Visualize VGG16 Filters

In [4]:

    

from __future__ import print_function

from scipy.misc import imsave
import numpy as np
import time
from keras.applications import vgg16
from keras import backend as K


    

    




Using Theano backend.

In [5]:

    

# dimensions of the generated pictures for each filter.
img_width = 128
img_height = 128

# the name of the layer we want to visualize
# (see model definition at keras/applications/vgg16.py)
layer_name = 'block5_conv1'


    

In [6]:

    

# util function to convert a tensor into a valid image

def deprocess_image(x):
    # normalize tensor: center on 0., ensure std is 0.1
    x -= x.mean()
    x /= (x.std() + 1e-5)
    x *= 0.1

    # clip to [0, 1]
    x += 0.5
    x = np.clip(x, 0, 1)

    # convert to RGB array
    x *= 255
    if K.image_data_format() == 'channels_first':
        x = x.transpose((1, 2, 0))
    x = np.clip(x, 0, 255).astype('uint8')
    return x


    

In [7]:

    

model = vgg16.VGG16(weights='imagenet', include_top=False)
print('Model loaded.')

model.summary()


    

    




Model loaded.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, None, None, 3)     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, None, None, 64)    1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, None, None, 64)    36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, None, None, 64)    0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, None, None, 128)   73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, None, None, 128)   147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, None, None, 128)   0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, None, None, 256)   295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, None, None, 256)   0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, None, None, 512)   0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, None, None, 512)   0         
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________

In [8]:

    

# this is the placeholder for the input images
input_img = model.input

# get the symbolic outputs of each "key" layer (we gave them unique names).
layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]])

def normalize(x):
    # utility function to normalize a tensor by its L2 norm
    return x / (K.sqrt(K.mean(K.square(x))) + 1e-5)

kept_filters = []
for filter_index in range(0, 200):
    # we only scan through the first 200 filters,
    # but there are actually 512 of them
    print('Processing filter %d' % filter_index)
    start_time = time.time()

    # we build a loss function that maximizes the activation
    # of the nth filter of the layer considered
    layer_output = layer_dict[layer_name].output
    if K.image_data_format() == 'channels_first':
        loss = K.mean(layer_output[:, filter_index, :, :])
    else:
        loss = K.mean(layer_output[:, :, :, filter_index])

    # we compute the gradient of the input picture wrt this loss
    grads = K.gradients(loss, input_img)[0]

    # normalization trick: we normalize the gradient
    grads = normalize(grads)

    # this function returns the loss and grads given the input picture
    iterate = K.function([input_img], [loss, grads])

    # step size for gradient ascent
    step = 1.

    # we start from a gray image with some random noise
    if K.image_data_format() == 'channels_first':
        input_img_data = np.random.random((1, 3, img_width, img_height))
    else:
        input_img_data = np.random.random((1, img_width, img_height, 3))
    input_img_data = (input_img_data - 0.5) * 20 + 128

    # we run gradient ascent for 20 steps
    for i in range(20):
        loss_value, grads_value = iterate([input_img_data])
        input_img_data += grads_value * step

        print('Current loss value:', loss_value)
        if loss_value <= 0.:
            # some filters get stuck to 0, we can skip them
            break

    # decode the resulting input image
    if loss_value > 0:
        img = deprocess_image(input_img_data[0])
        kept_filters.append((img, loss_value))
    end_time = time.time()
    print('Filter %d processed in %ds' % (filter_index, end_time - start_time))

# we will stich the best 64 filters on a 8 x 8 grid.
n = 8

# the filters that have the highest loss are assumed to be better-looking.
# we will only keep the top 64 filters.
kept_filters.sort(key=lambda x: x[1], reverse=True)
kept_filters = kept_filters[:n * n]

# build a black picture with enough space for
# our 8 x 8 filters of size 128 x 128, with a 5px margin in between
margin = 5
width = n * img_width + (n - 1) * margin
height = n * img_height + (n - 1) * margin
stitched_filters = np.zeros((width, height, 3))

# fill the picture with our saved filters
for i in range(n):
    for j in range(n):
        img, loss = kept_filters[i * n + j]
        stitched_filters[(img_width + margin) * i: (img_width + margin) * i + img_width,
                         (img_height + margin) * j: (img_height + margin) * j + img_height, :] = img


    

    




