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Deep Learning básico con Keras (Parte 5): DenseNet

Publicado por Jesús Utrera Burgal el

Deep LearningKerasDenseNet

En este artículo vamos a mostrar la arquitectura DenseNet. Ésta fue introducida en el año 2016, consiguiendo en 2017 el premio CVPR 2017 Best Paper Award. El siguiente enlace nos lleva al paper: https://arxiv.org/abs/1608.06993, pero aconsejo el artículo del blog Towards Data Science, pues explica muy bien su funcionamiento.

La idea, muy resumida, se basa en concatenar cada salida de las capas previas hacia las siguientes:

Diagrama de arquitectura DenseNet

La imagen anterior representa un bloque denso de 5 capas con una tasa de crecimiento k = 4. Cada capa toma como entrada todos los valores de características anteriores.

Red neuronal de tres bloques densos

Como se puede ver en la imagen anterior, donde se representa una red de tres bloques densos, las capas entre dos bloques adyacentes hace referencia a una capa de transición cambiando el tamaño del mapa de características mediante convolución y pooling.

Entrenando la arquitectura DenseNet

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

from keras.applications import densenet  
from keras.applications import imagenet_utils as imut

def CustomDenseNet(blocks,  
             include_top=True,
             input_tensor=None,
             weights=None,
             input_shape=None,
             pooling=None,
             classes=1000):

    # Determine proper input shape
    input_shape = imut._obtain_input_shape(input_shape,
                                      default_size=224,
                                      min_size=32,
                                      data_format=K.image_data_format(),
                                      require_flatten=include_top,
                                      weights=weights)

    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

    bn_axis = 3 if K.image_data_format() == 'channels_last' else 1

    x = ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
    x = Conv2D(64, 7, strides=2, use_bias=False, name='conv1/conv')(x)
    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                           name='conv1/bn')(x)
    x = Activation('relu', name='conv1/relu')(x)
    x = ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
    x = MaxPooling2D(3, strides=2, name='pool1')(x)

    x = densenet.dense_block(x, blocks[0], name='conv2')
    x = densenet.transition_block(x, 0.5, name='pool2')
    x = densenet.dense_block(x, blocks[1], name='conv3')
    x = densenet.transition_block(x, 0.5, name='pool3')
    x = densenet.dense_block(x, blocks[2], name='conv4')
    x = densenet.transition_block(x, 0.5, name='pool4')
    x = densenet.dense_block(x, blocks[3], name='conv5')

    x = BatchNormalization(axis=bn_axis, epsilon=1.001e-5,
                           name='bn')(x)

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

    # Ensure that the model takes into account
    # any potential predecessors of `input_tensor`.
    if input_tensor is not None:
        inputs = imut.get_source_inputs(input_tensor)
    else:
        inputs = img_input

    # Create model.
    if blocks == [6, 12, 24, 16]:
        model = Model(inputs, x, name='densenet121')
    elif blocks == [6, 12, 32, 32]:
        model = Model(inputs, x, name='densenet169')
    elif blocks == [6, 12, 48, 32]:
        model = Model(inputs, x, name='densenet201')
    else:
        model = Model(inputs, x, name='densenet')

    # Load weights.
    if weights == 'imagenet':
        if include_top:
            if blocks == [6, 12, 24, 16]:
                weights_path = get_file(
                    'densenet121_weights_tf_dim_ordering_tf_kernels.h5',
                    DENSENET121_WEIGHT_PATH,
                    cache_subdir='models',
                    file_hash='0962ca643bae20f9b6771cb844dca3b0')
            elif blocks == [6, 12, 32, 32]:
                weights_path = get_file(
                    'densenet169_weights_tf_dim_ordering_tf_kernels.h5',
                    DENSENET169_WEIGHT_PATH,
                    cache_subdir='models',
                    file_hash='bcf9965cf5064a5f9eb6d7dc69386f43')
            elif blocks == [6, 12, 48, 32]:
                weights_path = get_file(
                    'densenet201_weights_tf_dim_ordering_tf_kernels.h5',
                    DENSENET201_WEIGHT_PATH,
                    cache_subdir='models',
                    file_hash='7bb75edd58cb43163be7e0005fbe95ef')
        else:
            if blocks == [6, 12, 24, 16]:
                weights_path = get_file(
                    'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5',
                    DENSENET121_WEIGHT_PATH_NO_TOP,
                    cache_subdir='models',
                    file_hash='4912a53fbd2a69346e7f2c0b5ec8c6d3')
            elif blocks == [6, 12, 32, 32]:
                weights_path = get_file(
                    'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5',
                    DENSENET169_WEIGHT_PATH_NO_TOP,
                    cache_subdir='models',
                    file_hash='50662582284e4cf834ce40ab4dfa58c6')
            elif blocks == [6, 12, 48, 32]:
                weights_path = get_file(
                    'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5',
                    DENSENET201_WEIGHT_PATH_NO_TOP,
                    cache_subdir='models',
                    file_hash='1c2de60ee40562448dbac34a0737e798')
        model.load_weights(weights_path)
    elif weights is not None:
        model.load_weights(weights)

    return model

def create_densenet():  
  base_model = CustomDenseNet([6, 12, 16, 8], include_top=False, weights=None, input_tensor=None, input_shape=(32,32,3), pooling=None, classes=100)
  x = base_model.output

  x = GlobalAveragePooling2D(name='avg_pool')(x)
  x = Dense(500)(x)
  x = Activation('relu')(x)
  x = Dropout(0.5)(x)
  predictions = Dense(100, activation='softmax')(x)
  model = Model(inputs=base_model.input, outputs=predictions)
  return model

Compilamos como hasta ahora...

custom_dense_model = create_densenet()  
custom_dense_model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['acc', 'mse'])  

