CNN Intro



In [1]:

    
import warnings
warnings.filterwarnings('ignore')



In [2]:

    
%matplotlib inline
%pylab inline









    



Populating the interactive namespace from numpy and matplotlib



In [3]:

    
import matplotlib.pylab as plt
import numpy as np



In [4]:

    
from distutils.version import StrictVersion



In [5]:

    
import sklearn
print(sklearn.__version__)

assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')



In [6]:

    
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)

assert StrictVersion(tf.__version__) >= StrictVersion('1.1.0')



In [7]:

    
import keras
print(keras.__version__)

assert StrictVersion(keras.__version__) >= StrictVersion('2.0.0')









    



Using TensorFlow backend.






    



2.0.8

Modell-Architektur

http://cs231n.github.io/neural-networks-1/#power

Layout of a typical CNN

http://cs231n.github.io/convolutional-networks/

Classic VGG like Architecture

we use a VGG like architecture
based on https://arxiv.org/abs/1409.1556
basic idea: sequential, deep, small convolutional filters, use dropouts to reduce overfitting
16/19 layers are typical
we choose less layers, because we have limited resources

Convolutional Blocks: Cascading many Convolutional Layers having down sampling in between

http://cs231n.github.io/convolutional-networks/#conv

Example of a Convolution

Original Image

Many convolutional filters applied over all channels

http://cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html

Downlsampling Layer: Reduces data sizes and risk of overfitting

http://cs231n.github.io/convolutional-networks/#pool

Activation Functions



In [54]:

    
def centerAxis(uses_negative=False):
    # http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot
    ax = plt.gca()
    ax.spines['left'].set_position('center')
    if uses_negative:
        ax.spines['bottom'].set_position('center')
    ax.spines['right'].set_color('none')
    ax.spines['top'].set_color('none')
    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')

Sigmoid

This is the classic
Continuous version of step function



In [55]:

    
def np_sigmoid(X):
    return 1 / (1 + np.exp(X * -1))



In [57]:

    
x = np.arange(-10,10,0.01)
y = np_sigmoid(x)

centerAxis()
plt.plot(x,y,lw=3)









    Out[57]:





[<matplotlib.lines.Line2D at 0x7ff61eaeba58>]

Relu

perfect for blacking out everyhing beyong threshold
this is just what everyone actually uses



In [58]:

    
def np_relu(x):
    return np.maximum(0, x)



In [59]:

    
x = np.arange(-10, 10, 0.01)
y = np_relu(x)

centerAxis()
plt.plot(x,y,lw=3)









    Out[59]:





[<matplotlib.lines.Line2D at 0x7ff61b1e45c0>]



In [ ]:

The classic VGG16 Architecture



In [33]:

    
def predict(model, img_path):
    img = image.load_img(img_path, target_size=(224, 224))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)

    preds = model.predict(x)
    # decode the results into a list of tuples (class, description, probability)
    # (one such list for each sample in the batch)
    print('Predicted:', decode_predictions(preds, top=3)[0])



In [34]:

    
from keras import applications
# applications.VGG16?
vgg16_model = applications.VGG16(weights='imagenet')

VGG starts with a number of convolutional blocks for feature extraction and ends with a fully connected classifier



In [35]:

    
vgg16_model.summary()









    



_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_5 (InputLayer)         (None, 224, 224, 3)       0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 25088)             0         
_________________________________________________________________
fc1 (Dense)                  (None, 4096)              102764544 
_________________________________________________________________
fc2 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
predictions (Dense)          (None, 1000)              4097000   
=================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________



In [37]:

    
!curl -O https://upload.wikimedia.org/wikipedia/commons/thumb/d/de/Beagle_Upsy.jpg/440px-Beagle_Upsy.jpg









    



  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 44891  100 44891    0     0    471      0  0:01:35  0:01:35 --:--:--  1989 0:00:40  0:17:08     01   62 27810    0     0    357      0  0:02:05  0:01:17  0:00:48  5322:22     0



In [38]:

    
predict(model = vgg16_model, img_path = '440px-Beagle_Upsy.jpg')









    



Predicted: [('n02088364', 'beagle', 0.32020867), ('n02089973', 'English_foxhound', 0.26515135), ('n02089867', 'Walker_hound', 0.20564148)]



In [47]:

    
!curl -O https://djcordhose.github.io/ai/img/cat-bonkers.png









    



