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Deep Learning using Python + Keras (Chapter 3): ResNet

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18 Jun 2018CPOL3 min read
Third article of a series of articles introducing deep learning coding in Python and Keras framework

Introduction

This article doesn't give you an introduction to deep learning. You are supposed to know the basics of deep learning and a little of Python coding. The main objective of this article is to introduce you to the basics of Keras framework and use with another known library to make a quick experiment and take the first conclusions.

Background

This article shows the ResNet architecture which was introduced by Microsoft, and won the ILSVRC (ImageNet Large Scale Visual Recognition Challenge) in 2015. You can see the paper here.

Image 1

The key concept is to increase the layer number introducing a residual connection (with an identity layer). This layer go to the next layer directly, improving the learning process.

Image 2

We will do the same experiment that we did in the previous chapters. I will not show the sections of loading the CIFAR-100 dataset, setting up the experiment and download python libraries. All are the same as in the previous chapter.

Using the Code

Keras has this architecture at our disposal, but has the problem that, by default, the size of the images must be greater than 187 pixels, so we will define a smaller architecture.

Python
def CustomResNet50(include_top=True, input_tensor=None, 
                   input_shape=(32,32,3), pooling=None, classes=100):
    if input_tensor is None:
        img_input = Input(shape=input_shape)
    else:
        if not K.is_keras_tensor(input_tensor):
            img_input = Input(tensor=input_tensor, shape=input_shape)
        else:
            img_input = input_tensor
    if K.image_data_format() == 'channels_last':
        bn_axis = 3
    else:
        bn_axis = 1

    x = ZeroPadding2D(padding=(2, 2), name='conv1_pad')(img_input)
    
    x = resnet50.conv_block(x, 3, [32, 32, 64], stage=2, block='a')
    x = resnet50.identity_block(x, 3, [32, 32, 64], stage=2, block='b')
    x = resnet50.identity_block(x, 3, [32, 32, 64], stage=2, block='c')

    x = resnet50.conv_block(x, 3, [64, 64, 256], stage=3, block='a', strides=(1, 1))
    x = resnet50.identity_block(x, 3, [64, 64, 256], stage=3, block='b')
    x = resnet50.identity_block(x, 3, [64, 64, 256], stage=3, block='c')

    x = resnet50.conv_block(x, 3, [128, 128, 512], stage=4, block='a')
    x = resnet50.identity_block(x, 3, [128, 128, 512], stage=4, block='b')
    x = resnet50.identity_block(x, 3, [128, 128, 512], stage=4, block='c')
    x = resnet50.identity_block(x, 3, [128, 128, 512], stage=4, block='d')

    x = resnet50.conv_block(x, 3, [256, 256, 1024], stage=5, block='a')
    x = resnet50.identity_block(x, 3, [256, 256, 1024], stage=5, block='b')
    x = resnet50.identity_block(x, 3, [256, 256, 1024], stage=5, block='c')
    x = resnet50.identity_block(x, 3, [256, 256, 1024], stage=5, block='d')
    x = resnet50.identity_block(x, 3, [256, 256, 1024], stage=5, block='e')
    x = resnet50.identity_block(x, 3, [256, 256, 1024], stage=5, block='f')

    x = resnet50.conv_block(x, 3, [512, 512, 2048], stage=6, block='a')
    x = resnet50.identity_block(x, 3, [512, 512, 2048], stage=6, block='b')
    x = resnet50.identity_block(x, 3, [512, 512, 2048], stage=6, block='c')

    x = AveragePooling2D((1, 1), name='avg_pool')(x)

    if include_top:
        x = Flatten()(x)
        x = Dense(classes, activation='softmax', name='fc1000')(x)
    else:
        if pooling == 'avg':
            x = GlobalAveragePooling2D()(x)
        elif pooling == 'max':
            x = GlobalMaxPooling2D()(x)

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = get_source_inputs(input_tensor)
    else:
        inputs = img_input
    # Create model.
    model = Model(inputs, x, name='resnet50')

    return model

Like previous articles, we compile with same parameters:

Python
def create_custom_resnet50():
  model = CustomResNet50(include_top=True, input_tensor=None, 
                         input_shape=(32,32,3), pooling=None, classes=100)
  
  return model
  
custom_resnet50_model = create_custom_resnet50()
custom_resnet50_model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['acc', 'mse'])

Once it's done, we can see a summary of the model created.