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

custom_dense_model.summary()  
__________________________________________________________________________________________________  
Layer (type)                    Output Shape         Param #     Connected to  
==================================================================================================
input_1 (InputLayer)            (None, 32, 32, 3)    0  
__________________________________________________________________________________________________  
zero_padding2d_1 (ZeroPadding2D (None, 38, 38, 3)    0           input_1[0][0]  
__________________________________________________________________________________________________  
conv1/conv (Conv2D)             (None, 16, 16, 64)   9408        zero_padding2d_1[0][0]  
__________________________________________________________________________________________________  
conv1/bn (BatchNormalization)   (None, 16, 16, 64)   256         conv1/conv[0][0]  
__________________________________________________________________________________________________  
conv1/relu (Activation)         (None, 16, 16, 64)   0           conv1/bn[0][0]  
__________________________________________________________________________________________________  
zero_padding2d_2 (ZeroPadding2D (None, 18, 18, 64)   0           conv1/relu[0][0]  
__________________________________________________________________________________________________  
pool1 (MaxPooling2D)            (None, 8, 8, 64)     0           zero_padding2d_2[0][0]  
__________________________________________________________________________________________________  
conv2_block1_0_bn (BatchNormali (None, 8, 8, 64)     256         pool1[0][0]  
__________________________________________________________________________________________________  
conv2_block1_0_relu (Activation (None, 8, 8, 64)     0           conv2_block1_0_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block1_1_conv (Conv2D)    (None, 8, 8, 128)    8192        conv2_block1_0_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block1_1_bn (BatchNormali (None, 8, 8, 128)    512         conv2_block1_1_conv[0][0]  
__________________________________________________________________________________________________  
conv2_block1_1_relu (Activation (None, 8, 8, 128)    0           conv2_block1_1_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block1_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv2_block1_1_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block1_concat (Concatenat (None, 8, 8, 96)     0           pool1[0][0]  
                                                                 conv2_block1_2_conv[0][0]        
__________________________________________________________________________________________________  
conv2_block2_0_bn (BatchNormali (None, 8, 8, 96)     384         conv2_block1_concat[0][0]  
__________________________________________________________________________________________________  
conv2_block2_0_relu (Activation (None, 8, 8, 96)     0           conv2_block2_0_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block2_1_conv (Conv2D)    (None, 8, 8, 128)    12288       conv2_block2_0_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block2_1_bn (BatchNormali (None, 8, 8, 128)    512         conv2_block2_1_conv[0][0]  
__________________________________________________________________________________________________  
conv2_block2_1_relu (Activation (None, 8, 8, 128)    0           conv2_block2_1_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block2_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv2_block2_1_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block2_concat (Concatenat (None, 8, 8, 128)    0           conv2_block1_concat[0][0]  
                                                                 conv2_block2_2_conv[0][0]        
__________________________________________________________________________________________________  
conv2_block3_0_bn (BatchNormali (None, 8, 8, 128)    512         conv2_block2_concat[0][0]  
__________________________________________________________________________________________________  
conv2_block3_0_relu (Activation (None, 8, 8, 128)    0           conv2_block3_0_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block3_1_conv (Conv2D)    (None, 8, 8, 128)    16384       conv2_block3_0_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block3_1_bn (BatchNormali (None, 8, 8, 128)    512         conv2_block3_1_conv[0][0]  
__________________________________________________________________________________________________  
conv2_block3_1_relu (Activation (None, 8, 8, 128)    0           conv2_block3_1_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block3_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv2_block3_1_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block3_concat (Concatenat (None, 8, 8, 160)    0           conv2_block2_concat[0][0]  
                                                                 conv2_block3_2_conv[0][0]        
__________________________________________________________________________________________________  
conv2_block4_0_bn (BatchNormali (None, 8, 8, 160)    640         conv2_block3_concat[0][0]  
__________________________________________________________________________________________________  
conv2_block4_0_relu (Activation (None, 8, 8, 160)    0           conv2_block4_0_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block4_1_conv (Conv2D)    (None, 8, 8, 128)    20480       conv2_block4_0_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block4_1_bn (BatchNormali (None, 8, 8, 128)    512         conv2_block4_1_conv[0][0]  
__________________________________________________________________________________________________  
conv2_block4_1_relu (Activation (None, 8, 8, 128)    0           conv2_block4_1_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block4_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv2_block4_1_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block4_concat (Concatenat (None, 8, 8, 192)    0           conv2_block3_concat[0][0]  
                                                                 conv2_block4_2_conv[0][0]        
__________________________________________________________________________________________________  
conv2_block5_0_bn (BatchNormali (None, 8, 8, 192)    768         conv2_block4_concat[0][0]  
__________________________________________________________________________________________________  
conv2_block5_0_relu (Activation (None, 8, 8, 192)    0           conv2_block5_0_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block5_1_conv (Conv2D)    (None, 8, 8, 128)    24576       conv2_block5_0_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block5_1_bn (BatchNormali (None, 8, 8, 128)    512         conv2_block5_1_conv[0][0]  
__________________________________________________________________________________________________  
conv2_block5_1_relu (Activation (None, 8, 8, 128)    0           conv2_block5_1_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block5_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv2_block5_1_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block5_concat (Concatenat (None, 8, 8, 224)    0           conv2_block4_concat[0][0]  
                                                                 conv2_block5_2_conv[0][0]        
__________________________________________________________________________________________________  
conv2_block6_0_bn (BatchNormali (None, 8, 8, 224)    896         conv2_block5_concat[0][0]  
__________________________________________________________________________________________________  
conv2_block6_0_relu (Activation (None, 8, 8, 224)    0           conv2_block6_0_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block6_1_conv (Conv2D)    (None, 8, 8, 128)    28672       conv2_block6_0_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block6_1_bn (BatchNormali (None, 8, 8, 128)    512         conv2_block6_1_conv[0][0]  
__________________________________________________________________________________________________  
conv2_block6_1_relu (Activation (None, 8, 8, 128)    0           conv2_block6_1_bn[0][0]  
__________________________________________________________________________________________________  
conv2_block6_2_conv (Conv2D)    (None, 8, 8, 32)     36864       conv2_block6_1_relu[0][0]  
__________________________________________________________________________________________________  
conv2_block6_concat (Concatenat (None, 8, 8, 256)    0           conv2_block5_concat[0][0]  
                                                                 conv2_block6_2_conv[0][0]        
__________________________________________________________________________________________________  
pool2_bn (BatchNormalization)   (None, 8, 8, 256)    1024        conv2_block6_concat[0][0]  
__________________________________________________________________________________________________  
pool2_relu (Activation)         (None, 8, 8, 256)    0           pool2_bn[0][0]  
__________________________________________________________________________________________________  
pool2_conv (Conv2D)             (None, 8, 8, 128)    32768       pool2_relu[0][0]  
__________________________________________________________________________________________________  
pool2_pool (AveragePooling2D)   (None, 4, 4, 128)    0           pool2_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block1_0_bn (BatchNormali (None, 4, 4, 128)    512         pool2_pool[0][0]  
__________________________________________________________________________________________________  
conv3_block1_0_relu (Activation (None, 4, 4, 128)    0           conv3_block1_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block1_1_conv (Conv2D)    (None, 4, 4, 128)    16384       conv3_block1_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block1_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block1_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block1_1_relu (Activation (None, 4, 4, 128)    0           conv3_block1_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block1_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv3_block1_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block1_concat (Concatenat (None, 4, 4, 160)    0           pool2_pool[0][0]  
                                                                 conv3_block1_2_conv[0][0]        