  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  843k  100  843k    0     0   9866      0  0:01:27  0:01:27 --:--:--  156k 0 147      0  1:37:54  0:00:33  1:37:21  1152  0843k    7 64947    0     0   1072      0  0:13:25  0:01:00  0:12:25     0  843k    7 64947    0     0    990      0  0:14:32  0:01:05  0:13:27     0:14  0:01:09  0:11:05  357224  0:01:22  0:09:02  3594



In [52]:

    
predict(model = vgg16_model, img_path = 'cat-bonkers.png')









    



Predicted: [('n02124075', 'Egyptian_cat', 0.93594509), ('n02123045', 'tabby', 0.040695436), ('n02123159', 'tiger_cat', 0.019523595)]



In [32]:

    
!curl -O https://djcordhose.github.io/ai/img/squirrels/original/Michigan-MSU-raschka.jpg
!curl -O https://djcordhose.github.io/ai/img/squirrels/original/Black_New_York_stuy_town_squirrel_amanda_ernlund.jpeg
!curl -O https://djcordhose.github.io/ai/img/squirrels/original/london.jpg









    



  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  126k  100  126k    0     0   6755      0  0:00:19  0:00:19 --:--:-- 18071126k   12 15796    0     0   1254      0  0:01:43  0:00:12  0:01:31  3064
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  302k  100  302k    0     0  83072      0  0:00:03  0:00:03 --:--:-- 85627
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  494k  100  494k    0     0  14207      0  0:00:35  0:00:35 --:--:-- 35943    0    0     0    0     0      0      0 --:--:--  0:00:11 --:--:--     0 3354      0  0:02:30  0:00:19  0:02:11  6509



In [36]:

    
predict(model = vgg16_model, img_path = 'Michigan-MSU-raschka.jpg')









    



Predicted: [('n02356798', 'fox_squirrel', 0.99995279), ('n02361337', 'marmot', 4.3569533e-05), ('n02120505', 'grey_fox', 2.3456425e-06)]



In [25]:

    
predict(model = vgg16_model, img_path = 'Black_New_York_stuy_town_squirrel_amanda_ernlund.jpeg')









    



Predicted: [('n01514859', 'hen', 0.251127), ('n01514668', 'cock', 0.13989805), ('n02486410', 'baboon', 0.13570367)]



In [26]:

    
predict(model = vgg16_model, img_path = 'london.jpg')









    



Predicted: [('n02655020', 'puffer', 0.21103905), ('n02441942', 'weasel', 0.085964032), ('n01950731', 'sea_slug', 0.06110343)]

What does the CNN "see"?

Does it "see" the right thing?

Each filter output of a convolutional layer is called feature channel
with each input they should ideally either be
- blank if they do not recognize any feature in the input or
- encode what the feature channel "sees" in the input
feature channels directly before FC layers are often called bottleneck feature channels

Some activations from bottleneck features:



In [42]:

    
# create a tmp dir in the local directory this notebook runs in, otherwise quiver will fail (and won't tell you why)
!rm -rf tmp
!mkdir tmp

Visualizing feature channels using Quiver

Only works locally



In [53]:

    
# https://github.com/keplr-io/quiver

# Alternative with more styles of visualization: https://github.com/raghakot/keras-vis

# https://github.com/keplr-io/quiver
from quiver_engine import server
server.launch(vgg16_model, input_folder='.', port=7000)

# open at http://localhost:7000/
# interrupt kernel to return control to notebook









    



Starting webserver from: /home/olli/anaconda3/lib/python3.6/site-packages/quiver_engine






    



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KeyboardInterrupt
Thu Sep 28 20:46:11 2017






    



---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-53-85b6fa938e72> in <module>()
      5 # https://github.com/keplr-io/quiver
      6 from quiver_engine import server
----> 7 server.launch(vgg16_model, input_folder='.', port=7000)
      8 
      9 # open at http://localhost:7000/

~/anaconda3/lib/python3.6/site-packages/quiver_engine/server.py in launch(model, temp_folder, input_folder, port, html_base_dir)
    159         get_app(model, html_base_dir=html_base_dir,
    160                 temp_folder=temp_folder, input_folder=input_folder),
--> 161         port
    162     )
    163 

~/anaconda3/lib/python3.6/site-packages/quiver_engine/server.py in run_app(app, port)
    143     http_server = WSGIServer(('', port), app)
    144     webbrowser.open_new('http://localhost:' + str(port))
--> 145     http_server.serve_forever()
    146 
    147 

