KerasSequential模型雖然相同，但準確性低于功能模型

我訓練了兩個具有相同可訓練引數和相同結構的模型。但是與順序模型相比，功能模型的性能更好。試圖從給定影像中預測向量。影像輸出來自 vgg16 模型。不包括頂層。當將原始向量與預測向量進行比較時。函式模型往往與原始向量具有更大的相似性。有人可以解釋為什么會這樣嗎？

下面的代碼 -

from keras.models import Sequential
import numpy as np
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from tensorflow import keras
from numpy import random
from sklearn.metrics.pairwise import cosine_similarity

epochs=2000

x = random.random_sample((1, 4096))
y = np.array([ 0.01897711, 0.00196044, -0.0100884 , 0.08048831, 0.07945059, -0.13450155, -0.00228113, 0.30315322, -0.2170798 , 0.12462355, -0.12226178, -0.19237731, -0.14406398, 0.11556922, 0.04466464, -0.22505943, -0.07492258, -0.05925079, 0.02871693, -0.32403016, 0.16885516, -0.01677704, 0.03490563, 0.08720589, -0.03105724, -0.10850648, 0.04820024, -0.1348836 , -0.26358405, 0.08388387, 0.13177398, 0.00133367, -0.01074621, -0.01703981, 0.14912938, 0.13562258, 0.12910905, -0.02097122, -0.05823291, -0.21523051, -0.1051832 , -0.0112495 , -0.02306462, 0.30883443, 0.24211378, -0.01332151, -0.04171557, -0.07624041, 0.05742156, 0.17561561, -0.05971769, -0.22914584, -0.2354534 , -0.12413627, -0.02892042, -0.08661073, 0.14135012, -0.15514424, -0.09965582, -0.13770337, 0.09548005, 0.0925705 , -0.10030732, 0.16057852, -0.17537649, 0.23076315, -0.12471516, 0.2811343 , -0.1576465 , 0.17364068, 0.0658261 , 0.044597 , 0.27390295, -0.04520088, 0.00317772, 0.05926268, 0.06897669, -0.2579084 , -0.30417407, -0.08170868, -0.10205928, -0.14339833, -0.2291172 , 0.1584655 , -0.108877 , 0.03841971, -0.02097263, -0.00477816, -0.08784705, 0.00944081, 0.01409219, 0.1655657 , 0.09393094, 0.233216 , 0.28611556, -0.00573498, 0.1374636 , -0.19641444, 0.14472656, 0.254758 , -0.26166946, 0.30998066, 0.1026804 , -0.0578127 , -0.0882837 , -0.25514072, 0.12337176, 0.1786545 , 0.04052542, -0.17535737, -0.05401937, -0.27649277, -0.04952267, 0.08122452, 0.04374097, -0.07044917, 0.0653659 , -0.36983526, -0.02356564, -0.01144519, 0.1440273 , 0.12321867, 0.10163002, -0.13444787, -0.06148207, 0.11309719, -0.24679276, -0.04028287, -0.0930292 , -0.06392674, 0.10477038, 0.00828285, -0.11968364, -0.16145884, -0.08808196, 0.14231506, -0.02768413, -0.24046096, 0.02477906, -0.3868386 , 0.08224358, -0.30728677, -0.31634584, -0.24805053, -0.19289431, -0.04890246, -0.23479757, 0.13149938, 0.02801071, 0.12761658, 0.02897108, -0.14499697, 0.05322106, 0.06153642, -0.21517622, 0.255269 , 0.08573797, 0.09940388, -0.10590497, 0.13063994, 0.11253715, 0.15636472, -0.19782121, 0.01258014, -0.04391019, 0.16168897, -0.05669969, -0.17957021, -0.04841055, -0.00175814, -0.25425357, 0.14485207, 0.08319512, -0.20990393, 0.04344559, 0.20995931, -0.16608813, 0.28736553, 0.12240092, 0.12146739, 0.05718496, 0.01994314, 0.09686041, 0.13452487, 0.1052431 , 0.10266875, -0.01051683, 0.01536175, 0.25623122, 0.11273847, 0.06577922, -0.09992851, -0.02046986, -0.11516961, 0.12051879, 0.00518495, 0.0988002 , -0.279763 , -0.09997523, -0.04474135])
y = y.reshape(1,-1)

inputs  = Input(shape=(4096,))
decoder = Dense(256, activation="sigmoid")(inputs)
decoder = Dense(256, activation="sigmoid")(decoder)
decoder = Dense(256, activation="sigmoid")(decoder)
outputs = Dense(200, activation="sigmoid")(decoder)

functional = Model(inputs=inputs, outputs=outputs)
opt = keras.optimizers.Adam(learning_rate=0.01)
functional.compile(loss="mse", optimizer=opt)

sequen = Sequential()
sequen.add(Dense(256,input_shape=(4096,),activation="sigmoid"))
sequen.add(Dense(256,activation="sigmoid"))
sequen.add(Dense(256,activation="sigmoid"))
sequen.add(Dense(200,activation="sigmoid"))
sequen.compile(loss="mse", optimizer=opt)

functional.fit(x,y,verbose=1,validation_data=(x, y),epochs=epochs)
sequen.fit(x,y,verbose=1,validation_data=(x, y),epochs=epochs)


functional_output = cosine_similarity(functional.predict(x),y)
sequential_output = cosine_similarity(sequen.predict(x),y)
print(functional_output,sequential_output)

#Calculating cosine_similarity between both outputs. Functional api gives gives better output.
#output - array([[0.65056009]]), array([[0.19631703]])

功能模型結構

Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 4096)]            0         
                                                                 
 dense (Dense)               (None, 256)               1048832   
                                                                 
 dense_1 (Dense)             (None, 256)               65792     
                                                                 
 dense_2 (Dense)             (None, 256)               65792     
                                                                 
 dense_3 (Dense)             (None, 200)               51400     
                                                                 
=================================================================
Total params: 1,231,816
Trainable params: 1,231,816
Non-trainable params: 0
_________________________________________________________________

序列模型結構

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_4 (Dense)             (None, 256)               1048832   
                                                                 
 dense_5 (Dense)             (None, 256)               65792     
                                                                 
 dense_6 (Dense)             (None, 256)               65792     
                                                                 
 dense_7 (Dense)             (None, 200)               51400     
                                                                 
=================================================================
Total params: 1,231,816
Trainable params: 1,231,816
Non-trainable params: 0
_________________________________________________________________

uj5u.com熱心網友回復：

我認為主要問題是你使用相同的優化器來訓練你的模型，并且在訓練你的第一個模型之后，優化器已經有了一個內部狀態。使用兩個單獨的優化器似乎會產生（幾乎）相同的結果：

...
opt1 = keras.optimizers.Adam(learning_rate=0.01)
opt2 = keras.optimizers.Adam(learning_rate=0.01)
...

...
[[0.65034289]] [[0.65033581]]

uj5u.com熱心網友回復：

這兩個模型使用不同的權重和偏差進行初始化。kernel_initializer=tf.keras.initializers.Zeros()您可以通過添加引數和將模型的權重和偏差初始化為零矩陣bias_initializer=tf.keras.initializers.Zeros()。如果你運行這段代碼，你會看到類似的結果，但并不完全相同。正如@AloneTogether
所指出的，在訓練你的第一個模型之后，優化器已經有了一個內部狀態。因此，再次初始化該優化器將解決此問題。

所以，如果你運行這段代碼，你會得到相同的結果：

from keras.models import Sequential
import numpy as np
from keras.models import Model
from keras.layers import Input
from keras.layers import Dense
from tensorflow import keras
import tensorflow as tf
from numpy import random
from sklearn.metrics.pairwise import cosine_similarity

epochs=200

x = random.random_sample((1, 4096))
y = np.array([ 0.01897711, 0.00196044, -0.0100884 , 0.08048831, 0.07945059, -0.13450155, -0.00228113, 0.30315322, -0.2170798 , 0.12462355, -0.12226178, -0.19237731, -0.14406398, 0.11556922, 0.04466464, -0.22505943, -0.07492258, -0.05925079, 0.02871693, -0.32403016, 0.16885516, -0.01677704, 0.03490563, 0.08720589, -0.03105724, -0.10850648, 0.04820024, -0.1348836 , -0.26358405, 0.08388387, 0.13177398, 0.00133367, -0.01074621, -0.01703981, 0.14912938, 0.13562258, 0.12910905, -0.02097122, -0.05823291, -0.21523051, -0.1051832 , -0.0112495 , -0.02306462, 0.30883443, 0.24211378, -0.01332151, -0.04171557, -0.07624041, 0.05742156, 0.17561561, -0.05971769, -0.22914584, -0.2354534 , -0.12413627, -0.02892042, -0.08661073, 0.14135012, -0.15514424, -0.09965582, -0.13770337, 0.09548005, 0.0925705 , -0.10030732, 0.16057852, -0.17537649, 0.23076315, -0.12471516, 0.2811343 , -0.1576465 , 0.17364068, 0.0658261 , 0.044597 , 0.27390295, -0.04520088, 0.00317772, 0.05926268, 0.06897669, -0.2579084 , -0.30417407, -0.08170868, -0.10205928, -0.14339833, -0.2291172 , 0.1584655 , -0.108877 , 0.03841971, -0.02097263, -0.00477816, -0.08784705, 0.00944081, 0.01409219, 0.1655657 , 0.09393094, 0.233216 , 0.28611556, -0.00573498, 0.1374636 , -0.19641444, 0.14472656, 0.254758 , -0.26166946, 0.30998066, 0.1026804 , -0.0578127 , -0.0882837 , -0.25514072, 0.12337176, 0.1786545 , 0.04052542, -0.17535737, -0.05401937, -0.27649277, -0.04952267, 0.08122452, 0.04374097, -0.07044917, 0.0653659 , -0.36983526, -0.02356564, -0.01144519, 0.1440273 , 0.12321867, 0.10163002, -0.13444787, -0.06148207, 0.11309719, -0.24679276, -0.04028287, -0.0930292 , -0.06392674, 0.10477038, 0.00828285, -0.11968364, -0.16145884, -0.08808196, 0.14231506, -0.02768413, -0.24046096, 0.02477906, -0.3868386 , 0.08224358, -0.30728677, -0.31634584, -0.24805053, -0.19289431, -0.04890246, -0.23479757, 0.13149938, 0.02801071, 0.12761658, 0.02897108, -0.14499697, 0.05322106, 0.06153642, -0.21517622, 0.255269 , 0.08573797, 0.09940388, -0.10590497, 0.13063994, 0.11253715, 0.15636472, -0.19782121, 0.01258014, -0.04391019, 0.16168897, -0.05669969, -0.17957021, -0.04841055, -0.00175814, -0.25425357, 0.14485207, 0.08319512, -0.20990393, 0.04344559, 0.20995931, -0.16608813, 0.28736553, 0.12240092, 0.12146739, 0.05718496, 0.01994314, 0.09686041, 0.13452487, 0.1052431 , 0.10266875, -0.01051683, 0.01536175, 0.25623122, 0.11273847, 0.06577922, -0.09992851, -0.02046986, -0.11516961, 0.12051879, 0.00518495, 0.0988002 , -0.279763 , -0.09997523, -0.04474135])
y = y.reshape(1,-1)

inputs  = Input(shape=(4096,))
decoder = Dense(256, activation="sigmoid", kernel_initializer=tf.keras.initializers.Zeros(), bias_initializer=tf.keras.initializers.Zeros())(inputs)
decoder = Dense(256, activation="sigmoid", kernel_initializer=tf.keras.initializers.Zeros(), bias_initializer=tf.keras.initializers.Zeros())(decoder)
decoder = Dense(256, activation="sigmoid", kernel_initializer=tf.keras.initializers.Zeros(), bias_initializer=tf.keras.initializers.Zeros())(decoder)
outputs = Dense(200, activation="sigmoid", kernel_initializer=tf.keras.initializers.Zeros(), bias_initializer=tf.keras.initializers.Zeros())(decoder)

functional = Model(inputs=inputs, outputs=outputs)
opt = keras.optimizers.Adam(learning_rate=0.01)
functional.compile(loss="mse", optimizer=opt)

sequen = Sequential()
sequen.add(Input(shape=(4096,)))
sequen.add(Dense(256,activation="sigmoid", kernel_initializer=tf.keras.initializers.Zeros(), bias_initializer=tf.keras.initializers.Zeros()))
sequen.add(Dense(256,activation="sigmoid", kernel_initializer=tf.keras.initializers.Zeros(), bias_initializer=tf.keras.initializers.Zeros()))
sequen.add(Dense(256,activation="sigmoid", kernel_initializer=tf.keras.initializers.Zeros(), bias_initializer=tf.keras.initializers.Zeros()))
sequen.add(Dense(200,activation="sigmoid", kernel_initializer=tf.keras.initializers.Zeros(), bias_initializer=tf.keras.initializers.Zeros()))

opt2 = keras.optimizers.Adam(learning_rate=0.01)
sequen.compile(loss="mse", optimizer=opt2)

functional.fit(x,y,verbose=1,validation_data=(x, y),epochs=epochs)
sequen.fit(x,y,verbose=1,validation_data=(x, y),epochs=epochs)


functional_output = cosine_similarity(functional.predict(x),y)
sequential_output = cosine_similarity(sequen.predict(x),y)
print(functional_output,sequential_output)

標籤：Python 张量流 机器学习 喀拉斯

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