__________________________________________________________________________________________________  
conv3_block2_0_bn (BatchNormali (None, 4, 4, 160)    640         conv3_block1_concat[0][0]  
__________________________________________________________________________________________________  
conv3_block2_0_relu (Activation (None, 4, 4, 160)    0           conv3_block2_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block2_1_conv (Conv2D)    (None, 4, 4, 128)    20480       conv3_block2_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block2_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block2_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block2_1_relu (Activation (None, 4, 4, 128)    0           conv3_block2_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block2_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv3_block2_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block2_concat (Concatenat (None, 4, 4, 192)    0           conv3_block1_concat[0][0]  
                                                                 conv3_block2_2_conv[0][0]        
__________________________________________________________________________________________________  
conv3_block3_0_bn (BatchNormali (None, 4, 4, 192)    768         conv3_block2_concat[0][0]  
__________________________________________________________________________________________________  
conv3_block3_0_relu (Activation (None, 4, 4, 192)    0           conv3_block3_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block3_1_conv (Conv2D)    (None, 4, 4, 128)    24576       conv3_block3_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block3_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block3_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block3_1_relu (Activation (None, 4, 4, 128)    0           conv3_block3_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block3_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv3_block3_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block3_concat (Concatenat (None, 4, 4, 224)    0           conv3_block2_concat[0][0]  
                                                                 conv3_block3_2_conv[0][0]        
__________________________________________________________________________________________________  
conv3_block4_0_bn (BatchNormali (None, 4, 4, 224)    896         conv3_block3_concat[0][0]  
__________________________________________________________________________________________________  
conv3_block4_0_relu (Activation (None, 4, 4, 224)    0           conv3_block4_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block4_1_conv (Conv2D)    (None, 4, 4, 128)    28672       conv3_block4_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block4_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block4_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block4_1_relu (Activation (None, 4, 4, 128)    0           conv3_block4_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block4_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv3_block4_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block4_concat (Concatenat (None, 4, 4, 256)    0           conv3_block3_concat[0][0]  
                                                                 conv3_block4_2_conv[0][0]        
__________________________________________________________________________________________________  
conv3_block5_0_bn (BatchNormali (None, 4, 4, 256)    1024        conv3_block4_concat[0][0]  
__________________________________________________________________________________________________  
conv3_block5_0_relu (Activation (None, 4, 4, 256)    0           conv3_block5_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block5_1_conv (Conv2D)    (None, 4, 4, 128)    32768       conv3_block5_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block5_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block5_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block5_1_relu (Activation (None, 4, 4, 128)    0           conv3_block5_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block5_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv3_block5_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block5_concat (Concatenat (None, 4, 4, 288)    0           conv3_block4_concat[0][0]  
                                                                 conv3_block5_2_conv[0][0]        
__________________________________________________________________________________________________  
conv3_block6_0_bn (BatchNormali (None, 4, 4, 288)    1152        conv3_block5_concat[0][0]  
__________________________________________________________________________________________________  
conv3_block6_0_relu (Activation (None, 4, 4, 288)    0           conv3_block6_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block6_1_conv (Conv2D)    (None, 4, 4, 128)    36864       conv3_block6_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block6_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block6_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block6_1_relu (Activation (None, 4, 4, 128)    0           conv3_block6_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block6_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv3_block6_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block6_concat (Concatenat (None, 4, 4, 320)    0           conv3_block5_concat[0][0]  
                                                                 conv3_block6_2_conv[0][0]        
__________________________________________________________________________________________________  
conv3_block7_0_bn (BatchNormali (None, 4, 4, 320)    1280        conv3_block6_concat[0][0]  
__________________________________________________________________________________________________  
conv3_block7_0_relu (Activation (None, 4, 4, 320)    0           conv3_block7_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block7_1_conv (Conv2D)    (None, 4, 4, 128)    40960       conv3_block7_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block7_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block7_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block7_1_relu (Activation (None, 4, 4, 128)    0           conv3_block7_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block7_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv3_block7_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block7_concat (Concatenat (None, 4, 4, 352)    0           conv3_block6_concat[0][0]  
                                                                 conv3_block7_2_conv[0][0]        
__________________________________________________________________________________________________  
conv3_block8_0_bn (BatchNormali (None, 4, 4, 352)    1408        conv3_block7_concat[0][0]  
__________________________________________________________________________________________________  
conv3_block8_0_relu (Activation (None, 4, 4, 352)    0           conv3_block8_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block8_1_conv (Conv2D)    (None, 4, 4, 128)    45056       conv3_block8_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block8_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block8_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block8_1_relu (Activation (None, 4, 4, 128)    0           conv3_block8_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block8_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv3_block8_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block8_concat (Concatenat (None, 4, 4, 384)    0           conv3_block7_concat[0][0]  
                                                                 conv3_block8_2_conv[0][0]        
__________________________________________________________________________________________________  
conv3_block9_0_bn (BatchNormali (None, 4, 4, 384)    1536        conv3_block8_concat[0][0]  
__________________________________________________________________________________________________  
conv3_block9_0_relu (Activation (None, 4, 4, 384)    0           conv3_block9_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block9_1_conv (Conv2D)    (None, 4, 4, 128)    49152       conv3_block9_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block9_1_bn (BatchNormali (None, 4, 4, 128)    512         conv3_block9_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block9_1_relu (Activation (None, 4, 4, 128)    0           conv3_block9_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block9_2_conv (Conv2D)    (None, 4, 4, 32)     36864       conv3_block9_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block9_concat (Concatenat (None, 4, 4, 416)    0           conv3_block8_concat[0][0]  
                                                                 conv3_block9_2_conv[0][0]        
__________________________________________________________________________________________________  
conv3_block10_0_bn (BatchNormal (None, 4, 4, 416)    1664        conv3_block9_concat[0][0]  
__________________________________________________________________________________________________  
conv3_block10_0_relu (Activatio (None, 4, 4, 416)    0           conv3_block10_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block10_1_conv (Conv2D)   (None, 4, 4, 128)    53248       conv3_block10_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block10_1_bn (BatchNormal (None, 4, 4, 128)    512         conv3_block10_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block10_1_relu (Activatio (None, 4, 4, 128)    0           conv3_block10_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block10_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv3_block10_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block10_concat (Concatena (None, 4, 4, 448)    0           conv3_block9_concat[0][0]  
                                                                 conv3_block10_2_conv[0][0]       
__________________________________________________________________________________________________  
conv3_block11_0_bn (BatchNormal (None, 4, 4, 448)    1792        conv3_block10_concat[0][0]  