Python
custom_resnet50_model.summary()
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            (None, 32, 32, 3)    0                                            
__________________________________________________________________________________________________
conv1_pad (ZeroPadding2D)       (None, 36, 36, 3)    0           input_1[0][0]                    
__________________________________________________________________________________________________
res2a_branch2a (Conv2D)         (None, 18, 18, 32)   128         conv1_pad[0][0]                  
__________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizati (None, 18, 18, 32)   128         res2a_branch2a[0][0]             
__________________________________________________________________________________________________
activation_1 (Activation)       (None, 18, 18, 32)   0           bn2a_branch2a[0][0]              
__________________________________________________________________________________________________
res2a_branch2b (Conv2D)         (None, 18, 18, 32)   9248        activation_1[0][0]               
__________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizati (None, 18, 18, 32)   128         res2a_branch2b[0][0]             
__________________________________________________________________________________________________
activation_2 (Activation)       (None, 18, 18, 32)   0           bn2a_branch2b[0][0]              
__________________________________________________________________________________________________
res2a_branch2c (Conv2D)         (None, 18, 18, 64)   2112        activation_2[0][0]               
__________________________________________________________________________________________________
res2a_branch1 (Conv2D)          (None, 18, 18, 64)   256         conv1_pad[0][0]                  
__________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizati (None, 18, 18, 64)   256         res2a_branch2c[0][0]             
__________________________________________________________________________________________________
bn2a_branch1 (BatchNormalizatio (None, 18, 18, 64)   256         res2a_branch1[0][0]              
__________________________________________________________________________________________________
add_1 (Add)                     (None, 18, 18, 64)   0           bn2a_branch2c[0][0]              
                                                                 bn2a_branch1[0][0]               
__________________________________________________________________________________________________
activation_3 (Activation)       (None, 18, 18, 64)   0           add_1[0][0]                      
__________________________________________________________________________________________________
res2b_branch2a (Conv2D)         (None, 18, 18, 32)   2080        activation_3[0][0]               
__________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizati (None, 18, 18, 32)   128         res2b_branch2a[0][0]             
__________________________________________________________________________________________________
activation_4 (Activation)       (None, 18, 18, 32)   0           bn2b_branch2a[0][0]              
__________________________________________________________________________________________________
res2b_branch2b (Conv2D)         (None, 18, 18, 32)   9248        activation_4[0][0]               
__________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizati (None, 18, 18, 32)   128         res2b_branch2b[0][0]             
__________________________________________________________________________________________________
activation_5 (Activation)       (None, 18, 18, 32)   0           bn2b_branch2b[0][0]              
__________________________________________________________________________________________________
res2b_branch2c (Conv2D)         (None, 18, 18, 64)   2112        activation_5[0][0]               
__________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizati (None, 18, 18, 64)   256         res2b_branch2c[0][0]             
__________________________________________________________________________________________________
add_2 (Add)                     (None, 18, 18, 64)   0           bn2b_branch2c[0][0]              
                                                                 activation_3[0][0]               
__________________________________________________________________________________________________
activation_6 (Activation)       (None, 18, 18, 64)   0           add_2[0][0]                      
__________________________________________________________________________________________________
res2c_branch2a (Conv2D)         (None, 18, 18, 32)   2080        activation_6[0][0]               
__________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizati (None, 18, 18, 32)   128         res2c_branch2a[0][0]             
__________________________________________________________________________________________________
activation_7 (Activation)       (None, 18, 18, 32)   0           bn2c_branch2a[0][0]              
__________________________________________________________________________________________________
res2c_branch2b (Conv2D)         (None, 18, 18, 32)   9248        activation_7[0][0]               
__________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizati (None, 18, 18, 32)   128         res2c_branch2b[0][0]             
__________________________________________________________________________________________________
activation_8 (Activation)       (None, 18, 18, 32)   0           bn2c_branch2b[0][0]              
__________________________________________________________________________________________________
res2c_branch2c (Conv2D)         (None, 18, 18, 64)   2112        activation_8[0][0]               
__________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizati (None, 18, 18, 64)   256         res2c_branch2c[0][0]             
__________________________________________________________________________________________________
add_3 (Add)                     (None, 18, 18, 64)   0           bn2c_branch2c[0][0]              
                                                                 activation_6[0][0]               
__________________________________________________________________________________________________
activation_9 (Activation)       (None, 18, 18, 64)   0           add_3[0][0]                      
__________________________________________________________________________________________________
res3a_branch2a (Conv2D)         (None, 18, 18, 64)   4160        activation_9[0][0]               
__________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizati (None, 18, 18, 64)   256         res3a_branch2a[0][0]             
__________________________________________________________________________________________________
activation_10 (Activation)      (None, 18, 18, 64)   0           bn3a_branch2a[0][0]              
__________________________________________________________________________________________________
res3a_branch2b (Conv2D)         (None, 18, 18, 64)   36928       activation_10[0][0]              
__________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizati (None, 18, 18, 64)   256         res3a_branch2b[0][0]             
__________________________________________________________________________________________________
activation_11 (Activation)      (None, 18, 18, 64)   0           bn3a_branch2b[0][0]              
__________________________________________________________________________________________________
res3a_branch2c (Conv2D)         (None, 18, 18, 256)  16640       activation_11[0][0]              
__________________________________________________________________________________________________
res3a_branch1 (Conv2D)          (None, 18, 18, 256)  16640       activation_9[0][0]               
__________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizati (None, 18, 18, 256)  1024        res3a_branch2c[0][0]             
__________________________________________________________________________________________________
bn3a_branch1 (BatchNormalizatio (None, 18, 18, 256)  1024        res3a_branch1[0][0]              
__________________________________________________________________________________________________
add_4 (Add)                     (None, 18, 18, 256)  0           bn3a_branch2c[0][0]              
                                                                 bn3a_branch1[0][0]               
__________________________________________________________________________________________________
activation_12 (Activation)      (None, 18, 18, 256)  0           add_4[0][0]                      
__________________________________________________________________________________________________
res3b_branch2a (Conv2D)         (None, 18, 18, 64)   16448       activation_12[0][0]              
__________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizati (None, 18, 18, 64)   256         res3b_branch2a[0][0]             
__________________________________________________________________________________________________
activation_13 (Activation)      (None, 18, 18, 64)   0           bn3b_branch2a[0][0]              
__________________________________________________________________________________________________
res3b_branch2b (Conv2D)         (None, 18, 18, 64)   36928       activation_13[0][0]              
__________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizati (None, 18, 18, 64)   256         res3b_branch2b[0][0]             
__________________________________________________________________________________________________
activation_14 (Activation)      (None, 18, 18, 64)   0           bn3b_branch2b[0][0]              
__________________________________________________________________________________________________
res3b_branch2c (Conv2D)         (None, 18, 18, 256)  16640       activation_14[0][0]              