Processing filter 0
Current loss value: 12.368642807006836
Current loss value: 23.671627044677734
Current loss value: 42.381229400634766
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Filter 0 processed in 24s
Processing filter 1
Current loss value: 0.0
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Current loss value: 8.815566062927246
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Current loss value: 11.789143562316895
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Current loss value: 29.073070526123047
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Filter 28 processed in 8s
Processing filter 29
Current loss value: 39.30426788330078
Current loss value: 44.37983322143555
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Filter 29 processed in 8s
Processing filter 30
Current loss value: 27.302888870239258
Current loss value: 60.32315444946289
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Filter 30 processed in 8s
Processing filter 31
Current loss value: 1.9228883981704712
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Filter 31 processed in 8s
Processing filter 32
Current loss value: 11.09113883972168
Current loss value: 40.831966400146484
Current loss value: 88.67571258544922
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Filter 32 processed in 8s
Processing filter 33
Current loss value: 0.0
Filter 33 processed in 1s
Processing filter 34
Current loss value: 4.49452018737793
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Filter 34 processed in 8s
Processing filter 35
Current loss value: 0.0
Filter 35 processed in 1s
Processing filter 36
Current loss value: 9.570368766784668
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Filter 36 processed in 8s
Processing filter 37
Current loss value: 1.4125895500183105
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Filter 37 processed in 8s
Processing filter 38
Current loss value: 3.620694160461426
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Filter 38 processed in 8s
Processing filter 39
Current loss value: 0.12873534858226776
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Filter 39 processed in 8s
Processing filter 40
Current loss value: 0.7768726944923401
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Filter 40 processed in 8s
Processing filter 41
Current loss value: 0.03339635580778122
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Filter 41 processed in 8s
Processing filter 42
Current loss value: 9.199230194091797
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Filter 42 processed in 8s
Processing filter 43
Current loss value: 9.801602363586426
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Filter 43 processed in 8s
Processing filter 44
Current loss value: 6.57075309753418
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Filter 44 processed in 8s
Processing filter 45
Current loss value: 0.021276848390698433
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Filter 45 processed in 8s
Processing filter 46
Current loss value: 1.2699427604675293
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Filter 46 processed in 8s
Processing filter 47
Current loss value: 2.538386821746826
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Filter 47 processed in 8s
Processing filter 48
Current loss value: 1.553058385848999
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Filter 48 processed in 8s
Processing filter 49
Current loss value: 0.8038886189460754
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Filter 49 processed in 8s
Processing filter 50
Current loss value: 2.1621181964874268
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Filter 50 processed in 8s
Processing filter 51
Current loss value: 1.1207586526870728
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Filter 51 processed in 8s
Processing filter 52
Current loss value: 0.0
Filter 52 processed in 1s
Processing filter 53
Current loss value: 69.27392578125
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Filter 53 processed in 8s
Processing filter 54
Current loss value: 0.0
Filter 54 processed in 1s
Processing filter 55
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Filter 55 processed in 8s
Processing filter 56
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Filter 56 processed in 8s
Processing filter 57
Current loss value: 0.0
Filter 57 processed in 1s
Processing filter 58
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Filter 58 processed in 8s
Processing filter 59
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Filter 59 processed in 8s
Processing filter 60
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Filter 60 processed in 8s
Processing filter 61
Current loss value: 5.469378471374512
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Filter 61 processed in 8s
Processing filter 62
Current loss value: 5.891002178192139
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Filter 62 processed in 8s
Processing filter 63
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Filter 63 processed in 1s
Processing filter 64
Current loss value: 0.9733923673629761
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Filter 64 processed in 8s
Processing filter 65
Current loss value: 30.122987747192383
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Filter 65 processed in 8s
Processing filter 66
Current loss value: 10.791913032531738
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Filter 66 processed in 8s
Processing filter 67
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Filter 67 processed in 1s
Processing filter 68
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Filter 68 processed in 1s
Processing filter 69
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Filter 69 processed in 1s
Processing filter 70
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Filter 70 processed in 8s
Processing filter 71
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Filter 71 processed in 1s
Processing filter 72
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Filter 72 processed in 1s
Processing filter 73
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Filter 73 processed in 8s
Processing filter 74
Current loss value: 2.3190009593963623
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Filter 74 processed in 8s
Processing filter 75
Current loss value: 2.0024728775024414
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Filter 75 processed in 8s
Processing filter 76
Current loss value: 13.011283874511719
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Filter 76 processed in 8s
Processing filter 77
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Filter 77 processed in 1s
Processing filter 78
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Filter 78 processed in 8s
Processing filter 79
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Filter 79 processed in 8s
Processing filter 80
Current loss value: 1.2951725721359253
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Filter 80 processed in 8s
Processing filter 81
Current loss value: 1.775284767150879
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Filter 81 processed in 8s