__________________________________________________________________________________________________  
conv3_block11_0_relu (Activatio (None, 4, 4, 448)    0           conv3_block11_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block11_1_conv (Conv2D)   (None, 4, 4, 128)    57344       conv3_block11_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block11_1_bn (BatchNormal (None, 4, 4, 128)    512         conv3_block11_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block11_1_relu (Activatio (None, 4, 4, 128)    0           conv3_block11_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block11_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv3_block11_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block11_concat (Concatena (None, 4, 4, 480)    0           conv3_block10_concat[0][0]  
                                                                 conv3_block11_2_conv[0][0]       
__________________________________________________________________________________________________  
conv3_block12_0_bn (BatchNormal (None, 4, 4, 480)    1920        conv3_block11_concat[0][0]  
__________________________________________________________________________________________________  
conv3_block12_0_relu (Activatio (None, 4, 4, 480)    0           conv3_block12_0_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block12_1_conv (Conv2D)   (None, 4, 4, 128)    61440       conv3_block12_0_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block12_1_bn (BatchNormal (None, 4, 4, 128)    512         conv3_block12_1_conv[0][0]  
__________________________________________________________________________________________________  
conv3_block12_1_relu (Activatio (None, 4, 4, 128)    0           conv3_block12_1_bn[0][0]  
__________________________________________________________________________________________________  
conv3_block12_2_conv (Conv2D)   (None, 4, 4, 32)     36864       conv3_block12_1_relu[0][0]  
__________________________________________________________________________________________________  
conv3_block12_concat (Concatena (None, 4, 4, 512)    0           conv3_block11_concat[0][0]  
                                                                 conv3_block12_2_conv[0][0]       
__________________________________________________________________________________________________  
pool3_bn (BatchNormalization)   (None, 4, 4, 512)    2048        conv3_block12_concat[0][0]  
__________________________________________________________________________________________________  
pool3_relu (Activation)         (None, 4, 4, 512)    0           pool3_bn[0][0]  
__________________________________________________________________________________________________  
pool3_conv (Conv2D)             (None, 4, 4, 256)    131072      pool3_relu[0][0]  
__________________________________________________________________________________________________  
pool3_pool (AveragePooling2D)   (None, 2, 2, 256)    0           pool3_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block1_0_bn (BatchNormali (None, 2, 2, 256)    1024        pool3_pool[0][0]  
__________________________________________________________________________________________________  
conv4_block1_0_relu (Activation (None, 2, 2, 256)    0           conv4_block1_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block1_1_conv (Conv2D)    (None, 2, 2, 128)    32768       conv4_block1_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block1_1_bn (BatchNormali (None, 2, 2, 128)    512         conv4_block1_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block1_1_relu (Activation (None, 2, 2, 128)    0           conv4_block1_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block1_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv4_block1_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block1_concat (Concatenat (None, 2, 2, 288)    0           pool3_pool[0][0]  
                                                                 conv4_block1_2_conv[0][0]        
__________________________________________________________________________________________________  
conv4_block2_0_bn (BatchNormali (None, 2, 2, 288)    1152        conv4_block1_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block2_0_relu (Activation (None, 2, 2, 288)    0           conv4_block2_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block2_1_conv (Conv2D)    (None, 2, 2, 128)    36864       conv4_block2_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block2_1_bn (BatchNormali (None, 2, 2, 128)    512         conv4_block2_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block2_1_relu (Activation (None, 2, 2, 128)    0           conv4_block2_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block2_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv4_block2_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block2_concat (Concatenat (None, 2, 2, 320)    0           conv4_block1_concat[0][0]  
                                                                 conv4_block2_2_conv[0][0]        
__________________________________________________________________________________________________  
conv4_block3_0_bn (BatchNormali (None, 2, 2, 320)    1280        conv4_block2_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block3_0_relu (Activation (None, 2, 2, 320)    0           conv4_block3_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block3_1_conv (Conv2D)    (None, 2, 2, 128)    40960       conv4_block3_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block3_1_bn (BatchNormali (None, 2, 2, 128)    512         conv4_block3_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block3_1_relu (Activation (None, 2, 2, 128)    0           conv4_block3_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block3_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv4_block3_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block3_concat (Concatenat (None, 2, 2, 352)    0           conv4_block2_concat[0][0]  
                                                                 conv4_block3_2_conv[0][0]        
__________________________________________________________________________________________________  
conv4_block4_0_bn (BatchNormali (None, 2, 2, 352)    1408        conv4_block3_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block4_0_relu (Activation (None, 2, 2, 352)    0           conv4_block4_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block4_1_conv (Conv2D)    (None, 2, 2, 128)    45056       conv4_block4_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block4_1_bn (BatchNormali (None, 2, 2, 128)    512         conv4_block4_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block4_1_relu (Activation (None, 2, 2, 128)    0           conv4_block4_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block4_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv4_block4_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block4_concat (Concatenat (None, 2, 2, 384)    0           conv4_block3_concat[0][0]  
                                                                 conv4_block4_2_conv[0][0]        
__________________________________________________________________________________________________  
conv4_block5_0_bn (BatchNormali (None, 2, 2, 384)    1536        conv4_block4_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block5_0_relu (Activation (None, 2, 2, 384)    0           conv4_block5_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block5_1_conv (Conv2D)    (None, 2, 2, 128)    49152       conv4_block5_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block5_1_bn (BatchNormali (None, 2, 2, 128)    512         conv4_block5_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block5_1_relu (Activation (None, 2, 2, 128)    0           conv4_block5_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block5_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv4_block5_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block5_concat (Concatenat (None, 2, 2, 416)    0           conv4_block4_concat[0][0]  
                                                                 conv4_block5_2_conv[0][0]        
__________________________________________________________________________________________________  
conv4_block6_0_bn (BatchNormali (None, 2, 2, 416)    1664        conv4_block5_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block6_0_relu (Activation (None, 2, 2, 416)    0           conv4_block6_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block6_1_conv (Conv2D)    (None, 2, 2, 128)    53248       conv4_block6_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block6_1_bn (BatchNormali (None, 2, 2, 128)    512         conv4_block6_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block6_1_relu (Activation (None, 2, 2, 128)    0           conv4_block6_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block6_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv4_block6_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block6_concat (Concatenat (None, 2, 2, 448)    0           conv4_block5_concat[0][0]  
                                                                 conv4_block6_2_conv[0][0]        
__________________________________________________________________________________________________  
conv4_block7_0_bn (BatchNormali (None, 2, 2, 448)    1792        conv4_block6_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block7_0_relu (Activation (None, 2, 2, 448)    0           conv4_block7_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block7_1_conv (Conv2D)    (None, 2, 2, 128)    57344       conv4_block7_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block7_1_bn (BatchNormali (None, 2, 2, 128)    512         conv4_block7_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block7_1_relu (Activation (None, 2, 2, 128)    0           conv4_block7_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block7_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv4_block7_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block7_concat (Concatenat (None, 2, 2, 480)    0           conv4_block6_concat[0][0]  
                                                                 conv4_block7_2_conv[0][0]        
__________________________________________________________________________________________________  
conv4_block8_0_bn (BatchNormali (None, 2, 2, 480)    1920        conv4_block7_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block8_0_relu (Activation (None, 2, 2, 480)    0           conv4_block8_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block8_1_conv (Conv2D)    (None, 2, 2, 128)    61440       conv4_block8_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block8_1_bn (BatchNormali (None, 2, 2, 128)    512         conv4_block8_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block8_1_relu (Activation (None, 2, 2, 128)    0           conv4_block8_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block8_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv4_block8_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block8_concat (Concatenat (None, 2, 2, 512)    0           conv4_block7_concat[0][0]  