__________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizati (None, 18, 18, 256)  1024        res3b_branch2c[0][0]             
__________________________________________________________________________________________________
add_5 (Add)                     (None, 18, 18, 256)  0           bn3b_branch2c[0][0]              
                                                                 activation_12[0][0]              
__________________________________________________________________________________________________
activation_15 (Activation)      (None, 18, 18, 256)  0           add_5[0][0]                      
__________________________________________________________________________________________________
res3c_branch2a (Conv2D)         (None, 18, 18, 64)   16448       activation_15[0][0]              
__________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizati (None, 18, 18, 64)   256         res3c_branch2a[0][0]             
__________________________________________________________________________________________________
activation_16 (Activation)      (None, 18, 18, 64)   0           bn3c_branch2a[0][0]              
__________________________________________________________________________________________________
res3c_branch2b (Conv2D)         (None, 18, 18, 64)   36928       activation_16[0][0]              
__________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizati (None, 18, 18, 64)   256         res3c_branch2b[0][0]             
__________________________________________________________________________________________________
activation_17 (Activation)      (None, 18, 18, 64)   0           bn3c_branch2b[0][0]              
__________________________________________________________________________________________________
res3c_branch2c (Conv2D)         (None, 18, 18, 256)  16640       activation_17[0][0]              
__________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizati (None, 18, 18, 256)  1024        res3c_branch2c[0][0]             
__________________________________________________________________________________________________
add_6 (Add)                     (None, 18, 18, 256)  0           bn3c_branch2c[0][0]              
                                                                 activation_15[0][0]              
__________________________________________________________________________________________________
activation_18 (Activation)      (None, 18, 18, 256)  0           add_6[0][0]                      
__________________________________________________________________________________________________
res4a_branch2a (Conv2D)         (None, 9, 9, 128)    32896       activation_18[0][0]              
__________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizati (None, 9, 9, 128)    512         res4a_branch2a[0][0]             
__________________________________________________________________________________________________
activation_19 (Activation)      (None, 9, 9, 128)    0           bn4a_branch2a[0][0]              
__________________________________________________________________________________________________
res4a_branch2b (Conv2D)         (None, 9, 9, 128)    147584      activation_19[0][0]              
__________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizati (None, 9, 9, 128)    512         res4a_branch2b[0][0]             
__________________________________________________________________________________________________
activation_20 (Activation)      (None, 9, 9, 128)    0           bn4a_branch2b[0][0]              
__________________________________________________________________________________________________
res4a_branch2c (Conv2D)         (None, 9, 9, 512)    66048       activation_20[0][0]              
__________________________________________________________________________________________________
res4a_branch1 (Conv2D)          (None, 9, 9, 512)    131584      activation_18[0][0]              
__________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizati (None, 9, 9, 512)    2048        res4a_branch2c[0][0]             
__________________________________________________________________________________________________
bn4a_branch1 (BatchNormalizatio (None, 9, 9, 512)    2048        res4a_branch1[0][0]              
__________________________________________________________________________________________________
add_7 (Add)                     (None, 9, 9, 512)    0           bn4a_branch2c[0][0]              
                                                                 bn4a_branch1[0][0]               
__________________________________________________________________________________________________
activation_21 (Activation)      (None, 9, 9, 512)    0           add_7[0][0]                      
__________________________________________________________________________________________________
res4b_branch2a (Conv2D)         (None, 9, 9, 128)    65664       activation_21[0][0]              
__________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizati (None, 9, 9, 128)    512         res4b_branch2a[0][0]             
__________________________________________________________________________________________________
activation_22 (Activation)      (None, 9, 9, 128)    0           bn4b_branch2a[0][0]              
__________________________________________________________________________________________________
res4b_branch2b (Conv2D)         (None, 9, 9, 128)    147584      activation_22[0][0]              
__________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizati (None, 9, 9, 128)    512         res4b_branch2b[0][0]             
__________________________________________________________________________________________________
activation_23 (Activation)      (None, 9, 9, 128)    0           bn4b_branch2b[0][0]              
__________________________________________________________________________________________________
res4b_branch2c (Conv2D)         (None, 9, 9, 512)    66048       activation_23[0][0]              
__________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizati (None, 9, 9, 512)    2048        res4b_branch2c[0][0]             
__________________________________________________________________________________________________
add_8 (Add)                     (None, 9, 9, 512)    0           bn4b_branch2c[0][0]              
                                                                 activation_21[0][0]              
__________________________________________________________________________________________________
activation_24 (Activation)      (None, 9, 9, 512)    0           add_8[0][0]                      
__________________________________________________________________________________________________
res4c_branch2a (Conv2D)         (None, 9, 9, 128)    65664       activation_24[0][0]              
__________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizati (None, 9, 9, 128)    512         res4c_branch2a[0][0]             
__________________________________________________________________________________________________
activation_25 (Activation)      (None, 9, 9, 128)    0           bn4c_branch2a[0][0]              
__________________________________________________________________________________________________
res4c_branch2b (Conv2D)         (None, 9, 9, 128)    147584      activation_25[0][0]              
__________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizati (None, 9, 9, 128)    512         res4c_branch2b[0][0]             
__________________________________________________________________________________________________
activation_26 (Activation)      (None, 9, 9, 128)    0           bn4c_branch2b[0][0]              
__________________________________________________________________________________________________
res4c_branch2c (Conv2D)         (None, 9, 9, 512)    66048       activation_26[0][0]              
__________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizati (None, 9, 9, 512)    2048        res4c_branch2c[0][0]             
__________________________________________________________________________________________________
add_9 (Add)                     (None, 9, 9, 512)    0           bn4c_branch2c[0][0]              
                                                                 activation_24[0][0]              
__________________________________________________________________________________________________
activation_27 (Activation)      (None, 9, 9, 512)    0           add_9[0][0]                      
__________________________________________________________________________________________________
res4d_branch2a (Conv2D)         (None, 9, 9, 128)    65664       activation_27[0][0]              
__________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizati (None, 9, 9, 128)    512         res4d_branch2a[0][0]             
__________________________________________________________________________________________________
activation_28 (Activation)      (None, 9, 9, 128)    0           bn4d_branch2a[0][0]              
__________________________________________________________________________________________________
res4d_branch2b (Conv2D)         (None, 9, 9, 128)    147584      activation_28[0][0]              
__________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizati (None, 9, 9, 128)    512         res4d_branch2b[0][0]             
__________________________________________________________________________________________________
activation_29 (Activation)      (None, 9, 9, 128)    0           bn4d_branch2b[0][0]              
__________________________________________________________________________________________________
res4d_branch2c (Conv2D)         (None, 9, 9, 512)    66048       activation_29[0][0]              
__________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizati (None, 9, 9, 512)    2048        res4d_branch2c[0][0]             
__________________________________________________________________________________________________
add_10 (Add)                    (None, 9, 9, 512)    0           bn4d_branch2c[0][0]              