Processing filter 82
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Filter 82 processed in 8s
Processing filter 83
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Filter 83 processed in 1s
Processing filter 84
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Filter 84 processed in 8s
Processing filter 85
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Filter 85 processed in 2s
Processing filter 86
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Filter 86 processed in 8s
Processing filter 87
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Filter 87 processed in 8s
Processing filter 88
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Filter 88 processed in 1s
Processing filter 89
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Filter 89 processed in 8s
Processing filter 90
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Filter 90 processed in 1s
Processing filter 91
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Filter 91 processed in 8s
Processing filter 92
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Filter 92 processed in 8s
Processing filter 93
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Filter 93 processed in 8s
Processing filter 94
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Filter 94 processed in 1s
Processing filter 95
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Filter 95 processed in 8s
Processing filter 96
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Filter 96 processed in 1s
Processing filter 97
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Filter 97 processed in 8s
Processing filter 98
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Filter 98 processed in 2s
Processing filter 99
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Filter 99 processed in 1s
Processing filter 100
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Filter 100 processed in 1s
Processing filter 101
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Filter 101 processed in 8s
Processing filter 102
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Filter 102 processed in 1s
Processing filter 103
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Filter 103 processed in 8s
Processing filter 104
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Filter 104 processed in 1s
Processing filter 105
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Processing filter 106
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Filter 106 processed in 1s
Processing filter 107
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Filter 107 processed in 8s
Processing filter 108
Current loss value: 0.0
Filter 108 processed in 1s
Processing filter 109
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Filter 109 processed in 8s
Processing filter 110
Current loss value: 11.833144187927246
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Filter 110 processed in 8s
Processing filter 111
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Filter 111 processed in 2s
Processing filter 112
Current loss value: 9.163039207458496
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Filter 112 processed in 8s
Processing filter 113
Current loss value: 13.858527183532715
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Filter 113 processed in 8s
Processing filter 114
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Filter 114 processed in 8s
Processing filter 115
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Filter 115 processed in 1s
Processing filter 116
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Filter 116 processed in 8s
Processing filter 117
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Filter 117 processed in 1s
Processing filter 118
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Filter 118 processed in 8s
Processing filter 119
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Filter 119 processed in 8s
Processing filter 120
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Filter 120 processed in 8s
Processing filter 121
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Filter 121 processed in 8s
Processing filter 122
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Filter 122 processed in 8s
Processing filter 123
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Filter 123 processed in 8s
Processing filter 124
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Filter 124 processed in 1s
Processing filter 125
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Filter 125 processed in 8s
Processing filter 126
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Filter 126 processed in 8s
Processing filter 127
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Filter 127 processed in 8s
Processing filter 128
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Filter 128 processed in 9s
Processing filter 129
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Filter 129 processed in 1s
Processing filter 130
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Filter 130 processed in 1s
Processing filter 131
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Filter 131 processed in 8s
Processing filter 132
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Filter 132 processed in 8s
Processing filter 133
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Filter 133 processed in 8s
Processing filter 134
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Filter 134 processed in 1s
Processing filter 135
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Filter 135 processed in 8s
Processing filter 136
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Filter 136 processed in 8s
Processing filter 137
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Filter 137 processed in 8s
Processing filter 138
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Filter 138 processed in 8s
Processing filter 139
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Filter 139 processed in 8s
Processing filter 140
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Filter 140 processed in 8s
Processing filter 141
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Filter 141 processed in 8s
Processing filter 142
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Filter 142 processed in 1s
Processing filter 143
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Processing filter 144
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Processing filter 145
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Filter 145 processed in 8s
Processing filter 146
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Filter 146 processed in 8s
Processing filter 147
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Filter 147 processed in 8s
Processing filter 148
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Filter 148 processed in 8s
Processing filter 149
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Filter 149 processed in 8s
Processing filter 150
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Filter 150 processed in 8s
Processing filter 151
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Filter 151 processed in 8s
Processing filter 152






