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batch_size = 1


    

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%matplotlib inline
import matplotlib.pylab as plt


    

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import vgg19_model
reload(vgg19_model)
VGG19 = vgg19_model.VGG19
import numpy as np
import theano
layer_weights = vgg19_model.layer_weights
bias_weights = vgg19_model.bias_weights


VGG19_conv2d_layer = vgg19_model.VGG19_conv2d_layer
batch_size = 1
input_image = np.ones(((batch_size,3, 224, 224)))
image = theano.tensor.tensor4('image')


    

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import cv2
img = cv2.resize(cv2.imread('../bald_eagle.jpg'), (224, 224)).astype(np.float32)
# im[:,:,0] -= 103.939
# im[:,:,1] -= 116.779
# im[:,:,2] -= 123.68

mean_pixel = [103.939, 116.779, 123.68]
img = img.astype(np.float32, copy=False)
for c in range(3):
    img[:, :, c] = img[:, :, c] - mean_pixel[c]
img = img.transpose((2,0,1))
img = np.expand_dims(img, axis=0)

im = img


    

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im.shape


    

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im2 = im.reshape(1,224,224,3)
print im2.shape

new_input_image = np.transpose(im2,(0,3,1,2))


    

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vgg19 = VGG19(input_image_shape=(1,224,224,3))
try_out = theano.function(inputs=[vgg19.input],outputs=vgg19.dense_12.output)


    

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softmax_output = try_out(im)


    

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print softmax_output.shape
print softmax_output.sum()


    

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softmax_output[0].sum()


    

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print np.argmax(softmax_output[0])

print labels[22]


    

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import pickle
import pandas as pd
file = open('../labels/imagenet.bet.pickle','rb')
labels = pickle.load(file)
df_labels = pd.DataFrame(zip(labels[labels.keys()[1]],labels[labels.keys()[3]]))
df_labels.columns = ['Text Label','Numeric Label']


    

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labels.keys()


    

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# def print_top_5(out):
top_five = np.argsort(softmax_output[0])

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

for i,label in enumerate(labels[top_five]):
    print str(i), ' output label: ', label.strip().split(' ')[1]


    

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labels


    

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softmax_output[0]


    

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labels[top_five]


    

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labels


    

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for i in vgg19.__dict__.keys():
    print i,vgg19.__dict__[i].get_values()


    

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vgg19.convolution2d_50_pool.o


    

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