我收到错误，例如“图层“model_5”的输入 1 与图层不兼容：预期形状 =（无，224、224、3），找到形状 =（无，5）

Question

我正在尝试融合形状（299、299、3）、（224、224、3）的两个图像输入的特征，并且出现形状错误。

这是我的代码

    from tensorFlow.keras.applications.inception_v3 import InceptionV3
    from tensorflow.keras.applications.vgg16 import VGG16
    import tensorflow as tf
    from tensorflow.keras import layers, Input
    
    inp_pre_trained_model = InceptionV3( include_top=False)
    inp_pre_trained_model.trainable=False
    inp_input=tf.keras.Input(shape=(299,299,3),name="input_layer_inception_V3")
    inp_x=inp_pre_trained_model (inp_input)
    inp_x=layers.GlobalAveragePooling2D(name="global_average_pooling_layer_inception_v3")(inp_x)
    vgg_pre_trained_model = VGG16( include_top=False)
    vgg_pre_trained_model.trainable=False
    vgg_input=tf.keras.Input(shape=(224,224,3),name="input_layer_VGG_16")
    
    vgg_x=vgg_pre_trained_model(vgg_input)
    vgg_x=layers.GlobalAveragePooling2D(name="global_average_pooling_layer_vgg_16")(vgg_x)
    x=tf.keras.layers.concatenate([inp_x,vgg_x],axis=-1)
    x = tf.keras.layers.Flatten()(x)
    outputs=tf.keras.layers.Dense(5,activation="softmax", name= "output_layer") (x)
    model=tf.keras.Model(inputs=[inp_input,vgg_input],outputs=outputs)

    
    model.summary()

我的model总结

    Model: "model_9"
    __________________________________________________________________________________________________
     Layer (type)                   Output Shape         Param #     Connected to                     
    ==================================================================================================
     input_layer_inception_V3 (Inpu  [(None, 224, 224, 3  0          []                               
     tLayer)                        )]                                                                
                                                                                                      
     input_layer_VGG_16 (InputLayer  [(None, 299, 299, 3  0          []                               
     )                              )]                                                                
                                                                                                      
     inception_v3 (Functional)      (None, None, None,   21802784    ['input_layer_inception_V3[0][0]'
                                    2048)                            ]                                
                                                                                                      
     vgg16 (Functional)             (None, None, None,   14714688    ['input_layer_VGG_16[0][0]']     
                                    512)                                                              
                                                                                                      
     global_average_pooling_incepti  (None, 2048)        0           ['inception_v3[0][0]']           
     on (GlobalAveragePooling2D)                                                                      
                                                                                                      
     global_average_pooling_vgg (Gl  (None, 512)         0           ['vgg16[0][0]']                  
     obalAveragePooling2D)                                                                            
                                                                                                      
     concatenate_71 (Concatenate)   (None, 2560)         0           ['global_average_pooling_inceptio
                                                                     n[0][0]',                        
                                                                      'global_average_pooling_vgg[0][0
                                                                     ]']                              
                                                                                                      
     output_layer (Dense)           (None, 5)            12805       ['concatenate_71[0][0]']         
                                                                                                      
    ==================================================================================================
    Total params: 36,530,277
    Trainable params: 12,805
    Non-trainable params: 36,517,472

编译器

model.compile(loss="sparse_categorical_crossentropy",optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),metrics=["accuracy"])

train = tf.data.Dataset.zip((cache_train_data, ceced_train_data))  
test = tf.data.Dataset.zip((cache_test_data, ceced_test_data))  
train_dataset = train.prefetch(tf.data.AUTOTUNE)  
test_dataset = test.prefetch(tf.data.AUTOTUNE)

train_dataset, test_dataset

--->(<PrefetchDataset element_spec=((TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 5), dtype=tf. float32, name=None)), (TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 5), dtype=tf.float32,名称=无）））>，
<PrefetchDataset element_spec=((TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 5), dtype=tf.float32, name=None )), (TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 5), dtype=tf.float32, name=None)) )>)

适合 model

model_history = model.fit(train_dataset, 
                              steps_per_epoch=len(train_dataset),
                              epochs=3,
                          validation_data=test_dataset,
                           validation_steps=len(test_dataset))

错误ValueError：在用户代码中：

File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1051, in train_function  *
    return step_function(self, iterator)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1040, in step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1030, in run_step  **
    outputs = model.train_step(data)
File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 889, in train_step
    y_pred = self(x, training=True)
File "/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py", line 67, in error_handler
    raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py", line 264, in assert_input_compatibility
    raise ValueError(f'Input {input_index} of layer "{layer_name}" is '

ValueError: Input 1 of layer "model_9" is incompatible with the layer: expected shape=(None, 299, 299, 3), found shape=(None, 5)

Answer 1

您的拟合 function 有问题，与火车数据有关。 从 Keras文档中可以看到， fit可以采取以下 arguments：

Model.fit(
    x=None,
    y=None,
    batch_size=None,
    epochs=1,
    verbose="auto",
    callbacks=None,
    validation_split=0.0,
    validation_data=None,
    shuffle=True,
    class_weight=None,
    sample_weight=None,
    initial_epoch=0,
    steps_per_epoch=None,
    validation_steps=None,
    validation_batch_size=None,
    validation_freq=1,
    max_queue_size=10,
    workers=1,
    use_multiprocessing=False,
)

但是你传递的是一个 train_dataset，我认为它是一个将tf.data.Dataset和y_train保存x_train的 tf.data.Dataset。

为了修复错误，您应该将x与y分开并将它们作为 arguments 传递。

我认为这样的事情应该做：

for images, labels in train_dataset.take(-1):
    X_train = images.numpy()
    y_train = labels.numpy()

# doing the same for validation
for images, labels in test_dataset.take(-1):
    X_test = images.numpy()
    y_test = labels.numpy()

你想要这样的东西：

model_history = model.fit(x=X_train, y=y_train, steps_per_epoch=len(train_dataset), epochs=3, validation_data=(X_test, y_test), validation_steps=len(test_dataset))