File "E:\PyCharmFile\vgg\vgg19_classification.py", line 173, in <module> model = transfer.ResNet50_model(nb_classes=2, img_rows=image_size, img_cols=image_size, is_plot_model=True)

你的代码中调用了一个名为 `ResNet50_model` 的函数，该函数位于 `transfer` 模块中，并且需要传入四个参数：`nb_classes`、`img_rows`、`img_cols` 和 `is_plot_model`。请检查该函数的定义是否正确，并确保你已经正确地导入了 `transfer` 模块。此外，你还需要检查传入的参数是否正确。如果问题仍然存在，请提供更多的代码信息和错误提示。

相关问题

vgg16效果为什么比resnet50好

VGG16 and ResNet50 are both popular deep learning models used for image classification tasks. VGG16 has a simpler architecture compared to ResNet50, consisting of 16 layers with smaller convolution filters. ResNet50, on the other hand, has a more complex architecture with 50 layers and residual connections.

The effectiveness of a deep learning model depends on various factors such as the complexity of the problem, size of the dataset, training methodology, and hyperparameters. In some cases, VGG16 might perform better than ResNet50 due to the specific nature of the problem or dataset being used. However, in general, ResNet50 is considered to be a more powerful model due to its ability to handle deeper networks and overcome the vanishing gradient problem.

That being said, the performance of a model depends on the specific task and dataset at hand, and it is always recommended to try out multiple models and compare their performance before choosing the best one for a particular task.

use python to programing a garbage classification program

As an AI language model, I don't have the capability to write a full-fledged program in Python. However, I can provide some guidance on how you can program a garbage classification program in Python.

1. Collect Data: The first step is to collect and label the images that you will use to train your model. You can use online datasets or create your own.

2. Preprocess the Data: You need to preprocess the data by resizing the images, normalizing the pixel values, and splitting the data into training and testing sets.

3. Build the Model: You can use different deep learning frameworks such as TensorFlow or PyTorch to build your garbage classification model. You can use pre-trained models such as ResNet or VGGNet as a starting point.

4. Train the Model: You need to train the model on the training data by feeding the images into the model and adjusting the weights to minimize the loss.

5. Evaluate the Model: You need to evaluate the model on the testing data to see how well it performs. You can calculate metrics such as accuracy, precision, recall, and F1 score.

6. Use the Model: Once you have trained and evaluated the model, you can use it to classify new images of garbage.

Here is some sample code to get you started:

import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# define data directories
train_dir = 'path/to/training/data'
test_dir = 'path/to/testing/data'

# define data generators
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)

# define batch size and image size
batch_size = 32
img_size = (224, 224)

# define training and testing data generators
train_generator = train_datagen.flow_from_directory(
        train_dir,
        target_size=img_size,
        batch_size=batch_size,
        class_mode='categorical')

test_generator = test_datagen.flow_from_directory(
        test_dir,
        target_size=img_size,
        batch_size=batch_size,
        class_mode='categorical')

# define the model
model = tf.keras.applications.ResNet50(
        include_top=True,
        weights=None,
        input_shape=(224, 224, 3),
        classes=6)

# compile the model
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# train the model
model.fit(train_generator,
          epochs=10,
          validation_data=test_generator)

# evaluate the model
model.evaluate(test_generator)

This is just a basic example of how you can build a garbage classification program in Python. You can modify and improve the model according to your specific requirements.