. harmonic networks: deep translation and rotation equivariance. in: ieee co

音调网络是一种具有深度翻译和旋转等变性的神经网络模型。它被广泛应用于计算机视觉和图像处理任务中。

音调网络通过引入卷积和池化运算，使得网络具备了对平移变换的等变性。这意味着无论输入图像发生平移变换，网络输出仍然保持不变。这种平移等变性使得音调网络可以在不同位置捕捉到相同的特征，增强了网络对图像中局部细节的感知能力。

此外，音调网络还通过引入旋转等变性，使得网络具备对图像旋转变换的等变性。这意味着当输入图像发生旋转变换时，网络输出仍然保持不变。这种旋转等变性使得音调网络能够捕捉到具有不同旋转角度的相同物体的特征，提高了网络对物体形状的识别能力。

音调网络是基于谐波分析的思想发展而来的。谐波分析是一种用于分解信号和图像的方法，它将复杂的信号分解成多个简单的正弦波进行表示。音调网络利用这种分解思想，通过对不同频率的谐波进行卷积运算，实现对图像的多尺度分析。

总而言之，音调网络是一种具有深度翻译和旋转等变性的神经网络模型，通过引入卷积和池化运算，实现对图像的平移和旋转等变性。它在计算机视觉和图像处理任务中具有广泛应用前景。

相关问题

Task: - Download the UCM dataset from the official source or any reputable repository. - Preprocess the dataset by splitting it into training, validation, and test sets. Please use an 80-10-10 split, respectively. - Choose a suitable deep-learning algorithm for scene classification. You can use popular classifiers such as Convolutional Neural Networks (CNNs). - Implement the chosen algorithm in Python, using appropriate libraries. - Train the classifier using the training set and tune the hyperparameters using the validation set. - Evaluate the trained classifier on the test set using the following metrics: Accuracy: The overall classification accuracy. Precision: The ability to correctly classify positive samples. Recall: The ability to correctly identify positive samples. F1-score: The harmonic mean of precision and recall. Confusion Matrix: A table showing the number of correct and incorrect predictions for each class. Report: Prepare a one-page report summarizing the results of your scene classification experiment and the architecture of your network. Submit your Python code along with the one-page report summarizing the results. Make sure to organize your code and report in a clear and readable manner.

Sure, here's an outline of the steps you'll need to take to complete this task:

1. Download the UCM dataset from the official source or any reputable repository.
2. Preprocess the dataset by splitting it into training, validation, and test sets. Use an 80-10-10 split, respectively. You can use a library like scikit-learn to do this.
3. Choose a suitable deep-learning algorithm for scene classification. Convolutional Neural Networks (CNNs) are a popular choice for image classification tasks like this.
4. Implement the chosen algorithm in Python, using appropriate libraries such as TensorFlow or PyTorch.
5. Train the classifier using the training set and tune the hyperparameters using the validation set.
6. Evaluate the trained classifier on the test set using the following metrics: accuracy, precision, recall, F1-score, and confusion matrix. You can use libraries like scikit-learn to compute these metrics.
7. Prepare a one-page report summarizing the results of your scene classification experiment and the architecture of your network. Include any relevant information such as which hyperparameters you tuned and which ones you used for the final model.

Here's some sample code to get you started:

# Step 1: Download UCM dataset
# TODO: Download dataset and extract files

# Step 2: Preprocess dataset
from sklearn.model_selection import train_test_split

# TODO: Load dataset into memory
X_train_val, X_test, y_train_val, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.1, random_state=42)

# Step 3: Choose deep-learning algorithm
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

model = tf.keras.Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(256, 256, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Conv2D(256, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(256, activation='relu'),
    Dense(21, activation='softmax')
])

# Step 4: Implement algorithm in Python
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Step 5: Train classifier
history = model.fit(X_train, y_train, epochs=10, validation_data=(X_val, y_val))

# Step 6: Evaluate trained classifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix

y_pred = model.predict(X_test)
y_pred_classes = np.argmax(y_pred, axis=1)
y_test_classes = np.argmax(y_test, axis=1)

accuracy = accuracy_score(y_test_classes, y_pred_classes)
precision = precision_score(y_test_classes, y_pred_classes, average='macro')
recall = recall_score(y_test_classes, y_pred_classes, average='macro')
f1 = f1_score(y_test_classes, y_pred_classes, average='macro')
confusion_mat = confusion_matrix(y_test_classes, y_pred_classes)

print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1-score:", f1)
print("Confusion matrix:\n", confusion_mat)

# Step 7: Prepare report
# TODO: Write report summarizing results and network architecture

harmonic_func() missing 1 required positional argument: 'g'

As an AI language model, I cannot provide a specific solution without the context of the problem or the code snippet. However, the error message "harmonic_func() missing 1 required positional argument: 'g'" indicates that the function harmonic_func() requires an argument named 'g', which is not being provided when the function is called.

You may need to check the function definition to see what arguments it requires and make sure that all required arguments are being passed when calling the function.