                                                                 activation_27[0][0]              
__________________________________________________________________________________________________
activation_30 (Activation)      (None, 9, 9, 512)    0           add_10[0][0]                     
__________________________________________________________________________________________________
res5a_branch2a (Conv2D)         (None, 5, 5, 256)    131328      activation_30[0][0]              
__________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizati (None, 5, 5, 256)    1024        res5a_branch2a[0][0]             
__________________________________________________________________________________________________
activation_31 (Activation)      (None, 5, 5, 256)    0           bn5a_branch2a[0][0]              
__________________________________________________________________________________________________
res5a_branch2b (Conv2D)         (None, 5, 5, 256)    590080      activation_31[0][0]              
__________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizati (None, 5, 5, 256)    1024        res5a_branch2b[0][0]             
__________________________________________________________________________________________________
activation_32 (Activation)      (None, 5, 5, 256)    0           bn5a_branch2b[0][0]              
__________________________________________________________________________________________________
res5a_branch2c (Conv2D)         (None, 5, 5, 1024)   263168      activation_32[0][0]              
__________________________________________________________________________________________________
res5a_branch1 (Conv2D)          (None, 5, 5, 1024)   525312      activation_30[0][0]              
__________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizati (None, 5, 5, 1024)   4096        res5a_branch2c[0][0]             
__________________________________________________________________________________________________
bn5a_branch1 (BatchNormalizatio (None, 5, 5, 1024)   4096        res5a_branch1[0][0]              
__________________________________________________________________________________________________
add_11 (Add)                    (None, 5, 5, 1024)   0           bn5a_branch2c[0][0]              
                                                                 bn5a_branch1[0][0]               
__________________________________________________________________________________________________
activation_33 (Activation)      (None, 5, 5, 1024)   0           add_11[0][0]                     
__________________________________________________________________________________________________
res5b_branch2a (Conv2D)         (None, 5, 5, 256)    262400      activation_33[0][0]              
__________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizati (None, 5, 5, 256)    1024        res5b_branch2a[0][0]             
__________________________________________________________________________________________________
activation_34 (Activation)      (None, 5, 5, 256)    0           bn5b_branch2a[0][0]              
__________________________________________________________________________________________________
res5b_branch2b (Conv2D)         (None, 5, 5, 256)    590080      activation_34[0][0]              
__________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizati (None, 5, 5, 256)    1024        res5b_branch2b[0][0]             
__________________________________________________________________________________________________
activation_35 (Activation)      (None, 5, 5, 256)    0           bn5b_branch2b[0][0]              
__________________________________________________________________________________________________
res5b_branch2c (Conv2D)         (None, 5, 5, 1024)   263168      activation_35[0][0]              
__________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizati (None, 5, 5, 1024)   4096        res5b_branch2c[0][0]             
__________________________________________________________________________________________________
add_12 (Add)                    (None, 5, 5, 1024)   0           bn5b_branch2c[0][0]              
                                                                 activation_33[0][0]              
__________________________________________________________________________________________________
activation_36 (Activation)      (None, 5, 5, 1024)   0           add_12[0][0]                     
__________________________________________________________________________________________________
res5c_branch2a (Conv2D)         (None, 5, 5, 256)    262400      activation_36[0][0]              
__________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizati (None, 5, 5, 256)    1024        res5c_branch2a[0][0]             
__________________________________________________________________________________________________
activation_37 (Activation)      (None, 5, 5, 256)    0           bn5c_branch2a[0][0]              
__________________________________________________________________________________________________
res5c_branch2b (Conv2D)         (None, 5, 5, 256)    590080      activation_37[0][0]              
__________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizati (None, 5, 5, 256)    1024        res5c_branch2b[0][0]             
__________________________________________________________________________________________________
activation_38 (Activation)      (None, 5, 5, 256)    0           bn5c_branch2b[0][0]              
__________________________________________________________________________________________________
res5c_branch2c (Conv2D)         (None, 5, 5, 1024)   263168      activation_38[0][0]              
__________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizati (None, 5, 5, 1024)   4096        res5c_branch2c[0][0]             
__________________________________________________________________________________________________
add_13 (Add)                    (None, 5, 5, 1024)   0           bn5c_branch2c[0][0]              
                                                                 activation_36[0][0]              
__________________________________________________________________________________________________
activation_39 (Activation)      (None, 5, 5, 1024)   0           add_13[0][0]                     
__________________________________________________________________________________________________
res5d_branch2a (Conv2D)         (None, 5, 5, 256)    262400      activation_39[0][0]              
__________________________________________________________________________________________________
bn5d_branch2a (BatchNormalizati (None, 5, 5, 256)    1024        res5d_branch2a[0][0]             
__________________________________________________________________________________________________
activation_40 (Activation)      (None, 5, 5, 256)    0           bn5d_branch2a[0][0]              
__________________________________________________________________________________________________
res5d_branch2b (Conv2D)         (None, 5, 5, 256)    590080      activation_40[0][0]              
__________________________________________________________________________________________________
bn5d_branch2b (BatchNormalizati (None, 5, 5, 256)    1024        res5d_branch2b[0][0]             
__________________________________________________________________________________________________
activation_41 (Activation)      (None, 5, 5, 256)    0           bn5d_branch2b[0][0]              
__________________________________________________________________________________________________
res5d_branch2c (Conv2D)         (None, 5, 5, 1024)   263168      activation_41[0][0]              
__________________________________________________________________________________________________
bn5d_branch2c (BatchNormalizati (None, 5, 5, 1024)   4096        res5d_branch2c[0][0]             
__________________________________________________________________________________________________
add_14 (Add)                    (None, 5, 5, 1024)   0           bn5d_branch2c[0][0]              
                                                                 activation_39[0][0]              
__________________________________________________________________________________________________
activation_42 (Activation)      (None, 5, 5, 1024)   0           add_14[0][0]                     
__________________________________________________________________________________________________
res5e_branch2a (Conv2D)         (None, 5, 5, 256)    262400      activation_42[0][0]              
__________________________________________________________________________________________________
bn5e_branch2a (BatchNormalizati (None, 5, 5, 256)    1024        res5e_branch2a[0][0]             
__________________________________________________________________________________________________
activation_43 (Activation)      (None, 5, 5, 256)    0           bn5e_branch2a[0][0]              
__________________________________________________________________________________________________
res5e_branch2b (Conv2D)         (None, 5, 5, 256)    590080      activation_43[0][0]              
__________________________________________________________________________________________________
bn5e_branch2b (BatchNormalizati (None, 5, 5, 256)    1024        res5e_branch2b[0][0]             
__________________________________________________________________________________________________
activation_44 (Activation)      (None, 5, 5, 256)    0           bn5e_branch2b[0][0]              
__________________________________________________________________________________________________
res5e_branch2c (Conv2D)         (None, 5, 5, 1024)   263168      activation_44[0][0]              
__________________________________________________________________________________________________
bn5e_branch2c (BatchNormalizati (None, 5, 5, 1024)   4096        res5e_branch2c[0][0]             
