介绍一下这段代码的Depthwise卷积层def get_data4EEGNet(kernels, chans, samples): K.set_image_data_format('channels_last') data_path = '/Users/Administrator/Desktop/project 5-5-1/' raw_fname = data_path + 'concatenated.fif' event_fname = data_path + 'concatenated.fif' tmin, tmax = -0.5, 0.5 #event_id = dict(aud_l=769, aud_r=770, foot=771, tongue=772) raw = io.Raw(raw_fname, preload=True, verbose=False) raw.filter(2, None, method='iir') events, event_id = mne.events_from_annotations(raw, event_id={'769': 1, '770': 2,'770': 3, '771': 4}) #raw.info['bads'] = ['MEG 2443'] picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False) epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False, picks=picks, baseline=None, preload=True, verbose=False) labels = epochs.events[:, -1] print(len(labels)) print(len(epochs)) #epochs.plot(block=True) X = epochs.get_data() * 250 y = labels X_train = X[0:144,] Y_train = y[0:144] X_validate = X[144:216, ] Y_validate = y[144:216] X_test = X[216:, ] Y_test = y[216:] Y_train = np_utils.to_categorical(Y_train - 1) Y_validate = np_utils.to_categorical(Y_validate - 1) Y_test = np_utils.to_categorical(Y_test - 1) X_train = X_train.reshape(X_train.shape[0], chans, samples, kernels) X_validate = X_validate.reshape(X_validate.shape[0], chans, samples, kernels) X_test = X_test.reshape(X_test.shape[0], chans, samples, kernels) return X_train, X_validate, X_test, Y_train, Y_validate, Y_test kernels, chans, samples = 1, 3, 251 X_train, X_validate, X_test, Y_train, Y_validate, Y_test = get_data4EEGNet(kernels, chans, samples) model = EEGNet(nb_classes=3, Chans=chans, Samples=samples, dropoutRate=0.5, kernLength=32, F1=8, D=2, F2=16, dropoutType='Dropout') model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) checkpointer = ModelCheckpoint(filepath='/Users/XXX/baseline.h5', verbose=1, save_best_only=True) class_weights = {0: 1, 1: 1, 2: 1, 3: 1} fittedModel = model.fit(X_train, Y_train, batch_size=2, epochs=100, verbose=2, validation_data=(X_validate, Y_validate), callbacks=[checkpointer], class_weight=class_weights) probs = model.predict(X_test) preds = probs.argmax(axis=-1) acc = np.mean(preds == Y_test.argmax(axis=-1)) print("Classification accuracy: %f " % (acc))
时间: 2023-06-16 15:06:02 浏览: 123
这段代码是用于对EEG数据进行分类的。首先,它读取了一个数据集,将数据集分成训练集、验证集和测试集。然后,它使用EEGNet模型对数据进行训练和验证,并输出分类准确率。其中,EEGNet模型是一种针对EEG数据设计的卷积神经网络模型,它包括了深度可分离卷积层、批归一化层和Dense层。这个模型的输入是EEG信号,输出是分类结果。在训练过程中,该代码使用了dropout技术和class weight调节技术,以提高模型的鲁棒性和泛化性能。最后,该代码输出了测试集上的分类准确率。
相关问题
def mean_shift(xs: np.ndarray, num_iter: int = 50, k_type: str = 'rbf', bandwidth: float = 0.1) -> np.ndarray: """ Implement the mean-shift algorithm :param xs: a set of samples with size (N, D), where N is the number of samples, D is the dimension of features :param num_iter: the number of iterations :param k_type: the type of kernels, including 'rbf', 'gate', 'triangle', 'linear' :param bandwidth: the hyperparameter controlling the width of rbf/gate/triangle kernels :return: the estimated means with size (N, D) """ # TODO: change the code below and implement the mean-shift algorithm return
Sure! Here's one possible implementation of the mean-shift algorithm:
```python
def mean_shift(xs: np.ndarray, num_iter: int = 50, k_type: str = 'rbf', bandwidth: float = 0.1) -> np.ndarray:
"""
Implement the mean-shift algorithm
:param xs: a set of samples with size (N, D), where N is the number of samples, D is the dimension of features
:param num_iter: the number of iterations
:param k_type: the type of kernels, including 'rbf', 'gate', 'triangle', 'linear'
:param bandwidth: the hyperparameter controlling the width of rbf/gate/triangle kernels
:return:
the estimated means with size (N, D)
"""
N, D = xs.shape
means = xs.copy()
for i in range(num_iter):
for j, x in enumerate(xs):
# compute the kernel weights
if k_type == 'rbf':
