

In [1]:

    
import keras
from keras.applications.vgg16 import VGG16









    



Using TensorFlow backend.



In [2]:

    
big_fancy_model = VGG16(weights=None, input_shape=(224, 224, 3))
big_fancy_model.summary()









    



WARNING:tensorflow:From /srv/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:1047: calling reduce_prod (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_1 (InputLayer)             (None, 224, 224, 3)   0                                            
____________________________________________________________________________________________________
block1_conv1 (Convolution2D)     (None, 224, 224, 64)  1792        input_1[0][0]                    
____________________________________________________________________________________________________
block1_conv2 (Convolution2D)     (None, 224, 224, 64)  36928       block1_conv1[0][0]               
____________________________________________________________________________________________________
block1_pool (MaxPooling2D)       (None, 112, 112, 64)  0           block1_conv2[0][0]               
____________________________________________________________________________________________________
block2_conv1 (Convolution2D)     (None, 112, 112, 128) 73856       block1_pool[0][0]                
____________________________________________________________________________________________________
block2_conv2 (Convolution2D)     (None, 112, 112, 128) 147584      block2_conv1[0][0]               
____________________________________________________________________________________________________
block2_pool (MaxPooling2D)       (None, 56, 56, 128)   0           block2_conv2[0][0]               
____________________________________________________________________________________________________
block3_conv1 (Convolution2D)     (None, 56, 56, 256)   295168      block2_pool[0][0]                
____________________________________________________________________________________________________
block3_conv2 (Convolution2D)     (None, 56, 56, 256)   590080      block3_conv1[0][0]               
____________________________________________________________________________________________________
block3_conv3 (Convolution2D)     (None, 56, 56, 256)   590080      block3_conv2[0][0]               
____________________________________________________________________________________________________
block3_pool (MaxPooling2D)       (None, 28, 28, 256)   0           block3_conv3[0][0]               
____________________________________________________________________________________________________
block4_conv1 (Convolution2D)     (None, 28, 28, 512)   1180160     block3_pool[0][0]                
____________________________________________________________________________________________________
block4_conv2 (Convolution2D)     (None, 28, 28, 512)   2359808     block4_conv1[0][0]               
____________________________________________________________________________________________________
block4_conv3 (Convolution2D)     (None, 28, 28, 512)   2359808     block4_conv2[0][0]               
____________________________________________________________________________________________________
block4_pool (MaxPooling2D)       (None, 14, 14, 512)   0           block4_conv3[0][0]               
____________________________________________________________________________________________________
block5_conv1 (Convolution2D)     (None, 14, 14, 512)   2359808     block4_pool[0][0]                
____________________________________________________________________________________________________
block5_conv2 (Convolution2D)     (None, 14, 14, 512)   2359808     block5_conv1[0][0]               
____________________________________________________________________________________________________
block5_conv3 (Convolution2D)     (None, 14, 14, 512)   2359808     block5_conv2[0][0]               
____________________________________________________________________________________________________
block5_pool (MaxPooling2D)       (None, 7, 7, 512)     0           block5_conv3[0][0]               
____________________________________________________________________________________________________
flatten (Flatten)                (None, 25088)         0           block5_pool[0][0]                
____________________________________________________________________________________________________
fc1 (Dense)                      (None, 4096)          102764544   flatten[0][0]                    
____________________________________________________________________________________________________
fc2 (Dense)                      (None, 4096)          16781312    fc1[0][0]                        
____________________________________________________________________________________________________
predictions (Dense)              (None, 1000)          4097000     fc2[0][0]                        
====================================================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
____________________________________________________________________________________________________

Wrap the Model

Here we wrap the model in the necessary HTML and JS so we can show it as a widget in jupyter



In [3]:

    
from IPython.display import HTML
HTML_TEMPLATE = """<script type="text/javascript" src="../dist/keras-model-editor.js"></script>
<style type="text/css">
      .model {
        height: 300px;
      }
    </style>
<div id="kerasmodel" class="model" height="300px" width="100%%"></div>
<script type="text/javascript">
let kmodel = JSON.parse('%s');
this.model = new KerasModelEditor(kmodel, 'kerasmodel', {
        "rankdir": "LR", // "LR"  "UD"
        "nodesep": 20,
        "edgesep": 20,
        "ranksep": 40,
        "marginx": 0,
        "marginy": 0
      });

this.model.show();
</script>
"""



In [4]:

    
full_html = HTML_TEMPLATE % (big_fancy_model.to_json())
HTML(full_html)









    Out[4]:



In [5]:

    
with open('test.html', 'w') as f:
    f.write(full_html)



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