__________________________________________________________________________________________________
add_15 (Add)                    (None, 5, 5, 1024)   0           bn5e_branch2c[0][0]              
                                                                 activation_42[0][0]              
__________________________________________________________________________________________________
activation_45 (Activation)      (None, 5, 5, 1024)   0           add_15[0][0]                     
__________________________________________________________________________________________________
res5f_branch2a (Conv2D)         (None, 5, 5, 256)    262400      activation_45[0][0]              
__________________________________________________________________________________________________
bn5f_branch2a (BatchNormalizati (None, 5, 5, 256)    1024        res5f_branch2a[0][0]             
__________________________________________________________________________________________________
activation_46 (Activation)      (None, 5, 5, 256)    0           bn5f_branch2a[0][0]              
__________________________________________________________________________________________________
res5f_branch2b (Conv2D)         (None, 5, 5, 256)    590080      activation_46[0][0]              
__________________________________________________________________________________________________
bn5f_branch2b (BatchNormalizati (None, 5, 5, 256)    1024        res5f_branch2b[0][0]             
__________________________________________________________________________________________________
activation_47 (Activation)      (None, 5, 5, 256)    0           bn5f_branch2b[0][0]              
__________________________________________________________________________________________________
res5f_branch2c (Conv2D)         (None, 5, 5, 1024)   263168      activation_47[0][0]              
__________________________________________________________________________________________________
bn5f_branch2c (BatchNormalizati (None, 5, 5, 1024)   4096        res5f_branch2c[0][0]             
__________________________________________________________________________________________________
add_16 (Add)                    (None, 5, 5, 1024)   0           bn5f_branch2c[0][0]              
                                                                 activation_45[0][0]              
__________________________________________________________________________________________________
activation_48 (Activation)      (None, 5, 5, 1024)   0           add_16[0][0]                     
__________________________________________________________________________________________________
res6a_branch2a (Conv2D)         (None, 3, 3, 512)    524800      activation_48[0][0]              
__________________________________________________________________________________________________
bn6a_branch2a (BatchNormalizati (None, 3, 3, 512)    2048        res6a_branch2a[0][0]             
__________________________________________________________________________________________________
activation_49 (Activation)      (None, 3, 3, 512)    0           bn6a_branch2a[0][0]              
__________________________________________________________________________________________________
res6a_branch2b (Conv2D)         (None, 3, 3, 512)    2359808     activation_49[0][0]              
__________________________________________________________________________________________________
bn6a_branch2b (BatchNormalizati (None, 3, 3, 512)    2048        res6a_branch2b[0][0]             
__________________________________________________________________________________________________
activation_50 (Activation)      (None, 3, 3, 512)    0           bn6a_branch2b[0][0]              
__________________________________________________________________________________________________
res6a_branch2c (Conv2D)         (None, 3, 3, 2048)   1050624     activation_50[0][0]              
__________________________________________________________________________________________________
res6a_branch1 (Conv2D)          (None, 3, 3, 2048)   2099200     activation_48[0][0]              
__________________________________________________________________________________________________
bn6a_branch2c (BatchNormalizati (None, 3, 3, 2048)   8192        res6a_branch2c[0][0]             
__________________________________________________________________________________________________
bn6a_branch1 (BatchNormalizatio (None, 3, 3, 2048)   8192        res6a_branch1[0][0]              
__________________________________________________________________________________________________
add_17 (Add)                    (None, 3, 3, 2048)   0           bn6a_branch2c[0][0]              
                                                                 bn6a_branch1[0][0]               
__________________________________________________________________________________________________
activation_51 (Activation)      (None, 3, 3, 2048)   0           add_17[0][0]                     
__________________________________________________________________________________________________
res6b_branch2a (Conv2D)         (None, 3, 3, 512)    1049088     activation_51[0][0]              
__________________________________________________________________________________________________
bn6b_branch2a (BatchNormalizati (None, 3, 3, 512)    2048        res6b_branch2a[0][0]             
__________________________________________________________________________________________________
activation_52 (Activation)      (None, 3, 3, 512)    0           bn6b_branch2a[0][0]              
__________________________________________________________________________________________________
res6b_branch2b (Conv2D)         (None, 3, 3, 512)    2359808     activation_52[0][0]              
__________________________________________________________________________________________________
bn6b_branch2b (BatchNormalizati (None, 3, 3, 512)    2048        res6b_branch2b[0][0]             
__________________________________________________________________________________________________
activation_53 (Activation)      (None, 3, 3, 512)    0           bn6b_branch2b[0][0]              
__________________________________________________________________________________________________
res6b_branch2c (Conv2D)         (None, 3, 3, 2048)   1050624     activation_53[0][0]              
__________________________________________________________________________________________________
bn6b_branch2c (BatchNormalizati (None, 3, 3, 2048)   8192        res6b_branch2c[0][0]             
__________________________________________________________________________________________________
add_18 (Add)                    (None, 3, 3, 2048)   0           bn6b_branch2c[0][0]              
                                                                 activation_51[0][0]              
__________________________________________________________________________________________________
activation_54 (Activation)      (None, 3, 3, 2048)   0           add_18[0][0]                     
__________________________________________________________________________________________________
res6c_branch2a (Conv2D)         (None, 3, 3, 512)    1049088     activation_54[0][0]              
__________________________________________________________________________________________________
bn6c_branch2a (BatchNormalizati (None, 3, 3, 512)    2048        res6c_branch2a[0][0]             
__________________________________________________________________________________________________
activation_55 (Activation)      (None, 3, 3, 512)    0           bn6c_branch2a[0][0]              
__________________________________________________________________________________________________
res6c_branch2b (Conv2D)         (None, 3, 3, 512)    2359808     activation_55[0][0]              
__________________________________________________________________________________________________
bn6c_branch2b (BatchNormalizati (None, 3, 3, 512)    2048        res6c_branch2b[0][0]             
__________________________________________________________________________________________________
activation_56 (Activation)      (None, 3, 3, 512)    0           bn6c_branch2b[0][0]              
__________________________________________________________________________________________________
res6c_branch2c (Conv2D)         (None, 3, 3, 2048)   1050624     activation_56[0][0]              
__________________________________________________________________________________________________
bn6c_branch2c (BatchNormalizati (None, 3, 3, 2048)   8192        res6c_branch2c[0][0]             
__________________________________________________________________________________________________
add_19 (Add)                    (None, 3, 3, 2048)   0           bn6c_branch2c[0][0]              
                                                                 activation_54[0][0]              
__________________________________________________________________________________________________
activation_57 (Activation)      (None, 3, 3, 2048)   0           add_19[0][0]                     
__________________________________________________________________________________________________
avg_pool (AveragePooling2D)     (None, 3, 3, 2048)   0           activation_57[0][0]              
__________________________________________________________________________________________________
flatten_1 (Flatten)             (None, 18432)        0           avg_pool[0][0]                   
__________________________________________________________________________________________________
fc1000 (Dense)                  (None, 100)          1843300     flatten_1[0][0]                  
==================================================================================================
Total params: 25,461,700
Trainable params: 25,407,812
Non-trainable params: 53,888
__________________________________________________________________________________________________