~/anaconda3/lib/python3.6/site-packages/gevent/baseserver.py in serve_forever(self, stop_timeout)
    360             self.start()
    361         try:
--> 362             self._stop_event.wait()
    363         finally:
    364             Greenlet.spawn(self.stop, timeout=stop_timeout).join()

~/anaconda3/lib/python3.6/site-packages/gevent/event.py in wait(self, timeout)
    217             noticeable when the *timeout* is present.
    218         """
--> 219         return self._wait(timeout)
    220 
    221     def _reset_internal_locks(self): # pragma: no cover

~/anaconda3/lib/python3.6/site-packages/gevent/event.py in _wait(self, timeout)
    127             return self._wait_return_value(False, False)
    128 
--> 129         gotit = self._wait_core(timeout)
    130         return self._wait_return_value(True, gotit)
    131 

~/anaconda3/lib/python3.6/site-packages/gevent/event.py in _wait_core(self, timeout, catch)
    104             try:
    105                 try:
--> 106                     result = self.hub.switch()
    107                     if result is not self: # pragma: no cover
    108                         raise InvalidSwitchError('Invalid switch into Event.wait(): %r' % (result, ))

~/anaconda3/lib/python3.6/site-packages/gevent/hub.py in switch(self)
    628         if switch_out is not None:
    629             switch_out()
--> 630         return RawGreenlet.switch(self)
    631 
    632     def switch_out(self):

KeyboardInterrupt:

Modern Alternative: Resnet

https://keras.io/applications/#resnet50
https://arxiv.org/abs/1512.03385
New Layer Type: https://keras.io/layers/normalization/



In [49]:

    
from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

resnet_model = ResNet50(weights='imagenet')



In [50]:

    
resnet_model.summary()









    