                                                                 conv4_block8_2_conv[0][0]        
__________________________________________________________________________________________________  
conv4_block9_0_bn (BatchNormali (None, 2, 2, 512)    2048        conv4_block8_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block9_0_relu (Activation (None, 2, 2, 512)    0           conv4_block9_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block9_1_conv (Conv2D)    (None, 2, 2, 128)    65536       conv4_block9_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block9_1_bn (BatchNormali (None, 2, 2, 128)    512         conv4_block9_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block9_1_relu (Activation (None, 2, 2, 128)    0           conv4_block9_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block9_2_conv (Conv2D)    (None, 2, 2, 32)     36864       conv4_block9_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block9_concat (Concatenat (None, 2, 2, 544)    0           conv4_block8_concat[0][0]  
                                                                 conv4_block9_2_conv[0][0]        
__________________________________________________________________________________________________  
conv4_block10_0_bn (BatchNormal (None, 2, 2, 544)    2176        conv4_block9_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block10_0_relu (Activatio (None, 2, 2, 544)    0           conv4_block10_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block10_1_conv (Conv2D)   (None, 2, 2, 128)    69632       conv4_block10_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block10_1_bn (BatchNormal (None, 2, 2, 128)    512         conv4_block10_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block10_1_relu (Activatio (None, 2, 2, 128)    0           conv4_block10_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block10_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv4_block10_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block10_concat (Concatena (None, 2, 2, 576)    0           conv4_block9_concat[0][0]  
                                                                 conv4_block10_2_conv[0][0]       
__________________________________________________________________________________________________  
conv4_block11_0_bn (BatchNormal (None, 2, 2, 576)    2304        conv4_block10_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block11_0_relu (Activatio (None, 2, 2, 576)    0           conv4_block11_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block11_1_conv (Conv2D)   (None, 2, 2, 128)    73728       conv4_block11_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block11_1_bn (BatchNormal (None, 2, 2, 128)    512         conv4_block11_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block11_1_relu (Activatio (None, 2, 2, 128)    0           conv4_block11_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block11_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv4_block11_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block11_concat (Concatena (None, 2, 2, 608)    0           conv4_block10_concat[0][0]  
                                                                 conv4_block11_2_conv[0][0]       
__________________________________________________________________________________________________  
conv4_block12_0_bn (BatchNormal (None, 2, 2, 608)    2432        conv4_block11_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block12_0_relu (Activatio (None, 2, 2, 608)    0           conv4_block12_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block12_1_conv (Conv2D)   (None, 2, 2, 128)    77824       conv4_block12_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block12_1_bn (BatchNormal (None, 2, 2, 128)    512         conv4_block12_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block12_1_relu (Activatio (None, 2, 2, 128)    0           conv4_block12_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block12_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv4_block12_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block12_concat (Concatena (None, 2, 2, 640)    0           conv4_block11_concat[0][0]  
                                                                 conv4_block12_2_conv[0][0]       
__________________________________________________________________________________________________  
conv4_block13_0_bn (BatchNormal (None, 2, 2, 640)    2560        conv4_block12_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block13_0_relu (Activatio (None, 2, 2, 640)    0           conv4_block13_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block13_1_conv (Conv2D)   (None, 2, 2, 128)    81920       conv4_block13_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block13_1_bn (BatchNormal (None, 2, 2, 128)    512         conv4_block13_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block13_1_relu (Activatio (None, 2, 2, 128)    0           conv4_block13_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block13_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv4_block13_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block13_concat (Concatena (None, 2, 2, 672)    0           conv4_block12_concat[0][0]  
                                                                 conv4_block13_2_conv[0][0]       
__________________________________________________________________________________________________  
conv4_block14_0_bn (BatchNormal (None, 2, 2, 672)    2688        conv4_block13_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block14_0_relu (Activatio (None, 2, 2, 672)    0           conv4_block14_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block14_1_conv (Conv2D)   (None, 2, 2, 128)    86016       conv4_block14_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block14_1_bn (BatchNormal (None, 2, 2, 128)    512         conv4_block14_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block14_1_relu (Activatio (None, 2, 2, 128)    0           conv4_block14_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block14_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv4_block14_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block14_concat (Concatena (None, 2, 2, 704)    0           conv4_block13_concat[0][0]  
                                                                 conv4_block14_2_conv[0][0]       
__________________________________________________________________________________________________  
conv4_block15_0_bn (BatchNormal (None, 2, 2, 704)    2816        conv4_block14_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block15_0_relu (Activatio (None, 2, 2, 704)    0           conv4_block15_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block15_1_conv (Conv2D)   (None, 2, 2, 128)    90112       conv4_block15_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block15_1_bn (BatchNormal (None, 2, 2, 128)    512         conv4_block15_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block15_1_relu (Activatio (None, 2, 2, 128)    0           conv4_block15_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block15_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv4_block15_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block15_concat (Concatena (None, 2, 2, 736)    0           conv4_block14_concat[0][0]  
                                                                 conv4_block15_2_conv[0][0]       
__________________________________________________________________________________________________  
conv4_block16_0_bn (BatchNormal (None, 2, 2, 736)    2944        conv4_block15_concat[0][0]  
__________________________________________________________________________________________________  
conv4_block16_0_relu (Activatio (None, 2, 2, 736)    0           conv4_block16_0_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block16_1_conv (Conv2D)   (None, 2, 2, 128)    94208       conv4_block16_0_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block16_1_bn (BatchNormal (None, 2, 2, 128)    512         conv4_block16_1_conv[0][0]  
__________________________________________________________________________________________________  
conv4_block16_1_relu (Activatio (None, 2, 2, 128)    0           conv4_block16_1_bn[0][0]  
__________________________________________________________________________________________________  
conv4_block16_2_conv (Conv2D)   (None, 2, 2, 32)     36864       conv4_block16_1_relu[0][0]  
__________________________________________________________________________________________________  
conv4_block16_concat (Concatena (None, 2, 2, 768)    0           conv4_block15_concat[0][0]  
                                                                 conv4_block16_2_conv[0][0]       
__________________________________________________________________________________________________  
pool4_bn (BatchNormalization)   (None, 2, 2, 768)    3072        conv4_block16_concat[0][0]  
__________________________________________________________________________________________________  
pool4_relu (Activation)         (None, 2, 2, 768)    0           pool4_bn[0][0]  
__________________________________________________________________________________________________  
pool4_conv (Conv2D)             (None, 2, 2, 384)    294912      pool4_relu[0][0]  
__________________________________________________________________________________________________  
pool4_pool (AveragePooling2D)   (None, 1, 1, 384)    0           pool4_conv[0][0]  
__________________________________________________________________________________________________  
conv5_block1_0_bn (BatchNormali (None, 1, 1, 384)    1536        pool4_pool[0][0]  
__________________________________________________________________________________________________  
conv5_block1_0_relu (Activation (None, 1, 1, 384)    0           conv5_block1_0_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block1_1_conv (Conv2D)    (None, 1, 1, 128)    49152       conv5_block1_0_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block1_1_bn (BatchNormali (None, 1, 1, 128)    512         conv5_block1_1_conv[0][0]  
__________________________________________________________________________________________________  
conv5_block1_1_relu (Activation (None, 1, 1, 128)    0           conv5_block1_1_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block1_2_conv (Conv2D)    (None, 1, 1, 32)     36864       conv5_block1_1_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block1_concat (Concatenat (None, 1, 1, 416)    0           pool4_pool[0][0]  
                                                                 conv5_block1_2_conv[0][0]        
__________________________________________________________________________________________________  
conv5_block2_0_bn (BatchNormali (None, 1, 1, 416)    1664        conv5_block1_concat[0][0]  
__________________________________________________________________________________________________  
conv5_block2_0_relu (Activation (None, 1, 1, 416)    0           conv5_block2_0_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block2_1_conv (Conv2D)    (None, 1, 1, 128)    53248       conv5_block2_0_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block2_1_bn (BatchNormali (None, 1, 1, 128)    512         conv5_block2_1_conv[0][0]  
__________________________________________________________________________________________________  
conv5_block2_1_relu (Activation (None, 1, 1, 128)    0           conv5_block2_1_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block2_2_conv (Conv2D)    (None, 1, 1, 32)     36864       conv5_block2_1_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block2_concat (Concatenat (None, 1, 1, 448)    0           conv5_block1_concat[0][0]  
                                                                 conv5_block2_2_conv[0][0]        
__________________________________________________________________________________________________  
conv5_block3_0_bn (BatchNormali (None, 1, 1, 448)    1792        conv5_block2_concat[0][0]  
__________________________________________________________________________________________________  
conv5_block3_0_relu (Activation (None, 1, 1, 448)    0           conv5_block3_0_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block3_1_conv (Conv2D)    (None, 1, 1, 128)    57344       conv5_block3_0_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block3_1_bn (BatchNormali (None, 1, 1, 128)    512         conv5_block3_1_conv[0][0]  
__________________________________________________________________________________________________  
conv5_block3_1_relu (Activation (None, 1, 1, 128)    0           conv5_block3_1_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block3_2_conv (Conv2D)    (None, 1, 1, 32)     36864       conv5_block3_1_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block3_concat (Concatenat (None, 1, 1, 480)    0           conv5_block2_concat[0][0]  
                                                                 conv5_block3_2_conv[0][0]        
__________________________________________________________________________________________________  
conv5_block4_0_bn (BatchNormali (None, 1, 1, 480)    1920        conv5_block3_concat[0][0]  
__________________________________________________________________________________________________  
conv5_block4_0_relu (Activation (None, 1, 1, 480)    0           conv5_block4_0_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block4_1_conv (Conv2D)    (None, 1, 1, 128)    61440       conv5_block4_0_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block4_1_bn (BatchNormali (None, 1, 1, 128)    512         conv5_block4_1_conv[0][0]  
__________________________________________________________________________________________________  
conv5_block4_1_relu (Activation (None, 1, 1, 128)    0           conv5_block4_1_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block4_2_conv (Conv2D)    (None, 1, 1, 32)     36864       conv5_block4_1_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block4_concat (Concatenat (None, 1, 1, 512)    0           conv5_block3_concat[0][0]  
                                                                 conv5_block4_2_conv[0][0]        
__________________________________________________________________________________________________  
conv5_block5_0_bn (BatchNormali (None, 1, 1, 512)    2048        conv5_block4_concat[0][0]  
__________________________________________________________________________________________________  
conv5_block5_0_relu (Activation (None, 1, 1, 512)    0           conv5_block5_0_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block5_1_conv (Conv2D)    (None, 1, 1, 128)    65536       conv5_block5_0_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block5_1_bn (BatchNormali (None, 1, 1, 128)    512         conv5_block5_1_conv[0][0]  
__________________________________________________________________________________________________  
conv5_block5_1_relu (Activation (None, 1, 1, 128)    0           conv5_block5_1_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block5_2_conv (Conv2D)    (None, 1, 1, 32)     36864       conv5_block5_1_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block5_concat (Concatenat (None, 1, 1, 544)    0           conv5_block4_concat[0][0]  
                                                                 conv5_block5_2_conv[0][0]        
__________________________________________________________________________________________________  
conv5_block6_0_bn (BatchNormali (None, 1, 1, 544)    2176        conv5_block5_concat[0][0]  
__________________________________________________________________________________________________  
conv5_block6_0_relu (Activation (None, 1, 1, 544)    0           conv5_block6_0_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block6_1_conv (Conv2D)    (None, 1, 1, 128)    69632       conv5_block6_0_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block6_1_bn (BatchNormali (None, 1, 1, 128)    512         conv5_block6_1_conv[0][0]  
__________________________________________________________________________________________________  
conv5_block6_1_relu (Activation (None, 1, 1, 128)    0           conv5_block6_1_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block6_2_conv (Conv2D)    (None, 1, 1, 32)     36864       conv5_block6_1_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block6_concat (Concatenat (None, 1, 1, 576)    0           conv5_block5_concat[0][0]  
                                                                 conv5_block6_2_conv[0][0]        
__________________________________________________________________________________________________  
conv5_block7_0_bn (BatchNormali (None, 1, 1, 576)    2304        conv5_block6_concat[0][0]  
__________________________________________________________________________________________________  
conv5_block7_0_relu (Activation (None, 1, 1, 576)    0           conv5_block7_0_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block7_1_conv (Conv2D)    (None, 1, 1, 128)    73728       conv5_block7_0_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block7_1_bn (BatchNormali (None, 1, 1, 128)    512         conv5_block7_1_conv[0][0]  
__________________________________________________________________________________________________  
conv5_block7_1_relu (Activation (None, 1, 1, 128)    0           conv5_block7_1_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block7_2_conv (Conv2D)    (None, 1, 1, 32)     36864       conv5_block7_1_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block7_concat (Concatenat (None, 1, 1, 608)    0           conv5_block6_concat[0][0]  
                                                                 conv5_block7_2_conv[0][0]        
__________________________________________________________________________________________________  
conv5_block8_0_bn (BatchNormali (None, 1, 1, 608)    2432        conv5_block7_concat[0][0]  
__________________________________________________________________________________________________  
conv5_block8_0_relu (Activation (None, 1, 1, 608)    0           conv5_block8_0_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block8_1_conv (Conv2D)    (None, 1, 1, 128)    77824       conv5_block8_0_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block8_1_bn (BatchNormali (None, 1, 1, 128)    512         conv5_block8_1_conv[0][0]  
__________________________________________________________________________________________________  
conv5_block8_1_relu (Activation (None, 1, 1, 128)    0           conv5_block8_1_bn[0][0]  
__________________________________________________________________________________________________  
conv5_block8_2_conv (Conv2D)    (None, 1, 1, 32)     36864       conv5_block8_1_relu[0][0]  
__________________________________________________________________________________________________  
conv5_block8_concat (Concatenat (None, 1, 1, 640)    0           conv5_block7_concat[0][0]  
                                                                 conv5_block8_2_conv[0][0]        
__________________________________________________________________________________________________  
bn (BatchNormalization)         (None, 1, 1, 640)    2560        conv5_block8_concat[0][0]  
__________________________________________________________________________________________________  
avg_pool (GlobalAveragePooling2 (None, 640)          0           bn[0][0]  
__________________________________________________________________________________________________  
dense_1 (Dense)                 (None, 500)          320500      avg_pool[0][0]  
__________________________________________________________________________________________________  
activation_1 (Activation)       (None, 500)          0           dense_1[0][0]  
__________________________________________________________________________________________________  
dropout_1 (Dropout)             (None, 500)          0           activation_1[0][0]  
__________________________________________________________________________________________________  
dense_2 (Dense)                 (None, 100)          50100       dropout_1[0][0]  
==================================================================================================
Total params: 4,584,424  
Trainable params: 4,536,360  
Non-trainable params: 48,064  
__________________________________________________________________________________________________  