Then, the next step is to train the model.

Python
crn50 = custom_resnet50_model.fit(x=x_train, y=y_train, batch_size=32, 
        epochs=10, verbose=1, validation_data=(x_test, y_test), shuffle=True)

Train on 50000 samples, validate on 10000 samples
Epoch 1/10
 50000/50000 [==============================] - 441s 9ms/step - loss: 4.5655 - acc: 0.0817 
  - mean_squared_error: 0.0101 - val_loss: 4.2085 - val_acc: 0.1228 - val_mean_squared_error: 0.0099
Epoch 2/10
 50000/50000 [==============================] - 434s 9ms/step - loss: 4.1448 - acc: 0.1348 
  - mean_squared_error: 0.0098 - val_loss: 4.2032 - val_acc: 0.1236 - val_mean_squared_error: 0.0099
Epoch 3/10
 50000/50000 [==============================] - 433s 9ms/step - loss: 4.2682 - acc: 0.1146 
  - mean_squared_error: 0.0099 - val_loss: 4.3306 - val_acc: 0.1066 - val_mean_squared_error: 0.0100
Epoch 4/10
 50000/50000 [==============================] - 434s 9ms/step - loss: 4.1581 - acc: 0.1340 
  - mean_squared_error: 0.0098 - val_loss: 4.1405 - val_acc: 0.1384 - val_mean_squared_error: 0.0098
Epoch 5/10
 50000/50000 [==============================] - 431s 9ms/step - loss: 3.9395 - acc: 0.1653 
  - mean_squared_error: 0.0096 - val_loss: 3.8838 - val_acc: 0.1718 - val_mean_squared_error: 0.0095
Epoch 6/10
 50000/50000 [==============================] - 432s 9ms/step - loss: 3.9598 - acc: 0.1698 
  - mean_squared_error: 0.0096 - val_loss: 4.0047 - val_acc: 0.1608 - val_mean_squared_error: 0.0096
Epoch 7/10
 50000/50000 [==============================] - 433s 9ms/step - loss: 3.8715 - acc: 0.1797 
  - mean_squared_error: 0.0095 - val_loss: 4.2620 - val_acc: 0.1184 - val_mean_squared_error: 0.0099
Epoch 8/10
 50000/50000 [==============================] - 434s 9ms/step - loss: 3.9661 - acc: 0.1666 
  - mean_squared_error: 0.0096 - val_loss: 3.8181 - val_acc: 0.1898 - val_mean_squared_error: 0.0095
Epoch 9/10
 50000/50000 [==============================] - 434s 9ms/step - loss: 3.8110 - acc: 0.1901 
  - mean_squared_error: 0.0095 - val_loss: 3.7521 - val_acc: 0.1966 - val_mean_squared_error: 0.0094
Epoch 10/10
 50000/50000 [==============================] - 432s 9ms/step - loss: 3.7247 - acc: 0.2048 
  - mean_squared_error: 0.0094 - val_loss: 3.8206 - val_acc: 0.1929 - val_mean_squared_error: 0.0095