____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_6 (InputLayer)             (None, 224, 224, 3)   0                                            
____________________________________________________________________________________________________
conv1 (Conv2D)                   (None, 112, 112, 64)  9472        input_6[0][0]                    
____________________________________________________________________________________________________
bn_conv1 (BatchNormalization)    (None, 112, 112, 64)  256         conv1[0][0]                      
____________________________________________________________________________________________________
activation_50 (Activation)       (None, 112, 112, 64)  0           bn_conv1[0][0]                   
____________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D)   (None, 55, 55, 64)    0           activation_50[0][0]              
____________________________________________________________________________________________________
res2a_branch2a (Conv2D)          (None, 55, 55, 64)    4160        max_pooling2d_2[0][0]            
____________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizatio (None, 55, 55, 64)    256         res2a_branch2a[0][0]             
____________________________________________________________________________________________________
activation_51 (Activation)       (None, 55, 55, 64)    0           bn2a_branch2a[0][0]              
____________________________________________________________________________________________________
res2a_branch2b (Conv2D)          (None, 55, 55, 64)    36928       activation_51[0][0]              
____________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizatio (None, 55, 55, 64)    256         res2a_branch2b[0][0]             
____________________________________________________________________________________________________
activation_52 (Activation)       (None, 55, 55, 64)    0           bn2a_branch2b[0][0]              
____________________________________________________________________________________________________
res2a_branch2c (Conv2D)          (None, 55, 55, 256)   16640       activation_52[0][0]              
____________________________________________________________________________________________________
res2a_branch1 (Conv2D)           (None, 55, 55, 256)   16640       max_pooling2d_2[0][0]            
____________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizatio (None, 55, 55, 256)   1024        res2a_branch2c[0][0]             
____________________________________________________________________________________________________
bn2a_branch1 (BatchNormalization (None, 55, 55, 256)   1024        res2a_branch1[0][0]              
____________________________________________________________________________________________________
add_17 (Add)                     (None, 55, 55, 256)   0           bn2a_branch2c[0][0]              
                                                                   bn2a_branch1[0][0]               
____________________________________________________________________________________________________
activation_53 (Activation)       (None, 55, 55, 256)   0           add_17[0][0]                     
____________________________________________________________________________________________________
res2b_branch2a (Conv2D)          (None, 55, 55, 64)    16448       activation_53[0][0]              
____________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizatio (None, 55, 55, 64)    256         res2b_branch2a[0][0]             
____________________________________________________________________________________________________
activation_54 (Activation)       (None, 55, 55, 64)    0           bn2b_branch2a[0][0]              
____________________________________________________________________________________________________
res2b_branch2b (Conv2D)          (None, 55, 55, 64)    36928       activation_54[0][0]              
____________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizatio (None, 55, 55, 64)    256         res2b_branch2b[0][0]             
____________________________________________________________________________________________________
activation_55 (Activation)       (None, 55, 55, 64)    0           bn2b_branch2b[0][0]              
____________________________________________________________________________________________________
res2b_branch2c (Conv2D)          (None, 55, 55, 256)   16640       activation_55[0][0]              
____________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizatio (None, 55, 55, 256)   1024        res2b_branch2c[0][0]             
____________________________________________________________________________________________________
add_18 (Add)                     (None, 55, 55, 256)   0           bn2b_branch2c[0][0]              
                                                                   activation_53[0][0]              
____________________________________________________________________________________________________
activation_56 (Activation)       (None, 55, 55, 256)   0           add_18[0][0]                     
____________________________________________________________________________________________________
res2c_branch2a (Conv2D)          (None, 55, 55, 64)    16448       activation_56[0][0]              
____________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizatio (None, 55, 55, 64)    256         res2c_branch2a[0][0]             
____________________________________________________________________________________________________
activation_57 (Activation)       (None, 55, 55, 64)    0           bn2c_branch2a[0][0]              
____________________________________________________________________________________________________
res2c_branch2b (Conv2D)          (None, 55, 55, 64)    36928       activation_57[0][0]              
____________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizatio (None, 55, 55, 64)    256         res2c_branch2b[0][0]             
____________________________________________________________________________________________________
activation_58 (Activation)       (None, 55, 55, 64)    0           bn2c_branch2b[0][0]              
____________________________________________________________________________________________________
res2c_branch2c (Conv2D)          (None, 55, 55, 256)   16640       activation_58[0][0]              
____________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizatio (None, 55, 55, 256)   1024        res2c_branch2c[0][0]             
____________________________________________________________________________________________________
add_19 (Add)                     (None, 55, 55, 256)   0           bn2c_branch2c[0][0]              
                                                                   activation_56[0][0]              
____________________________________________________________________________________________________
activation_59 (Activation)       (None, 55, 55, 256)   0           add_19[0][0]                     
____________________________________________________________________________________________________
res3a_branch2a (Conv2D)          (None, 28, 28, 128)   32896       activation_59[0][0]              
____________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizatio (None, 28, 28, 128)   512         res3a_branch2a[0][0]             
____________________________________________________________________________________________________
activation_60 (Activation)       (None, 28, 28, 128)   0           bn3a_branch2a[0][0]              
____________________________________________________________________________________________________
res3a_branch2b (Conv2D)          (None, 28, 28, 128)   147584      activation_60[0][0]              
____________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizatio (None, 28, 28, 128)   512         res3a_branch2b[0][0]             
____________________________________________________________________________________________________
activation_61 (Activation)       (None, 28, 28, 128)   0           bn3a_branch2b[0][0]              
____________________________________________________________________________________________________
res3a_branch2c (Conv2D)          (None, 28, 28, 512)   66048       activation_61[0][0]              
____________________________________________________________________________________________________
res3a_branch1 (Conv2D)           (None, 28, 28, 512)   131584      activation_59[0][0]              