Recordemos que la arquitectura ResNet tenía aproximadamente 25 millones de parámetros a entrenar. Esto quiere decir que hemos aumentado la profundidad pero hemos reducido el número de parámetros a entrenar con 4 millones y medio.

Bien, dicho esto, pasamos a entrenar el modelo:

cdense = custom_dense_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 [==============================] - 328s 7ms/step - loss: 4.2465 - acc: 0.0707 - mean_squared_error: 0.0098 - val_loss: 3.9126 - val_acc: 0.1075 - val_mean_squared_error: 0.0096
Epoch 2/10  
 50000/50000 [==============================] - 321s 6ms/step - loss: 3.7411 - acc: 0.1331 - mean_squared_error: 0.0095 - val_loss: 3.6431 - val_acc: 0.1434 - val_mean_squared_error: 0.0094
Epoch 3/10  
 50000/50000 [==============================] - 322s 6ms/step - loss: 3.4654 - acc: 0.1751 - mean_squared_error: 0.0092 - val_loss: 3.4077 - val_acc: 0.1869 - val_mean_squared_error: 0.0091
Epoch 4/10  
 50000/50000 [==============================] - 325s 7ms/step - loss: 3.2441 - acc: 0.2161 - mean_squared_error: 0.0090 - val_loss: 3.2245 - val_acc: 0.2252 - val_mean_squared_error: 0.0089
Epoch 5/10  
 50000/50000 [==============================] - 324s 6ms/step - loss: 3.0505 - acc: 0.2472 - mean_squared_error: 0.0087 - val_loss: 3.0746 - val_acc: 0.2480 - val_mean_squared_error: 0.0087
Epoch 6/10  
 50000/50000 [==============================] - 327s 7ms/step - loss: 2.8631 - acc: 0.2835 - mean_squared_error: 0.0084 - val_loss: 2.9652 - val_acc: 0.2710 - val_mean_squared_error: 0.0085
Epoch 7/10  
 50000/50000 [==============================] - 326s 7ms/step - loss: 2.7206 - acc: 0.3130 - mean_squared_error: 0.0082 - val_loss: 2.8479 - val_acc: 0.2935 - val_mean_squared_error: 0.0083
Epoch 8/10  
 50000/50000 [==============================] - 329s 7ms/step - loss: 2.5673 - acc: 0.3409 - mean_squared_error: 0.0079 - val_loss: 2.7761 - val_acc: 0.3077 - val_mean_squared_error: 0.0082
Epoch 9/10  
 50000/50000 [==============================] - 325s 7ms/step - loss: 2.4398 - acc: 0.3682 - mean_squared_error: 0.0077 - val_loss: 2.7609 - val_acc: 0.3194 - val_mean_squared_error: 0.0082
Epoch 10/10  
 50000/50000 [==============================] - 327s 7ms/step - loss: 2.3162 - acc: 0.3963 - mean_squared_error: 0.0074 - val_loss: 2.6835 - val_acc: 0.3373 - val_mean_squared_error: 0.0080

Veamos las métricas obtenidas para el entrenamiento y validación gráficamente:

plt.figure(0)  
plt.plot(cdense.history['acc'],'r')  
plt.plot(cdense.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(cdense.history['loss'],'r')  
plt.plot(cdense.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 aunque no ha generalizado bien, los resultados a priori parecen mejores. Veámoslo a continuación.

