Из скольких слоев состоит модель Inception-ResNet-v2?

На основе Inception ResNet V2, представленного в https://github.com/titu1994/Inception-v4/blob/master/inception_resnet_v2.py

ResNet V2 имеет 467 слоев, как показано ниже.

input_1 
conv2d_1 
conv2d_2 
conv2d_3 
max_pooling2d_1 
conv2d_4 
merge_1 
conv2d_7 
conv2d_8 
conv2d_5 
conv2d_9 
conv2d_6 
conv2d_10 
merge_2 
max_pooling2d_2 
conv2d_11 
merge_3 
batch_normalization_1 
activation_1 
conv2d_15 
conv2d_13 
conv2d_16 
conv2d_12 
conv2d_14 
conv2d_17 
merge_4 
conv2d_18 
lambda_1 
merge_5 
batch_normalization_2 
activation_2 
conv2d_22 
conv2d_20 
conv2d_23 
conv2d_19 
conv2d_21 
conv2d_24 
merge_6 
conv2d_25 
lambda_2 
merge_7 
batch_normalization_3 
activation_3 
conv2d_29 
conv2d_27 
conv2d_30 
conv2d_26 
conv2d_28 
conv2d_31 
merge_8 
conv2d_32 
lambda_3 
merge_9 
batch_normalization_4 
activation_4 
conv2d_36 
conv2d_34 
conv2d_37 
conv2d_33 
conv2d_35 
conv2d_38 
merge_10 
conv2d_39 
lambda_4 
merge_11 
batch_normalization_5 
activation_5 
conv2d_43 
conv2d_41 
conv2d_44 
conv2d_40 
conv2d_42 
conv2d_45 
merge_12 
conv2d_46 
lambda_5 
merge_13 
batch_normalization_6 
activation_6 
conv2d_50 
conv2d_48 
conv2d_51 
conv2d_47 
conv2d_49 
conv2d_52 
merge_14 
conv2d_53 
lambda_6 
merge_15 
batch_normalization_7 
activation_7 
conv2d_57 
conv2d_55 
conv2d_58 
conv2d_54 
conv2d_56 
conv2d_59 
merge_16 
conv2d_60 
lambda_7 
merge_17 
batch_normalization_8 
activation_8 
conv2d_64 
conv2d_62 
conv2d_65 
conv2d_61 
conv2d_63 
conv2d_66 
merge_18 
conv2d_67 
lambda_8 
merge_19 
batch_normalization_9 
activation_9 
conv2d_71 
conv2d_69 
conv2d_72 
conv2d_68 
conv2d_70 
conv2d_73 
merge_20 
conv2d_74 
lambda_9 
merge_21 
batch_normalization_10 
activation_10 
conv2d_78 
conv2d_76 
conv2d_79 
conv2d_75 
conv2d_77 
conv2d_80 
merge_22 
conv2d_81 
lambda_10 
merge_23 
batch_normalization_11 
activation_11 
conv2d_83 
conv2d_84 
max_pooling2d_3 
conv2d_82 
conv2d_85 
merge_24 
batch_normalization_12 
activation_12 
conv2d_87 
conv2d_88 
conv2d_86 
conv2d_89 
merge_25 
conv2d_90 
lambda_11 
merge_26 
batch_normalization_13 
activation_13 
conv2d_92 
conv2d_93 
conv2d_91 
conv2d_94 
merge_27 
conv2d_95 
lambda_12 
merge_28 
batch_normalization_14 
activation_14 
conv2d_97 
conv2d_98 
conv2d_96 
conv2d_99 
merge_29 
conv2d_100 
lambda_13 
merge_30 
batch_normalization_15 
activation_15 
conv2d_102 
conv2d_103 
conv2d_101 
conv2d_104 
merge_31 
conv2d_105 
lambda_14 
merge_32 
batch_normalization_16 
activation_16 
conv2d_107 
conv2d_108 
conv2d_106 
conv2d_109 
merge_33 
conv2d_110 
lambda_15 
merge_34 
batch_normalization_17 
activation_17 
conv2d_112 
conv2d_113 
conv2d_111 
conv2d_114 
merge_35 
conv2d_115 
lambda_16 
merge_36 
batch_normalization_18 
activation_18 
conv2d_117 
conv2d_118 
conv2d_116 
conv2d_119 
merge_37 
conv2d_120 
lambda_17 
merge_38 
batch_normalization_19 
activation_19 
conv2d_122 
conv2d_123 
conv2d_121 
conv2d_124 
merge_39 
conv2d_125 
lambda_18 
merge_40 
batch_normalization_20 
activation_20 
conv2d_127 
conv2d_128 
conv2d_126 
conv2d_129 
merge_41 
conv2d_130 
lambda_19 
merge_42 
batch_normalization_21 
activation_21 
conv2d_132 
conv2d_133 
conv2d_131 
conv2d_134 
merge_43 
conv2d_135 
lambda_20 
merge_44 
batch_normalization_22 
activation_22 
conv2d_137 
conv2d_138 
conv2d_136 
conv2d_139 
merge_45 
conv2d_140 
lambda_21 
merge_46 
batch_normalization_23 
activation_23 
conv2d_142 
conv2d_143 
conv2d_141 
conv2d_144 
merge_47 
conv2d_145 
lambda_22 
merge_48 
batch_normalization_24 
activation_24 
conv2d_147 
conv2d_148 
conv2d_146 
conv2d_149 
merge_49 
conv2d_150 
lambda_23 
merge_50 
batch_normalization_25 
activation_25 
conv2d_152 
conv2d_153 
conv2d_151 
conv2d_154 
merge_51 
conv2d_155 
lambda_24 
merge_52 
batch_normalization_26 
activation_26 
conv2d_157 
conv2d_158 
conv2d_156 
conv2d_159 
merge_53 
conv2d_160 
lambda_25 
merge_54 
batch_normalization_27 
activation_27 
conv2d_162 
conv2d_163 
conv2d_161 
conv2d_164 
merge_55 
conv2d_165 
lambda_26 
merge_56 
batch_normalization_28 
activation_28 
conv2d_167 
conv2d_168 
conv2d_166 
conv2d_169 
merge_57 
conv2d_170 
lambda_27 
merge_58 
batch_normalization_29 
activation_29 
conv2d_172 
conv2d_173 
conv2d_171 
conv2d_174 
merge_59 
conv2d_175 
lambda_28 
merge_60 
batch_normalization_30 
activation_30 
conv2d_177 
conv2d_178 
conv2d_176 
conv2d_179 
merge_61 
conv2d_180 
lambda_29 
merge_62 
batch_normalization_31 
activation_31 
conv2d_182 
conv2d_183 
conv2d_181 
conv2d_184 
merge_63 
conv2d_185 
lambda_30 
merge_64 
batch_normalization_32 
activation_32 
conv2d_192 
conv2d_188 
conv2d_190 
conv2d_193 
max_pooling2d_4 
conv2d_189 
conv2d_191 
conv2d_194 
merge_65 
batch_normalization_33 
activation_33 
conv2d_196 
conv2d_197 
conv2d_195 
conv2d_198 
merge_66 
conv2d_199 
lambda_31 
merge_67 
batch_normalization_34 
activation_34 
conv2d_201 
conv2d_202 
conv2d_200 
conv2d_203 
merge_68 
conv2d_204 
lambda_32 
merge_69 
batch_normalization_35 
activation_35 
conv2d_206 
conv2d_207 
conv2d_205 
conv2d_208 
merge_70 
conv2d_209 
lambda_33 
merge_71 
batch_normalization_36 
activation_36 
conv2d_211 
conv2d_212 
conv2d_210 
conv2d_213 
merge_72 
conv2d_214 
lambda_34 
merge_73 
batch_normalization_37 
activation_37 
conv2d_216 
conv2d_217 
conv2d_215 
conv2d_218 
merge_74 
conv2d_219 
lambda_35 
merge_75 
batch_normalization_38 
activation_38 
conv2d_221 
conv2d_222 
conv2d_220 
conv2d_223 
merge_76 
conv2d_224 
lambda_36 
merge_77 
batch_normalization_39 
activation_39 
conv2d_226 
conv2d_227 
conv2d_225 
conv2d_228 
merge_78 
conv2d_229 
lambda_37 
merge_79 
batch_normalization_40 
activation_40 
conv2d_231 
conv2d_232 
conv2d_230 
conv2d_233 
merge_80 
conv2d_234 
lambda_38 
merge_81 
batch_normalization_41 
activation_41 
conv2d_236 
conv2d_237 
conv2d_235 
conv2d_238 
merge_82 
conv2d_239 
lambda_39 
merge_83 
batch_normalization_42 
activation_42 
conv2d_241 
conv2d_242 
conv2d_240 
conv2d_243 
merge_84 
conv2d_244 
lambda_40 
merge_85 
batch_normalization_43 
activation_43 
average_pooling2d_1 
average_pooling2d_2 
conv2d_186 
dropout_1 
conv2d_187 
flatten_2 
flatten_1 
dense_2 
dense_1 

Чтобы просмотреть полное описание слоев, вы можете загрузить файл inception_resnet_v2.py и добавить эти две строки в его конец:

res2=create_inception_resnet_v2()
print(res2.summary())

Что касается вашего второго вопроса (кстати, в следующий раз я предлагаю вам разделить вопросы, а не писать их вместе) - Да, этих данных, скорее всего, будет вообще недостаточно для обучения любой из этих сетей. Честно говоря, этого было бы недостаточно даже для скромного VGG, если не использовать аугментацию с умом - и даже в этом случае, на мой взгляд, это было бы серьезным вызовом.

Вам следует подумать об использовании опубликованных весов, если это применимо, или, по крайней мере, использовать их для трансферного обучения.