____________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizatio (None, 28, 28, 512)   2048        res3a_branch2c[0][0]             
____________________________________________________________________________________________________
bn3a_branch1 (BatchNormalization (None, 28, 28, 512)   2048        res3a_branch1[0][0]              
____________________________________________________________________________________________________
add_20 (Add)                     (None, 28, 28, 512)   0           bn3a_branch2c[0][0]              
                                                                   bn3a_branch1[0][0]               
____________________________________________________________________________________________________
activation_62 (Activation)       (None, 28, 28, 512)   0           add_20[0][0]                     
____________________________________________________________________________________________________
res3b_branch2a (Conv2D)          (None, 28, 28, 128)   65664       activation_62[0][0]              
____________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizatio (None, 28, 28, 128)   512         res3b_branch2a[0][0]             
____________________________________________________________________________________________________
activation_63 (Activation)       (None, 28, 28, 128)   0           bn3b_branch2a[0][0]              
____________________________________________________________________________________________________
res3b_branch2b (Conv2D)          (None, 28, 28, 128)   147584      activation_63[0][0]              
____________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizatio (None, 28, 28, 128)   512         res3b_branch2b[0][0]             
____________________________________________________________________________________________________
activation_64 (Activation)       (None, 28, 28, 128)   0           bn3b_branch2b[0][0]              
____________________________________________________________________________________________________
res3b_branch2c (Conv2D)          (None, 28, 28, 512)   66048       activation_64[0][0]              
____________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizatio (None, 28, 28, 512)   2048        res3b_branch2c[0][0]             
____________________________________________________________________________________________________
add_21 (Add)                     (None, 28, 28, 512)   0           bn3b_branch2c[0][0]              
                                                                   activation_62[0][0]              
____________________________________________________________________________________________________
activation_65 (Activation)       (None, 28, 28, 512)   0           add_21[0][0]                     
____________________________________________________________________________________________________
res3c_branch2a (Conv2D)          (None, 28, 28, 128)   65664       activation_65[0][0]              
____________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizatio (None, 28, 28, 128)   512         res3c_branch2a[0][0]             
____________________________________________________________________________________________________
activation_66 (Activation)       (None, 28, 28, 128)   0           bn3c_branch2a[0][0]              
____________________________________________________________________________________________________
res3c_branch2b (Conv2D)          (None, 28, 28, 128)   147584      activation_66[0][0]              
____________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizatio (None, 28, 28, 128)   512         res3c_branch2b[0][0]             
____________________________________________________________________________________________________
activation_67 (Activation)       (None, 28, 28, 128)   0           bn3c_branch2b[0][0]              
____________________________________________________________________________________________________
res3c_branch2c (Conv2D)          (None, 28, 28, 512)   66048       activation_67[0][0]              
____________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizatio (None, 28, 28, 512)   2048        res3c_branch2c[0][0]             
____________________________________________________________________________________________________
add_22 (Add)                     (None, 28, 28, 512)   0           bn3c_branch2c[0][0]              
                                                                   activation_65[0][0]              
____________________________________________________________________________________________________
activation_68 (Activation)       (None, 28, 28, 512)   0           add_22[0][0]                     
____________________________________________________________________________________________________
res3d_branch2a (Conv2D)          (None, 28, 28, 128)   65664       activation_68[0][0]              
____________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizatio (None, 28, 28, 128)   512         res3d_branch2a[0][0]             
____________________________________________________________________________________________________
activation_69 (Activation)       (None, 28, 28, 128)   0           bn3d_branch2a[0][0]              
____________________________________________________________________________________________________
res3d_branch2b (Conv2D)          (None, 28, 28, 128)   147584      activation_69[0][0]              
____________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizatio (None, 28, 28, 128)   512         res3d_branch2b[0][0]             
____________________________________________________________________________________________________
activation_70 (Activation)       (None, 28, 28, 128)   0           bn3d_branch2b[0][0]              
____________________________________________________________________________________________________
res3d_branch2c (Conv2D)          (None, 28, 28, 512)   66048       activation_70[0][0]              
____________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizatio (None, 28, 28, 512)   2048        res3d_branch2c[0][0]             
____________________________________________________________________________________________________
add_23 (Add)                     (None, 28, 28, 512)   0           bn3d_branch2c[0][0]              
                                                                   activation_68[0][0]              
____________________________________________________________________________________________________
activation_71 (Activation)       (None, 28, 28, 512)   0           add_23[0][0]                     
____________________________________________________________________________________________________
res4a_branch2a (Conv2D)          (None, 14, 14, 256)   131328      activation_71[0][0]              
____________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024        res4a_branch2a[0][0]             
____________________________________________________________________________________________________
activation_72 (Activation)       (None, 14, 14, 256)   0           bn4a_branch2a[0][0]              
____________________________________________________________________________________________________
res4a_branch2b (Conv2D)          (None, 14, 14, 256)   590080      activation_72[0][0]              
____________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024        res4a_branch2b[0][0]             
____________________________________________________________________________________________________
activation_73 (Activation)       (None, 14, 14, 256)   0           bn4a_branch2b[0][0]              
____________________________________________________________________________________________________
res4a_branch2c (Conv2D)          (None, 14, 14, 1024)  263168      activation_73[0][0]              
____________________________________________________________________________________________________
res4a_branch1 (Conv2D)           (None, 14, 14, 1024)  525312      activation_71[0][0]              
____________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096        res4a_branch2c[0][0]             
____________________________________________________________________________________________________
bn4a_branch1 (BatchNormalization (None, 14, 14, 1024)  4096        res4a_branch1[0][0]              
____________________________________________________________________________________________________
add_24 (Add)                     (None, 14, 14, 1024)  0           bn4a_branch2c[0][0]              
                                                                   bn4a_branch1[0][0]               
____________________________________________________________________________________________________
activation_74 (Activation)       (None, 14, 14, 1024)  0           add_24[0][0]                     
____________________________________________________________________________________________________
res4b_branch2a (Conv2D)          (None, 14, 14, 256)   262400      activation_74[0][0]              
____________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024        res4b_branch2a[0][0]             
____________________________________________________________________________________________________
activation_75 (Activation)       (None, 14, 14, 256)   0           bn4b_branch2a[0][0]              
____________________________________________________________________________________________________
res4b_branch2b (Conv2D)          (None, 14, 14, 256)   590080      activation_75[0][0]              
____________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024        res4b_branch2b[0][0]             
____________________________________________________________________________________________________
activation_76 (Activation)       (None, 14, 14, 256)   0           bn4b_branch2b[0][0]              
____________________________________________________________________________________________________
res4b_branch2c (Conv2D)          (None, 14, 14, 1024)  263168      activation_76[0][0]              
____________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096        res4b_branch2c[0][0]             
____________________________________________________________________________________________________
add_25 (Add)                     (None, 14, 14, 1024)  0           bn4b_branch2c[0][0]              
                                                                   activation_74[0][0]              
____________________________________________________________________________________________________
activation_77 (Activation)       (None, 14, 14, 1024)  0           add_25[0][0]                     
____________________________________________________________________________________________________
res4c_branch2a (Conv2D)          (None, 14, 14, 256)   262400      activation_77[0][0]              
____________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024        res4c_branch2a[0][0]             
____________________________________________________________________________________________________
activation_78 (Activation)       (None, 14, 14, 256)   0           bn4c_branch2a[0][0]              
____________________________________________________________________________________________________
res4c_branch2b (Conv2D)          (None, 14, 14, 256)   590080      activation_78[0][0]              
____________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024        res4c_branch2b[0][0]             
____________________________________________________________________________________________________
activation_79 (Activation)       (None, 14, 14, 256)   0           bn4c_branch2b[0][0]              
____________________________________________________________________________________________________
res4c_branch2c (Conv2D)          (None, 14, 14, 1024)  263168      activation_79[0][0]              
____________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096        res4c_branch2c[0][0]             
____________________________________________________________________________________________________
add_26 (Add)                     (None, 14, 14, 1024)  0           bn4c_branch2c[0][0]              
                                                                   activation_77[0][0]              
____________________________________________________________________________________________________
activation_80 (Activation)       (None, 14, 14, 1024)  0           add_26[0][0]                     
____________________________________________________________________________________________________
res4d_branch2a (Conv2D)          (None, 14, 14, 256)   262400      activation_80[0][0]              
____________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024        res4d_branch2a[0][0]             
____________________________________________________________________________________________________
activation_81 (Activation)       (None, 14, 14, 256)   0           bn4d_branch2a[0][0]              
____________________________________________________________________________________________________
res4d_branch2b (Conv2D)          (None, 14, 14, 256)   590080      activation_81[0][0]              
____________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024        res4d_branch2b[0][0]             
____________________________________________________________________________________________________
activation_82 (Activation)       (None, 14, 14, 256)   0           bn4d_branch2b[0][0]              
____________________________________________________________________________________________________
res4d_branch2c (Conv2D)          (None, 14, 14, 1024)  263168      activation_82[0][0]              
____________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096        res4d_branch2c[0][0]             
____________________________________________________________________________________________________
add_27 (Add)                     (None, 14, 14, 1024)  0           bn4d_branch2c[0][0]              
                                                                   activation_80[0][0]              
____________________________________________________________________________________________________
activation_83 (Activation)       (None, 14, 14, 1024)  0           add_27[0][0]                     
____________________________________________________________________________________________________
res4e_branch2a (Conv2D)          (None, 14, 14, 256)   262400      activation_83[0][0]              
____________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024        res4e_branch2a[0][0]             
____________________________________________________________________________________________________
activation_84 (Activation)       (None, 14, 14, 256)   0           bn4e_branch2a[0][0]              
____________________________________________________________________________________________________
res4e_branch2b (Conv2D)          (None, 14, 14, 256)   590080      activation_84[0][0]              
____________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024        res4e_branch2b[0][0]             
____________________________________________________________________________________________________
activation_85 (Activation)       (None, 14, 14, 256)   0           bn4e_branch2b[0][0]              
____________________________________________________________________________________________________
res4e_branch2c (Conv2D)          (None, 14, 14, 1024)  263168      activation_85[0][0]              
____________________________________________________________________________________________________
bn4e_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096        res4e_branch2c[0][0]             
____________________________________________________________________________________________________
add_28 (Add)                     (None, 14, 14, 1024)  0           bn4e_branch2c[0][0]              
                                                                   activation_83[0][0]              
____________________________________________________________________________________________________
activation_86 (Activation)       (None, 14, 14, 1024)  0           add_28[0][0]                     
____________________________________________________________________________________________________
res4f_branch2a (Conv2D)          (None, 14, 14, 256)   262400      activation_86[0][0]              
____________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizatio (None, 14, 14, 256)   1024        res4f_branch2a[0][0]             
____________________________________________________________________________________________________
activation_87 (Activation)       (None, 14, 14, 256)   0           bn4f_branch2a[0][0]              