Let's see the metrics for the train and test results graphically (using matplotlib library, of course).

Python
plt.figure(0)
plt.plot(crn50.history['acc'],'r')
plt.plot(crn50.history['val_acc'],'g')
plt.xticks(np.arange(0, 11, 2.0))
plt.rcParams['figure.figsize'] = (8, 6)
plt.xlabel("Num of Epochs")
plt.ylabel("Accuracy")
plt.title("Training Accuracy vs Validation Accuracy")
plt.legend(['train','validation'])
 
plt.figure(1)
plt.plot(crn50.history['loss'],'r')
plt.plot(crn50.history['val_loss'],'g')
plt.xticks(np.arange(0, 11, 2.0))
plt.rcParams['figure.figsize'] = (8, 6)
plt.xlabel("Num of Epochs")
plt.ylabel("Loss")
plt.title("Training Loss vs Validation Loss")
plt.legend(['train','validation'])
 
plt.show()

Image 3

Image 4

The training has given acceptable results and has generalized well (0.0119).

Confusion Matrix

Once we have trained our model, we want to see another metrics before taking any conclusion of the usability of the model we have created. For this, we will create the confusion matrix and, from that, we will see the precision, recall y F1-score metrics (see wikipedia).

To create the confusion matrix, we need to make the predictions over the test set and then, we can create the confusion matrix and show that metrics.

Python
crn50_pred = custom_resnet50_model.predict(x_test, batch_size=32, verbose=1)
crn50_predicted = np.argmax(crn50_pred, axis=1)

crn50_cm = confusion_matrix(np.argmax(y_test, axis=1), crn50_predicted)

# Visualizing of confusion matrix
crn50_df_cm = pd.DataFrame(crn50_cm, range(100), range(100))
plt.figure(figsize = (20,14))
sn.set(font_scale=1.4) #for label size
sn.heatmap(crn50_df_cm, annot=True, annot_kws={"size": 12}) # font size
plt.show()

Image 5

And the next step, show the metrics.

Python
crn50_report = classification_report(np.argmax(y_test, axis=1), crn50_predicted)
print(crn50_report)

             precision    recall  f1-score   support

          0       0.46      0.32      0.38       100
          1       0.25      0.17      0.20       100
          2       0.17      0.09      0.12       100
          3       0.05      0.62      0.09       100
          4       0.18      0.06      0.09       100
          5       0.25      0.05      0.08       100
          6       0.11      0.14      0.12       100
          7       0.15      0.12      0.13       100
          8       0.21      0.20      0.20       100
          9       0.49      0.21      0.29       100
         10       0.11      0.03      0.05       100
         11       0.08      0.05      0.06       100
         12       0.38      0.13      0.19       100
         13       0.23      0.10      0.14       100
         14       0.18      0.05      0.08       100
         15       0.14      0.06      0.08       100
         16       0.19      0.24      0.21       100
         17       0.40      0.19      0.26       100
         18       0.19      0.24      0.21       100
         19       0.20      0.22      0.21       100
         20       0.42      0.31      0.36       100
         21       0.31      0.23      0.26       100
         22       0.35      0.09      0.14       100
         23       0.36      0.37      0.37       100
         24       0.31      0.49      0.38       100
         25       0.17      0.03      0.05       100
         26       0.43      0.06      0.11       100
         27       0.11      0.03      0.05       100
         28       0.31      0.35      0.33       100
         29       0.12      0.10      0.11       100
         30       0.27      0.33      0.30       100
         31       0.11      0.09      0.10       100
         32       0.22      0.20      0.21       100
         33       0.23      0.30      0.26       100
         34       0.17      0.05      0.08       100
         35       0.09      0.02      0.03       100
         36       0.10      0.23      0.14       100
         37       0.15      0.16      0.16       100
         38       0.08      0.24      0.12       100
         39       0.23      0.18      0.20       100
         40       0.26      0.20      0.22       100
         41       0.45      0.49      0.47       100
         42       0.12      0.17      0.14       100
         43       0.11      0.02      0.03       100
         44       0.14      0.09      0.11       100
         45       0.08      0.01      0.02       100
         46       0.07      0.29      0.12       100
         47       0.55      0.18      0.27       100
         48       0.23      0.31      0.26       100
         49       0.27      0.23      0.25       100
         50       0.12      0.05      0.07       100
         51       0.28      0.09      0.14       100
         52       0.47      0.62      0.54       100
         53       0.25      0.13      0.17       100
         54       0.18      0.25      0.21       100
         55       0.00      0.00      0.00       100
         56       0.27      0.27      0.27       100
         57       0.27      0.11      0.16       100
         58       0.15      0.41      0.22       100
         59       0.18      0.10      0.13       100
         60       0.41      0.63      0.50       100
         61       0.33      0.32      0.32       100
         62       0.15      0.07      0.09       100
         63       0.31      0.26      0.28       100
         64       0.11      0.11      0.11       100
         65       0.15      0.11      0.13       100
         66       0.10      0.06      0.08       100
         67       0.15      0.15      0.15       100
         68       0.37      0.66      0.47       100
         69       0.38      0.25      0.30       100
         70       0.21      0.04      0.07       100
         71       0.27      0.54      0.36       100
         72       0.20      0.01      0.02       100
         73       0.30      0.21      0.25       100
         74       0.14      0.15      0.14       100
         75       0.30      0.29      0.29       100
         76       0.40      0.40      0.40       100
         77       0.13      0.14      0.13       100
         78       0.15      0.08      0.10       100
         79       0.14      0.05      0.07       100
         80       0.08      0.05      0.06       100
         81       0.14      0.11      0.12       100
         82       0.37      0.24      0.29       100
         83       0.08      0.02      0.03       100
         84       0.10      0.11      0.10       100
         85       0.23      0.39      0.29       100
         86       0.36      0.21      0.26       100
         87       0.21      0.19      0.20       100
         88       0.05      0.06      0.05       100
         89       0.24      0.18      0.20       100
         90       0.21      0.24      0.22       100
         91       0.33      0.31      0.32       100
         92       0.11      0.11      0.11       100
         93       0.16      0.10      0.12       100
         94       0.38      0.26      0.31       100
         95       0.21      0.50      0.30       100
         96       0.22      0.23      0.22       100
         97       0.10      0.18      0.13       100
         98       0.12      0.02      0.03       100
         99       0.24      0.08      0.12       100

avg / total       0.22      0.19      0.19     10000

ROC Curve

The ROC curve is used by binary clasifiers because is a good tool to see the true positives rate versus false positives. The following lines show the code for the multiclass classification ROC curve. This code is from DloLogy, but you can go to the Scikit Learn documentation page.

