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Technical thoughts, stories and ideas

Deep Learning básico con Keras (Parte 4): ResNet

Publicado por Jesús Utrera Burgal el

Deep LearningKerasResNet

En este artículo vamos a mostrar la arquitectura ResNet. Ésta fue introducida por Microsoft, ganando la competición ILSVRC (ImageNet Large Scale Visual Recognition Challenge) en el año 2015. En el siguiente enlace se puede acceder al paper: https://arxiv.org/abs/1512.03385.

En este diagrama, podemos ver la arquitectura ResNet:
Diagrama de arquitectura ResNet

La idea, muy resumida, se basa en aumentar el número de capas introduciendo una conexión residual (con una capa identidad). Esta capa pasa a la siguiente directamente, mejorando el proceso de aprendizaje.

CNN tradicional VS CNN con conexión residual

Realizaremos el mismo experimento que en las partes anteriores. Obviaremos los puntos en los que importamos el dataset de CIFAR-100, la configuración básica del entorno del experimento y la importación de las librerías de python, pues son exactamente igual.

Entrenando la arquitectura ResNet

Keras tiene a nuestra disposición ésta arquitectura, pero tiene el problema que, por defecto, el tamaño de las imágenes debe ser mayor a 187 píxeles, por lo que definiremos una arquitectura más pequeña.

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

Compilamos como hasta ahora...

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'])  

Una vez hecho esto, vamos a ver un resumen del modelo creado.

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  
__________________________________________________________________________________________________  

Recordemos que la arquitectura VGG-16 tenía aproximadamente 34 millones de parámetros a entrenar. Esto quiere decir que hemos aumentado la profundidad pero hemos reducido el número de parámetros a entrenar.

Bien, dicho esto, pasamos a entrenar el modelo.

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

Veamos las métricas obtenidas para el entrenamiento y validación gráficamente.

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()  

Accuracy

Loss

El entrenamiento ha dado muy buenos resultados y ha generalizado bien (0,0119).

Matriz de confusión

Pasemos ahora a ver la matriz de confusión y las métricas de Accuracy, Recall y F1-score.

Vamos a hacer una predicción sobre el dataset de validación y, a partir de ésta, generamos la matriz de confusión y mostramos las métricas mencionadas anteriormente.

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()  

Matriz de confusión

Y por último, mostramos las métricas

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  

Curva ROC (tasas de verdaderos positivos y falsos positivos)

Vamos a codificar la curva ROC.

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()  

El resultado para tres clases se muestra en los siguientes gráficos.

Curva ROC para 3 clases

Zoom de la Curva ROC para 10 clases

Salvaremos los datos del histórico de entrenamiento para compararlos con otros modelos. Además, vamos a salvar el modelo con los pesos entrenados para usarlos en el futuro.

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

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

A continuación, vamos a comparar las métricas con los modelos anteriores (obviaremos el código que carga los datos de dichos modelos).

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'])  

Simple NN Vs CNN accuracy

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'])  

Simple NN Vs CNN loss

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'])  

Simple NN Vs CNN MSE

Conclusión sobre el experimento

Como se puede ver, la arquitectura marca un punto de inflexión. No sólo porque sea de los mejores resultados que las anteriores arquitecturas, sino también en los tiempos de entrenamiento, ya que permite aumentar las capas con un tiempo aceptable; y también en el número de parámetros, que se ha reducido considerablemente respecto a la arquitectura VGG.

En el siguiente artículo, presentaremos la arquitectura: DenseNet. ¡Síguenos en Twitter para estar al día de los próximos posts de esta serie y mucho más!

Jesús Utrera Burgal
Autor

Jesús Utrera Burgal

Desarrollador .NET por más de 10 años, en los últimos años me he adentrado en el mundo de Machine Learning, concretamente en el área de Supervised Deep Learning.