____________________________________________________________________________________________________
res4f_branch2b (Conv2D)          (None, 14, 14, 256)   590080      activation_87[0][0]              
____________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizatio (None, 14, 14, 256)   1024        res4f_branch2b[0][0]             
____________________________________________________________________________________________________
activation_88 (Activation)       (None, 14, 14, 256)   0           bn4f_branch2b[0][0]              
____________________________________________________________________________________________________
res4f_branch2c (Conv2D)          (None, 14, 14, 1024)  263168      activation_88[0][0]              
____________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizatio (None, 14, 14, 1024)  4096        res4f_branch2c[0][0]             
____________________________________________________________________________________________________
add_29 (Add)                     (None, 14, 14, 1024)  0           bn4f_branch2c[0][0]              
                                                                   activation_86[0][0]              
____________________________________________________________________________________________________
activation_89 (Activation)       (None, 14, 14, 1024)  0           add_29[0][0]                     
____________________________________________________________________________________________________
res5a_branch2a (Conv2D)          (None, 7, 7, 512)     524800      activation_89[0][0]              
____________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizatio (None, 7, 7, 512)     2048        res5a_branch2a[0][0]             
____________________________________________________________________________________________________
activation_90 (Activation)       (None, 7, 7, 512)     0           bn5a_branch2a[0][0]              
____________________________________________________________________________________________________
res5a_branch2b (Conv2D)          (None, 7, 7, 512)     2359808     activation_90[0][0]              
____________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizatio (None, 7, 7, 512)     2048        res5a_branch2b[0][0]             
____________________________________________________________________________________________________
activation_91 (Activation)       (None, 7, 7, 512)     0           bn5a_branch2b[0][0]              
____________________________________________________________________________________________________
res5a_branch2c (Conv2D)          (None, 7, 7, 2048)    1050624     activation_91[0][0]              
____________________________________________________________________________________________________
res5a_branch1 (Conv2D)           (None, 7, 7, 2048)    2099200     activation_89[0][0]              
____________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizatio (None, 7, 7, 2048)    8192        res5a_branch2c[0][0]             
____________________________________________________________________________________________________
bn5a_branch1 (BatchNormalization (None, 7, 7, 2048)    8192        res5a_branch1[0][0]              
____________________________________________________________________________________________________
add_30 (Add)                     (None, 7, 7, 2048)    0           bn5a_branch2c[0][0]              
                                                                   bn5a_branch1[0][0]               
____________________________________________________________________________________________________
activation_92 (Activation)       (None, 7, 7, 2048)    0           add_30[0][0]                     
____________________________________________________________________________________________________
res5b_branch2a (Conv2D)          (None, 7, 7, 512)     1049088     activation_92[0][0]              
____________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizatio (None, 7, 7, 512)     2048        res5b_branch2a[0][0]             
____________________________________________________________________________________________________
activation_93 (Activation)       (None, 7, 7, 512)     0           bn5b_branch2a[0][0]              
____________________________________________________________________________________________________
res5b_branch2b (Conv2D)          (None, 7, 7, 512)     2359808     activation_93[0][0]              
____________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizatio (None, 7, 7, 512)     2048        res5b_branch2b[0][0]             
____________________________________________________________________________________________________
activation_94 (Activation)       (None, 7, 7, 512)     0           bn5b_branch2b[0][0]              
____________________________________________________________________________________________________
res5b_branch2c (Conv2D)          (None, 7, 7, 2048)    1050624     activation_94[0][0]              
____________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizatio (None, 7, 7, 2048)    8192        res5b_branch2c[0][0]             
____________________________________________________________________________________________________
add_31 (Add)                     (None, 7, 7, 2048)    0           bn5b_branch2c[0][0]              
                                                                   activation_92[0][0]              
____________________________________________________________________________________________________
activation_95 (Activation)       (None, 7, 7, 2048)    0           add_31[0][0]                     
____________________________________________________________________________________________________
res5c_branch2a (Conv2D)          (None, 7, 7, 512)     1049088     activation_95[0][0]              
____________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizatio (None, 7, 7, 512)     2048        res5c_branch2a[0][0]             
____________________________________________________________________________________________________
activation_96 (Activation)       (None, 7, 7, 512)     0           bn5c_branch2a[0][0]              
____________________________________________________________________________________________________
res5c_branch2b (Conv2D)          (None, 7, 7, 512)     2359808     activation_96[0][0]              
____________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizatio (None, 7, 7, 512)     2048        res5c_branch2b[0][0]             
____________________________________________________________________________________________________
activation_97 (Activation)       (None, 7, 7, 512)     0           bn5c_branch2b[0][0]              
____________________________________________________________________________________________________
res5c_branch2c (Conv2D)          (None, 7, 7, 2048)    1050624     activation_97[0][0]              
____________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizatio (None, 7, 7, 2048)    8192        res5c_branch2c[0][0]             
____________________________________________________________________________________________________
add_32 (Add)                     (None, 7, 7, 2048)    0           bn5c_branch2c[0][0]              
                                                                   activation_95[0][0]              
____________________________________________________________________________________________________
activation_98 (Activation)       (None, 7, 7, 2048)    0           add_32[0][0]                     
____________________________________________________________________________________________________
avg_pool (AveragePooling2D)      (None, 1, 1, 2048)    0           activation_98[0][0]              
____________________________________________________________________________________________________
flatten_2 (Flatten)              (None, 2048)          0           avg_pool[0][0]                   
____________________________________________________________________________________________________
fc1000 (Dense)                   (None, 1000)          2049000     flatten_2[0][0]                  
====================================================================================================
Total params: 25,636,712
Trainable params: 25,583,592
Non-trainable params: 53,120
____________________________________________________________________________________________________