    




INFO (theano.gof.compilelock): Waiting for existing lock by process '72673' (I am process '73077')
INFO (theano.gof.compilelock): To manually release the lock, delete /Users/jo374v/.theano/compiledir_Darwin-16.6.0-x86_64-i386-64bit-i386-3.6.1-64/lock_dir
INFO (theano.gof.compilelock): Waiting for existing lock by process '72673' (I am process '73077')
INFO (theano.gof.compilelock): To manually release the lock, delete /Users/jo374v/.theano/compiledir_Darwin-16.6.0-x86_64-i386-64bit-i386-3.6.1-64/lock_dir
INFO (theano.gof.compilelock): Waiting for existing lock by process '72673' (I am process '73077')
INFO (theano.gof.compilelock): To manually release the lock, delete /Users/jo374v/.theano/compiledir_Darwin-16.6.0-x86_64-i386-64bit-i386-3.6.1-64/lock_dir






    




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Processing filter 153
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Processing filter 154
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Processing filter 155
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Processing filter 156
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Filter 156 processed in 2s
Processing filter 157
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Filter 157 processed in 11s
Processing filter 158
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Filter 158 processed in 11s
Processing filter 159
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Filter 159 processed in 10s
Processing filter 160
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Filter 160 processed in 11s
Processing filter 161
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Filter 161 processed in 11s
Processing filter 162
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Filter 162 processed in 11s
Processing filter 163
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Filter 163 processed in 10s
Processing filter 164
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Filter 164 processed in 2s
Processing filter 165
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Processing filter 166
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Filter 166 processed in 10s
Processing filter 167
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Filter 167 processed in 10s
Processing filter 168
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Processing filter 169
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Processing filter 170
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Processing filter 171
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Processing filter 172
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Filter 172 processed in 2s
Processing filter 173
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Processing filter 174
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Filter 174 processed in 10s
Processing filter 175
Current loss value: 0.7154371738433838
Current loss value: 20.604103088378906
Current loss value: 62.61783218383789
Current loss value: 112.79974365234375
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Filter 175 processed in 10s
Processing filter 176
Current loss value: 0.5065935850143433
Current loss value: 15.156623840332031
Current loss value: 75.95596313476562
Current loss value: 149.03561401367188
Current loss value: 218.45263671875
Current loss value: 291.33306884765625
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Filter 176 processed in 10s
Processing filter 177
Current loss value: 7.494861125946045
Current loss value: 18.816179275512695
Current loss value: 39.842098236083984
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Current loss value: 111.32730102539062
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Filter 177 processed in 10s
Processing filter 178
Current loss value: 0.0
Filter 178 processed in 2s
Processing filter 179
Current loss value: 0.0
Filter 179 processed in 2s
Processing filter 180
Current loss value: 1.0480828285217285
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Filter 180 processed in 10s
Processing filter 181
Current loss value: 12.617012977600098
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Current loss value: 95.19184875488281
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Filter 181 processed in 10s
Processing filter 182
Current loss value: 19.762115478515625
Current loss value: 71.87535095214844
Current loss value: 179.08372497558594
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Current loss value: 432.0817565917969
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Filter 182 processed in 10s
Processing filter 183
Current loss value: 44.05370330810547
Current loss value: 89.84777069091797
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Filter 183 processed in 10s
Processing filter 184
Current loss value: 0.0
Filter 184 processed in 2s
Processing filter 185
Current loss value: 2.2257261276245117
Current loss value: 20.54450798034668
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Filter 185 processed in 10s
Processing filter 186
Current loss value: 0.8701344728469849
Current loss value: 8.362115859985352
Current loss value: 30.543981552124023
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Filter 186 processed in 10s
Processing filter 187
Current loss value: 12.84656810760498
Current loss value: 69.91967010498047
Current loss value: 194.81430053710938
Current loss value: 316.08685302734375
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Filter 187 processed in 7s
Processing filter 188
Current loss value: 3.573184013366699
Current loss value: 55.607303619384766
Current loss value: 125.89163208007812
Current loss value: 188.14126586914062
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Filter 188 processed in 7s
Processing filter 189
Current loss value: 0.5670349597930908
Current loss value: 3.3536319732666016
Current loss value: 20.231042861938477
Current loss value: 60.65279769897461
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Filter 189 processed in 8s
Processing filter 190
Current loss value: 3.4644651412963867
Current loss value: 16.373262405395508
Current loss value: 63.707698822021484
Current loss value: 119.8336181640625
Current loss value: 171.56735229492188
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Filter 190 processed in 7s
Processing filter 191
Current loss value: 0.0024113149847835302
Current loss value: 4.132843017578125
Current loss value: 24.198501586914062
Current loss value: 56.271060943603516
Current loss value: 91.93131256103516
Current loss value: 131.85943603515625
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Filter 191 processed in 8s
Processing filter 192
Current loss value: 8.463644981384277
Current loss value: 15.083377838134766
Current loss value: 34.039005279541016
Current loss value: 69.4258041381836
Current loss value: 106.07029724121094
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Filter 192 processed in 7s
Processing filter 193
Current loss value: 0.0
Filter 193 processed in 1s
Processing filter 194
Current loss value: 0.0
Filter 194 processed in 1s
Processing filter 195
Current loss value: 0.1755741983652115
Current loss value: 2.241487503051758
Current loss value: 12.660040855407715
Current loss value: 24.62272834777832
Current loss value: 39.61341857910156
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Current loss value: 77.6168212890625
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Current loss value: 386.0159606933594
Current loss value: 434.2890625
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Filter 195 processed in 8s
Processing filter 196
Current loss value: 0.0
Filter 196 processed in 1s
Processing filter 197
Current loss value: 0.0
Filter 197 processed in 1s
Processing filter 198
Current loss value: 0.0
Filter 198 processed in 1s
Processing filter 199
Current loss value: 0.0
Filter 199 processed in 1s

In [9]:

    

# save the result to disk
imsave('img/stitched_filters.png' , stitched_filters)


    

In [ ]: