我正在嘗試構建一個 tensorflow 模型，該模型將 3 個影像作為輸入，并為每個輸入影像提供 3 個輸出嵌入。影像保存在一個名為triplet
的串列中。代碼部分如下：
def inner_model(act_func='relu', input_shape=(112,112,3):
model_input = layers.Input(shape=input_shape)
x = layers.Conv2D(filters=16, kernel_size=3, padding='same', activation=act_func, name='Conv1')(model_input)
x = layers.MaxPool2D()(x)
x = layers.Conv2D(filters=32, kernel_size=3, padding='same', activation=act_func, name='Conv2')(x)
x = layers.MaxPool2D()(x)
x = layers.Conv2D(filters=64, kernel_size=3, padding='same', activation=act_func, name='Conv3')(x)
x = layers.MaxPool2D()(x)
x = layers.Conv2D(filters=128, kernel_size=3, padding='same', activation=act_func, name='Conv4')(x)
x = layers.MaxPool2D()(x)
x = layers.Conv2D(filters=256, kernel_size=3, padding='same', activation=act_func, name='Conv5')(x)
x = layers.GlobalAvgPool2D(name='GAP')(x)
output = layers.Dense(128, activation=act_func, name='Dense1')(x)
model = Model(inputs=model_input, outputs=output)
model.summary()
return model
def tripler_trainer(input_shape=(112,112,3)):
anchor_input = layers.Input(shape=input_shape)
positive_input = layers.Input(shape=input_shape)
negative_input = layers.Input(shape=input_shape)
embedder = inner_model(act_func='relu', input_shape=(112,112,3)
anchor_embedding = embedder(anchor_input)
positive_embedding = embedder(positive_input)
negative_embedding = embedder(negative_input)
outer_network = Model(inputs=(anchor_input, positive_input, negative_input),
outputs=(anchor_embedding, positive_embedding, negative_embedding))
return outer_network
triplet_model = tripler_trainer(input_shape=(112,112,3))
triplets = triplet_model(triplet[0], triplet[1], triplet[2])
當我嘗試運行代碼時，出現以下錯誤：
Traceback (most recent call last):
File "C:/Users/G5205GK/Desktop/Working Dir/code/con_learning/main.py", line 35, in <module>
main()
File "C:/Users/G5205GK/Desktop/Working Dir/code/con_learning/main.py", line 31, in main
training_instance.train()
File "C:\Users\G5205GK\Desktop\Working Dir\code\con_learning\train.py", line 36, in train
anchor_embedding = self.model(triplet[0], triplet[1], triplet[2])
File "C:\Users\G5205GK\Anaconda3\envs\my_gpu_env\lib\site-packages\tensorflow\python\keras\engine\base_layer.py", line 1013, in __call__
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
File "C:\Users\G5205GK\Anaconda3\envs\my_gpu_env\lib\site-packages\tensorflow\python\keras\engine\input_spec.py", line 200, in assert_input_compatibility
raise ValueError('Layer ' layer_name ' expects '
ValueError: Layer model_1 expects 3 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor: shape=(112, 112, 3), dtype=float32,
任何人都可以請建議問題出在哪里。此外，提供的代碼針對堆疊溢位進行了簡化。所以它可能有語法問題。提前致謝。
uj5u.com熱心網友回復：
您應該使用方括號為模型提供多個輸入和輸出，因為您的模型需要輸入串列而不是輸入元組。試試這個：
import tensorflow as tf
def inner_model(act_func='relu', input_shape=(112,112,3)):
model_input = tf.keras.layers.Input(shape=input_shape)
x = tf.keras.layers.Conv2D(filters=16, kernel_size=3, padding='same', activation=act_func, name='Conv1')(model_input)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding='same', activation=act_func, name='Conv2')(x)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.Conv2D(filters=64, kernel_size=3, padding='same', activation=act_func, name='Conv3')(x)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.Conv2D(filters=128, kernel_size=3, padding='same', activation=act_func, name='Conv4')(x)
x = tf.keras.layers.MaxPool2D()(x)
x = tf.keras.layers.Conv2D(filters=256, kernel_size=3, padding='same', activation=act_func, name='Conv5')(x)
x = tf.keras.layers.GlobalAvgPool2D(name='GAP')(x)
output = tf.keras.layers.Dense(128, activation=act_func, name='Dense1')(x)
model = tf.keras.Model(inputs=model_input, outputs=output)
model.summary()
return model
def tripler_trainer(input_shape=(112,112,3)):
anchor_input = tf.keras.layers.Input(shape=input_shape)
positive_input = tf.keras.layers.Input(shape=input_shape)
negative_input = tf.keras.layers.Input(shape=input_shape)
embedder = inner_model(act_func='relu', input_shape=(112,112,3))
anchor_embedding = embedder(anchor_input)
positive_embedding = embedder(positive_input)
negative_embedding = embedder(negative_input)
outer_network = tf.keras.Model(inputs=[anchor_input, positive_input, negative_input],
outputs=[anchor_embedding, positive_embedding, negative_embedding])
return outer_network
triplet_model = tripler_trainer(input_shape=(112,112,3))
triplet1, triplet2, triplet3 = tf.random.normal((1,112,112,3)), tf.random.normal((1,112,112,3)), tf.random.normal((1,112,112,3))
triplets = triplet_model([triplet1, triplet2, triplet3])
print(triplets[0].shape, triplets[1].shape, triplets[2].shape)
# (1, 128) (1, 128) (1, 128)
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