tensorflow Triplet_semihard_loss 在多個時期后不會改變

Question

我正在編寫一個訓練自定義人臉重新識別系統的基本版本（使用 mnist 數據作為構建塊，tensorflow 定義了半硬三元組損失函數），但損失 /acc 顯示在多個時期后絕對沒有變化。 下面的代碼

def kerasTriplet( label, pred ):
        print('-------------------------')
        print( label )
        print( pred )
        def lossFunc( y_true, y_pred ):
                return tf.contrib.losses.metric_learning.triplet_semihard_loss( label, pred, 0.6 )
                #return nonTFTripletLoss.batch_hard_triplet_loss( label, pred, 0.6 )

        return lossFunc

def gen( trg, tgt ):
        batch_sz = BATCH_SZ
        start = np.random.randint( 0, len( trg ) - BATCH_SZ )
        return trg[ start: start+batch_sz] , tgt[ start: start+batch_sz ]

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
n_train, height, width = x_train.shape
x_train = x_train.reshape(n_train, height, width, 1).astype('float32')
x_train = x_train[ :(int(len(x_train)/BATCH_SZ))*BATCH_SZ ]
x_train /= 255
num_classes = 10
y_train_orig = y_train
y_train_orig = y_train_orig[ :(int(len(x_train)/BATCH_SZ))*BATCH_SZ ]
y_train = tf.keras.utils.to_categorical(y_train, num_classes)

input_shape = (28, 28, 1)

sequence_input = tf.keras.layers.Input(shape=input_shape , dtype='float32')
batch_inp, batch_tgt = gen( x_train, y_train_orig )


x = tf.keras.layers.Conv2D( 512, (3,3), activation='relu')( batch_inp )
x = tf.keras.layers.Conv2D( 256, (3,3), activation='relu')( x )
x = tf.keras.layers.Conv2D( 128, (3,3), activation='relu')( x )

x = tf.keras.layers.Flatten()(x)
img_embedding = tf.keras.layers.Dense( 128 )(x)

## since triplet loss requires embedding to be l2 normalized
l2_embed = tf.keras.backend.l2_normalize( img_embedding, -1 )

model = tf.keras.models.Model( sequence_input , l2_embed )

model.compile( loss=kerasTriplet( batch_tgt, img_embedding ) , optimizer='adam', metrics=['acc'] )

model.fit(x_train, y_train_orig, batch_size=BATCH_SZ,  epochs=10 , verbose=1)

我預計損失和 acc 會發生變化，即使幅度不大（因為我只運行了 10 個 epoch），但它完全一樣。 我確信這與我的代碼有關。 就是指不上它

Answer 1

您計算 l2_embedding 不正確。 嘗試這個

l2_embed = tf.keras.backend.l2_normalize（img_embedding，軸= 1）