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:

cdense_pred = custom_dense_model.predict(x_test, batch_size=32, verbose=1)  
cdense_predicted = np.argmax(cdense_pred, axis=1)

cdense_cm = confusion_matrix(np.argmax(y_test, axis=1), cdense_predicted)

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

Matriz de confusión

Y por último, mostramos las métricas:

cdense_report = classification_report(np.argmax(y_test, axis=1), cdense_predicted)  
print(cdense_report)

             precision    recall  f1-score   support

          0       0.55      0.64      0.59       100
          1       0.41      0.33      0.36       100
          2       0.29      0.19      0.23       100
          3       0.15      0.16      0.16       100
          4       0.12      0.09      0.10       100
          5       0.25      0.39      0.31       100
          6       0.39      0.38      0.38       100
          7       0.46      0.34      0.39       100
          8       0.37      0.48      0.42       100
          9       0.44      0.54      0.48       100
         10       0.25      0.16      0.19       100
         11       0.24      0.16      0.19       100
         12       0.39      0.34      0.36       100
         13       0.23      0.14      0.17       100
         14       0.24      0.30      0.27       100
         15       0.20      0.17      0.18       100
         16       0.43      0.41      0.42       100
         17       0.52      0.42      0.46       100
         18       0.30      0.28      0.29       100
         19       0.30      0.29      0.30       100
         20       0.63      0.56      0.59       100
         21       0.40      0.42      0.41       100
         22       0.32      0.23      0.27       100
         23       0.50      0.50      0.50       100
         24       0.50      0.56      0.53       100
         25       0.21      0.20      0.20       100
         26       0.24      0.25      0.25       100
         27       0.24      0.19      0.21       100
         28       0.45      0.45      0.45       100
         29       0.30      0.26      0.28       100
         30       0.31      0.42      0.36       100
         31       0.25      0.37      0.30       100
         32       0.27      0.23      0.25       100
         33       0.32      0.30      0.31       100
         34       0.17      0.14      0.15       100
         35       0.19      0.13      0.15       100
         36       0.35      0.27      0.31       100
         37       0.26      0.22      0.24       100
         38       0.18      0.16      0.17       100
         39       0.54      0.40      0.46       100
         40       0.32      0.31      0.31       100
         41       0.58      0.57      0.57       100
         42       0.19      0.25      0.22       100
         43       0.28      0.27      0.27       100
         44       0.09      0.04      0.06       100
         45       0.16      0.14      0.15       100
         46       0.19      0.15      0.17       100
         47       0.42      0.46      0.44       100
         48       0.45      0.51      0.48       100
         49       0.54      0.48      0.51       100
         50       0.08      0.06      0.07       100
         51       0.30      0.33      0.32       100
         52       0.43      0.73      0.54       100
         53       0.60      0.56      0.58       100
         54       0.49      0.48      0.49       100
         55       0.09      0.07      0.08       100
         56       0.52      0.49      0.51       100
         57       0.32      0.36      0.34       100
         58       0.29      0.25      0.27       100
         59       0.28      0.15      0.20       100
         60       0.64      0.69      0.67       100
         61       0.40      0.44      0.42       100
         62       0.37      0.51      0.43       100
         63       0.31      0.40      0.35       100
         64       0.09      0.09      0.09       100
         65       0.27      0.14      0.18       100
         66       0.25      0.21      0.23       100
         67       0.33      0.23      0.27       100
         68       0.58      0.67      0.62       100
         69       0.51      0.53      0.52       100
         70       0.34      0.37      0.35       100
         71       0.52      0.64      0.58       100
         72       0.05      0.04      0.05       100
         73       0.27      0.21      0.24       100
         74       0.14      0.14      0.14       100
         75       0.50      0.61      0.55       100
         76       0.54      0.62      0.58       100
         77       0.14      0.16      0.15       100
         78       0.18      0.11      0.14       100
         79       0.29      0.24      0.26       100
         80       0.09      0.10      0.09       100
         81       0.24      0.22      0.23       100
         82       0.57      0.74      0.64       100
         83       0.26      0.27      0.26       100
         84       0.17      0.14      0.15       100
         85       0.43      0.57      0.49       100
         86       0.37      0.34      0.35       100
         87       0.37      0.48      0.42       100
         88       0.18      0.29      0.23       100
         89       0.26      0.41      0.32       100
         90       0.23      0.28      0.25       100
         91       0.43      0.53      0.48       100
         92       0.22      0.23      0.22       100
         93       0.17      0.21      0.19       100
         94       0.49      0.70      0.57       100
         95       0.35      0.44      0.39       100
         96       0.25      0.19      0.22       100
         97       0.21      0.29      0.25       100
         98       0.10      0.09      0.10       100
         99       0.25      0.28      0.26       100

avg / total       0.32      0.33      0.32     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], cdense_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(), cdense_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(3), 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 3 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_dense_model.save(path_base + '/cdense.h5')

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

A continuación, vamos a comparar las métricas con los modelos anteriores:

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(cvgg16_history['val_acc'],'y')  
plt.plot(crn50_history['val_acc'],'gold')  
plt.plot(cdense_history['val_acc'],'m')  
plt.xticks(np.arange(0, 11, 2.0))  
plt.rcParams['figure.figsize'] = (8, 6)  
plt.xlabel("Num of Epochs")  
plt.ylabel("Accuracy")  
plt.title("Models")  
plt.legend(['simple NN','CNN','VGG 16','Custom VGG','Custom ResNet', 'Custom DenseNet'])  

Simple NN Vs CNN accuracy

plt.figure(1)  
plt.plot(snn_history['val_loss'],'r')  
plt.plot(scnn_history['val_loss'],'g')  
plt.plot(vgg16_history['val_loss'],'b')  
plt.plot(cvgg16_history['val_loss'],'y')  
plt.plot(crn50_history['val_loss'],'gold')  
plt.plot(cdense_history['val_loss'],'m')  
plt.xticks(np.arange(0, 11, 2.0))  
plt.rcParams['figure.figsize'] = (8, 6)  
plt.xlabel("Num of Epochs")  
plt.ylabel("Loss")  
plt.title("Models")  
plt.legend(['simple NN','CNN','VGG 16','Custom VGG','Custom ResNet', 'Custom DenseNet'])  

Simple NN Vs CNN loss

plt.figure(2)  
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(cvgg16_history['val_mean_squared_error'],'y')  
plt.plot(crn50_history['val_mean_squared_error'],'gold')  
plt.plot(cdense_history['val_mean_squared_error'],'m')  
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("Models")  
plt.legend(['simple NN','CNN','VGG 16','Custom VGG','Custom ResNet', 'Custom DenseNet'])  

Simple NN Vs CNN MSE

Conclusión sobre el experimento

La arquitectura DenseNet da unos resultados mucho mejores reduciendo el número de parámetros a aprender, con lo que también puede llegar a reducir los tiempos de entrenamiento.

En el siguiente artículo, presentaremos la arquitectura: NASNet.

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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.