ValueError：层“dense_2”需要 1 个输入，但它收到了 2 个输入张量

Question

我无法加载我的模型，它一直说错误
ValueError：层“dense_2”需要 1 个输入，但它收到了 2 个输入张量。收到的输入：[，]

这是我的代码

image_generator = ImageDataGenerator(
    rescale=1./255,
    rotation_range=20,
    zoom_range=0.2,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True,
    validation_split=0.2
)

train_dataset = image_generator.flow_from_directory(
    directory=path_to_dataset,
    target_size=(224, 224),
    batch_size=32,
    subset='training'
)

validation_dataset = image_generator.flow_from_directory(
    directory=path_to_dataset,
    target_size=(224, 224),
    batch_size=32,
    subset='validation'
)

# Menentukan jumlah kelas (num_classes) berdasarkan jumlah subfolder dalam dataset
num_classes = len(train_dataset.class_indices)

from tensorflow.keras.applications.mobilenet import MobileNet

# Load the MobileNet model
pre_trained_model = tf.keras.applications.MobileNetV2(input_shape=(224, 224, 3),
                                                      include_top=False,
                                                      weights='imagenet')


pre_trained_model.summary()

# Print dataset information for debugging
print(f"Training dataset shape: {train_dataset.image_shape}")
print(f"Validation dataset shape: {validation_dataset.image_shape}")

pre_trained_model.trainable = False

# Menambahkan layer kustom di atas model pre-trained
model = tf.keras.Sequential([
    pre_trained_model,
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(1024, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(num_classes, activation='softmax') 
])

# Compile model
#from tensorflow.keras.optimizers import RMSprop
model.compile(optimizer=Adam(learning_rate=0.0001),
              loss='categorical_crossentropy',
              metrics=['accuracy'])

 batch=40
 history = model.fit(train_dataset,
               validation_data=validation_dataset,
               epochs=20,
               steps_per_epoch = train_dataset.samples//batch,
               validation_steps = validation_dataset.samples//batch,
               verbose = 1
           )

# Load the model
model_save_path = '/content/drive/MyDrive/Machine Learning/saved_models/model_plastik.h5'

# Load the model, ensuring it's compiled if needed
loaded_model = tf.keras.models.load_model(model_save_path) 

# Now you can modify the loaded model if necessary
# For example, if you want to extract a sub-model:
input_layer_index = 0  # Replace with the actual index
dense_2_index = 3  # Replace with the actual index
loaded_model = tf.keras.models.Model(inputs=loaded_model.layers[input_layer_index].input, 
                                     outputs=loaded_model.layers[dense_2_index].output)

# Check the configuration of the loaded model
for i, layer in enumerate(loaded_model.layers):
    print(f"Layer {i}: {layer.name} - Input shape: {layer.input_shape} - Output Shape: {layer.output_shape}")

print("Revised model loaded successfully.")

我尝试加载模型，我希望它加载到测试

Answer 1

有同样的问题，只需尝试使用功能 API 实现相同的架构即可。