha(alpha)
   4949         if im.get_clip_path() is None:

/home/mckc/anaconda/lib/python2.7/site-packages/matplotlib/image.pyc in set_data(self, A)
    451         if (self._A.ndim not in (2, 3) or
    452                 (self._A.ndim == 3 and self._A.shape[-1] not in (3, 4))):
--> 453             raise TypeError("Invalid dimensions for image data")
    454 
    455         self._imcache = None

TypeError: Invalid dimensions for image data





    

In [11]:

    

niter = 20000
for i in range(niter):
    solver.step(1)
    print solver.test_nets[0].forward()


    

    




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---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-11-38c756d88341> in <module>()
      1 niter = 20000
      2 for i in range(niter):
----> 3     solver.step(1)
      4     print solver.test_nets[0].forward()

KeyboardInterrupt: 

In [ ]: