实现 TensorFlow Triplet Loss

Question

我想实现 triplet loss 的内置 TensorFlow 插件版本，并带有用于连体网络的教程 here，但我似乎无法完全正确。无论我如何处理代码，都会弹出另一个错误，目前

TypeError: Could not build a TypeSpec for <KerasTensor: shape=(3, None, 256) dtype=float32 (created by layer 'tf.math.l2_normalize_4')> with type KerasTensor.  

注意，这只是为了理解如何实现 Triplet Loss 而保持简单的令牌实现。我不希望该模型真正学到任何东西。
代码：

!pip install -U tensorflow-addons

import io
import numpy as np
import tensorflow as tf
import tensorflow_addons as tfa
from tensorflow.keras.datasets import fashion_mnist

# Dummy data to pass to the model
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

train_data = [x_train[:20000],x_train[20000:40000],x_train[40000:]]
train_labels = [y_train[:20000],y_train[20000:40000],y_train[40000:]] 

train_data = tf.convert_to_tensor(train_data)
train_labels = tf.convert_to_tensor(train_labels)
#train_data = np.asarray(train_data)
#train_labels = np.asarray(train_labels)

def create_model(input_shape):
  inp = tf.keras.layers.Input(shape=input_shape)
  x = tf.keras.layers.Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', input_shape=(28,28,1))(inp)
  x = tf.keras.layers.MaxPooling2D(pool_size=2)(x)
  x = tf.keras.layers.Dropout(0.3)(x)
  x = tf.keras.layers.Conv2D(filters=32, kernel_size=2, padding='same', activation='relu')(x)
  x = tf.keras.layers.MaxPooling2D(pool_size=2)(x)
  x = tf.keras.layers.Dropout(0.3)(x)
  x = tf.keras.layers.Flatten()(x)
  x = tf.keras.layers.Dense(256, activation=None)(x) # No activation on final dense layer
  #x = tf.keras.layers.Lambda(lambda y: tf.math.l2_normalize(x, axis=1))(x)
  model = tf.keras.Model(inp,x)
  return model

def get_siamese_model(input_shape):
    """
        Model architecture
    """
    
    # Define the tensors for the triplet of input images
    anchor_input = tf.keras.layers.Input(input_shape, name="anchor_input")
    positive_input = tf.keras.layers.Input(input_shape, name="positive_input")
    negative_input = tf.keras.layers.Input(input_shape, name="negative_input")
    
    # Convolutional Neural Network (same from earlier)
    embedding_model = create_model(input_shape)
    
    # Generate the embedding outputs 
    encoded_anchor = embedding_model(anchor_input)
    encoded_positive = embedding_model(positive_input)
    encoded_negative = embedding_model(negative_input)
    
    inputs = [anchor_input, positive_input, negative_input]
    outputs = [encoded_anchor, encoded_positive, encoded_negative]
    #x = tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(outputs, axis=1))(outputs)
    
    # Connect the inputs with the outputs
    siamese_triplet = tf.keras.Model(inputs=inputs,outputs=outputs)
    
    # return the model
    return embedding_model, siamese_triplet

emb_mod, model = get_siamese_model([28,28,1])

# Compile the model
model.compile(
    optimizer=tf.keras.optimizers.Adam(0.001),
    loss=tfa.losses.TripletSemiHardLoss())

# Train the network
#train_dataset = tf.convert_to_tensor(train_dataset)
history = model.fit(
    train_data,
    epochs=5)

Answer 1

我不确定您到底想做什么，但在使用 tfa.losses.TripletSemiHardLoss() 时，您还必须将标签合并到训练数据集中。这是一个工作示例：

import io
import numpy as np
import tensorflow as tf
import tensorflow_addons as tfa
from tensorflow.keras.datasets import fashion_mnist

# Dummy data to pass to the model
(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

train_data = tf.data.Dataset.zip((tf.data.Dataset.from_tensor_slices(x_train[:20000]),
                     tf.data.Dataset.from_tensor_slices(x_train[20000:40000]),
                     tf.data.Dataset.from_tensor_slices(x_train[40000:])))
train_labels = tf.data.Dataset.zip((tf.data.Dataset.from_tensor_slices(y_train[:20000]),
                     tf.data.Dataset.from_tensor_slices(y_train[20000:40000]),
                     tf.data.Dataset.from_tensor_slices(y_train[40000:])))

dataset =  tf.data.Dataset.zip((train_data, train_labels)).batch(32)                
def create_model(input_shape):
  inp = tf.keras.layers.Input(shape=input_shape)
  x = tf.keras.layers.Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', input_shape=(28,28,1))(inp)
  x = tf.keras.layers.MaxPooling2D(pool_size=2)(x)
  x = tf.keras.layers.Dropout(0.3)(x)
  x = tf.keras.layers.Conv2D(filters=32, kernel_size=2, padding='same', activation='relu')(x)
  x = tf.keras.layers.MaxPooling2D(pool_size=2)(x)
  x = tf.keras.layers.Dropout(0.3)(x)
  x = tf.keras.layers.Flatten()(x)
  x = tf.keras.layers.Dense(256, activation=None)(x) # No activation on final dense layer
  #x = tf.keras.layers.Lambda(lambda y: tf.math.l2_normalize(x, axis=1))(x)
  model = tf.keras.Model(inp,x)
  return model

def get_siamese_model(input_shape):
    """
        Model architecture
    """
    
    # Define the tensors for the triplet of input images
    anchor_input = tf.keras.layers.Input(input_shape, name="anchor_input")
    positive_input = tf.keras.layers.Input(input_shape, name="positive_input")
    negative_input = tf.keras.layers.Input(input_shape, name="negative_input")
    
    # Convolutional Neural Network (same from earlier)
    embedding_model = create_model(input_shape)
    
    # Generate the embedding outputs 
    encoded_anchor = embedding_model(anchor_input)
    encoded_positive = embedding_model(positive_input)
    encoded_negative = embedding_model(negative_input)
    
    inputs = [anchor_input, positive_input, negative_input]
    outputs = [encoded_anchor, encoded_positive, encoded_negative]
    #x = tf.keras.layers.Lambda(lambda x: tf.math.l2_normalize(outputs, axis=1))(outputs)
    
    # Connect the inputs with the outputs
    siamese_triplet = tf.keras.Model(inputs=inputs,outputs=outputs)
    
    # return the model
    return embedding_model, siamese_triplet

emb_mod, model = get_siamese_model([28,28,1])

# Compile the model
model.compile(
    optimizer=tf.keras.optimizers.Adam(0.001),
    loss=tfa.losses.TripletSemiHardLoss())

# Train the network
history = model.fit(
    dataset,
    epochs=1)

625/625 [==============================] - 76s 120ms/step - loss: 0.1354 - model_79_loss: 0.0572 - model_79_1_loss: 0.0453 - model_79_2_loss: 0.0330