Python
from sklearn.datasets import make_classification
from sklearn.preprocessing import label_binarize
from scipy import interp
from itertools import cycle

n_classes = 100

from sklearn.metrics import roc_curve, auc

# Plot linewidth.
lw = 2

# Compute ROC curve and ROC area for each class
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], crn50_pred[:, i])
    roc_auc[i] = auc(fpr[i], tpr[i])

# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), crn50_pred.ravel())
roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])

# Compute macro-average ROC curve and ROC area

# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))

# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
    mean_tpr += interp(all_fpr, fpr[i], tpr[i])

# Finally average it and compute AUC
mean_tpr /= n_classes

fpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])

# Plot all ROC curves
plt.figure(1)
plt.plot(fpr["micro"], tpr["micro"],
         label='micro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["micro"]),
         color='deeppink', linestyle=':', linewidth=4)

plt.plot(fpr["macro"], tpr["macro"],
         label='macro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["macro"]),
         color='navy', linestyle=':', linewidth=4)

colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(n_classes-97), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=lw,
             label='ROC curve of class {0} (area = {1:0.2f})'
             ''.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()


# Zoom in view of the upper left corner.
plt.figure(2)
plt.xlim(0, 0.2)
plt.ylim(0.8, 1)
plt.plot(fpr["micro"], tpr["micro"],
         label='micro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["micro"]),
         color='deeppink', linestyle=':', linewidth=4)

plt.plot(fpr["macro"], tpr["macro"],
         label='macro-average ROC curve (area = {0:0.2f})'
               ''.format(roc_auc["macro"]),
         color='navy', linestyle=':', linewidth=4)

colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(10), colors):
    plt.plot(fpr[i], tpr[i], color=color, lw=lw,
             label='ROC curve of class {0} (area = {1:0.2f})'
             ''.format(i, roc_auc[i]))

plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()

Image 6

Image 7

Then, we will save the train history results to future comparisons and the model.

Python
#Model
custom_resnet50_model.save(path_base + '/crn50.h5')

#Historical results
with open(path_base + '/crn50_history.txt', 'wb') as file_pi:
  pickle.dump(crn50.history, file_pi)

Models Comparisons

The next step is compare the metrics of the previous experiment with this results. We will compare accuracy, loss and mean squared errors for the models ConvNet and regular net that we saw in the previous chapters and some VGG models trained with the same parameters.

Python
plt.figure(0)
plt.plot(snn.history['val_acc'],'r')
plt.plot(scnn.history['val_acc'],'g')
plt.plot(vgg16.history['val_acc'],'b')
plt.plot(vgg19.history['val_acc'],'y')
plt.plot(vgg16Bis.history['val_acc'],'m')
plt.plot(crn50.history['val_acc'],'gold')
plt.xticks(np.arange(0, 11, 2.0))
plt.rcParams['figure.figsize'] = (8, 6)
plt.xlabel("Num of Epochs")
plt.ylabel("Accuracy")
plt.title("Simple NN Accuracy vs simple CNN Accuracy")
plt.legend(['simple NN','CNN','VGG 16','VGG 19','Custom VGG','Custom ResNet'])

Image 8

Python
plt.figure(0)
plt.plot(snn.history['val_loss'],'r')
plt.plot(scnn.history['val_loss'],'g')
plt.plot(vgg16.history['val_loss'],'b')
plt.plot(vgg19.history['val_loss'],'y')
plt.plot(vgg16Bis.history['val_loss'],'m')
plt.plot(crn50.history['val_loss'],'gold')
plt.xticks(np.arange(0, 11, 2.0))
plt.rcParams['figure.figsize'] = (8, 6)
plt.xlabel("Num of Epochs")
plt.ylabel("Loss")
plt.title("Simple NN Loss vs simple CNN Loss")
plt.legend(['simple NN','CNN','VGG 16','VGG 19','Custom VGG','Custom ResNet'])

Image 9

Python
plt.figure(0)
plt.plot(snn.history['val_mean_squared_error'],'r')
plt.plot(scnn.history['val_mean_squared_error'],'g')
plt.plot(vgg16.history['val_mean_squared_error'],'b')
plt.plot(vgg19.history['val_mean_squared_error'],'y')
plt.plot(vgg16Bis.history['val_mean_squared_error'],'m')
plt.plot(crn50.history['val_mean_squared_error'],'gold')
plt.xticks(np.arange(0, 11, 2.0))
plt.rcParams['figure.figsize'] = (8, 6)
plt.xlabel("Num of Epochs")
plt.ylabel("Mean Squared Error")
plt.title("Simple NN MSE vs simple CNN MSE")
plt.legend(['simple NN','CNN','VGG 16','VGG 19','Custom VGG','Custom ResNet'])

Image 10

Conclusion

As you can see, the architecture marks a turning point. Not only because it is of the best results than the previous architectures, but also in the training times, since it allows to increase the layers with an acceptable time; and also in the number of parameters, which has been reduced considerably with respect to the VGG architecture.

In the next article, we will show the DenseNet.

License

This article, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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About the Author

Jesús Utrera
Software Developer (Senior) AtSistemas
Spain Spain
Work at AtSistemas in Jerez de la Frontera (Cádiz)

Comments and Discussions

 
QuestionWhere are the links to Chapter 1 and 2? Pin
Jerry72220-Jun-18 6:40
professionalJerry72220-Jun-18 6:40 

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