w = np.exp(-0.5 * np.sum((x - means) ** 2, axis=1) / bandwidth ** 2)
elif k_type == 'gate':
w = np.exp(-np.sqrt(np.sum((x - means) ** 2, axis=1)) / bandwidth)
elif k_type == 'triangle':
w = np.maximum(0, 1 - np.sqrt(np.sum((x - means) ** 2, axis=1)) / bandwidth)
elif k_type == 'linear':
w = np.sum((x - means) * xs, axis=1) / (np.linalg.norm(x - means, axis=1) * np.linalg.norm(x))
else:
raise ValueError("Unsupported kernel type: {}".format(k_type))
# update the mean by weighted average
means[j] = np.sum(w[:, np.newaxis] * xs, axis=0) / np.sum(w)
return means
```
In this implementation, we start with the initial means being the same as the input samples, and then iteratively update the means by computing the kernel weights between each sample and the current means, and then computing the weighted average of the samples using these weights. The kernel can be selected using the `k_type` parameter, and the kernel width can be controlled by the `bandwidth` parameter. The number of iterations can be controlled by the `num_iter` parameter. Finally, the estimated means are returned.
Continue to refine the following code and don't add any other packages but numpy: def mean_shift2(xs: np.ndarray, num_iter: int = 50, k_type: str = 'rbf', bandwidth: float = 0.1) -> np.ndarray: """ Implement a variant of mean-shift algorithm, with unchanged kernel matrix :param xs: a set of samples with size (N, D), where N is the number of samples, D is the dimension of features :param num_iter: the number of iterations :param k_type: the type of kernels, including 'rbf', 'gate', 'triangle', 'linear' :param bandwidth: the hyperparameter controlling the width of rbf/gate/triangle kernels :return: the estimated means with size (N, D) """ # TODO: change the code below and implement the modified mean-shift return xs
Here is a possible implementation of the modified mean-shift algorithm:
```
def mean_shift2(xs: np.ndarray, num_iter: int = 50, k_type: str = 'rbf', bandwidth: float = 0.1) -> np.ndarray:
"""Implement a variant of mean-shift algorithm, with unchanged kernel matrix
:param xs: a set of samples with size (N, D), where N is the number of samples, D is the dimension of features
:param num_iter: the number of iterations
:param k_type: the type of kernels, including 'rbf', 'gate', 'triangle', 'linear'
:param bandwidth: the hyperparameter controlling the width of rbf/gate/triangle kernels
:return: the estimated means with size (N, D)
"""
N, D = xs.shape
# Initialize the means as the input samples
ms = xs.copy()
# Define the kernel function
if k_type == 'rbf':
k = lambda x, y: np.exp(-0.5 * np.linalg.norm(x - y)**2 / bandwidth**2)
elif k_type == 'gate':
k = lambda x, y: np.exp(-0.5 * np.abs(x - y) / bandwidth)
elif k_type == 'triangle':
k = lambda x, y: np.maximum(0, 1 - np.abs(x - y) / bandwidth)
elif k_type == 'linear':
k = lambda x, y: np.dot(x, y)
else:
raise ValueError('Unknown kernel type')
# Iterate to find the means
for _ in range(num_iter):
# Compute the kernel matrix
K = np.zeros((N, N))
for i in range(N):
for j in range(i, N):
K[i, j] = K[j, i] = k(xs[i], xs[j])
# Compute the weights and the updated means
weights = K.sum(axis=1)
ms_new = np.zeros((N, D))
for i in range(N):
ms_new[i] = (K[i, :, np.newaxis] * xs).sum(axis=0) / weights[i]
# Check for convergence
if np.allclose(ms, ms_new, rtol=1e-5):
break
ms = ms_new
return ms
```
In this implementation, we first initialize the means as the input samples. Then we define the kernel function based on the input `k_type` and `bandwidth`. We use a nested loop to compute the kernel matrix, which has size (N, N). We then use the kernel matrix to compute the weights and the updated means for each sample. We repeat this process for `num_iter` iterations or until the means converge. Finally, we return the estimated means. Note that this implementation only uses numpy and does not rely on any other packages.