In [51]:

    
predict(model = resnet_model, img_path = 'cat-bonkers.png')









    



Predicted: [('n02124075', 'Egyptian_cat', 0.84642726), ('n02123045', 'tabby', 0.11844059), ('n02123159', 'tiger_cat', 0.032821048)]



In [19]:

    
predict(model = resnet_model, img_path = 'Michigan-MSU-raschka.jpg')









    



Predicted: [('n02356798', 'fox_squirrel', 0.99944216), ('n02361337', 'marmot', 0.00048720645), ('n02120505', 'grey_fox', 2.5803765e-05)]



In [18]:

    
predict(model = resnet_model, img_path = 'Black_New_York_stuy_town_squirrel_amanda_ernlund.jpeg')









    



Predicted: [('n02096177', 'cairn', 0.24615261), ('n02487347', 'macaque', 0.094039716), ('n02112018', 'Pomeranian', 0.087077923)]



In [17]:

    
predict(model = resnet_model, img_path = 'london.jpg')









    



Predicted: [('n02356798', 'fox_squirrel', 0.41871732), ('n03223299', 'doormat', 0.19806233), ('n02321529', 'sea_cucumber', 0.056219026)]

Hands-On 1 (CNN Overview)

Experiment with all Kinds of Layers: https://transcranial.github.io/keras-js/#/mnist-cnn

Try to fool the network by incrementally drawing ambiguous digits

Side Node: Keras.js makes all Keras Models available in the Browser

Hands-On 2 (Filter Kernel Details)

Try out Filter Kernels: http://setosa.io/ev/image-kernels/

Try out Filter Kernels Sharpen and Blur on a speed limit sign: https://github.com/DJCordhose/speed-limit-signs/raw/master/data/real-world/4/100-sky-cutoff-detail.jpg
Create a custom filter



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