Как заставить модель Keras VGG16 показывать и включать подробные слои при использовании в новых настроенных моделях

РЕДАКТИРОВАТЬ: на основе ваших комментариев вот обновленное решение.

Вы можете сгладить вложенную модель, перебирая слои и добавляя их к последовательной модели. Вот отличное решение, которое у меня есть используется для кода ниже.

import numpy as np
import tensorflow as tf
from tensorflow.keras.applications import VGG16
from tensorflow.keras import layers, Model, utils

#Instantiating the VGG model
conv_base = VGG16(weights='imagenet',  # pre-train with ImageNet
                  include_top=False,  # exclude the three top layers
                  input_shape=(64, 64, 3),
                  pooling = 'max')

#Defining secondary nested model
inp = layers.Input((64,64,3))
cnn = conv_base(inp)
x = layers.BatchNormalization()(cnn)
x = layers.Dropout(0.2)(x)
x = layers.Dense(256, activation='linear')(x)
x = layers.BatchNormalization()(x)
x = layers.Dropout(0.2)(x)
out = layers.Dense(1, activation='linear')(x)

model = Model(inp, out)

#Flattening nested model
def flatten_model(model_nested):
    layers_flat = []
    for layer in model_nested.layers:
        try:
            layers_flat.extend(layer.layers)
        except AttributeError:
            layers_flat.append(layer)
    model_flat = tf.keras.models.Sequential(layers_flat)
    return model_flat

model_flat = flatten_model(model)

model_flat.summary()

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_10 (InputLayer)        multiple                  0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 64, 64, 64)        1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 64, 64, 64)        36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 32, 32, 64)        0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 32, 32, 128)       73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 32, 32, 128)       147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 16, 16, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 16, 16, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 16, 16, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 16, 16, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 8, 8, 256)         0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 8, 8, 512)         1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 4, 4, 512)         0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 2, 2, 512)         0         
_________________________________________________________________
global_max_pooling2d_3 (Glob (None, 512)               0         
_________________________________________________________________
batch_normalization_4 (Batch (None, 512)               2048      
_________________________________________________________________
dropout_4 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_4 (Dense)              (None, 256)               131328    
_________________________________________________________________
batch_normalization_5 (Batch (None, 256)               1024      
_________________________________________________________________
dropout_5 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_5 (Dense)              (None, 1)                 257       
=================================================================
Total params: 14,849,345
Trainable params: 14,847,809
Non-trainable params: 1,536
_________________________________________________________________

Я бы рекомендовал использовать альтернативный способ резюмирования модели.

Для этой цели вы можете использовать utils.plot_model с expand_nested=True.

tf.keras.utils.plot_model(model, show_shapes=True, show_layer_names=True, expand_nested=True)