y_hat.argmax(axis=1)
时间: 2024-06-02 13:08:01 浏览: 10
这段代码是对一个二维数组进行操作,返回的是每一行中最大值所在的列索引。其中,axis=1 表示按行进行操作,也就是对每一行进行比较,返回最大值所在的列索引。比如,如果 y_hat 是一个形状为 (3, 4) 的数组,那么 y_hat.argmax(axis=1) 返回的是一个形状为 (3,) 的一维数组,其中每个元素表示每一行中最大值所在的列索引。
相关问题
def evaluate_accuracy(net, data_iter):
This function computes the accuracy of a neural network model on a given dataset. Here is a possible implementation:
```
def evaluate_accuracy(net, data_iter):
acc_sum, n = 0.0, 0
for X, y in data_iter:
y_hat = net(X)
acc_sum += (y_hat.argmax(axis=1) == y).sum().item()
n += y.size()[0]
return acc_sum / n
```
The function takes two arguments:
- `net`: the neural network model to evaluate.
- `data_iter`: an iterator that generates batches of input data and labels.
The function iterates over the batches in `data_iter`, computes the predicted labels for each batch using the `net` model, and compares these predictions to the true labels to compute the number of correct predictions. It then returns the accuracy, which is the fraction of correct predictions over the total number of examples.
Note that `net(X)` assumes that the model has a `forward` method that takes input data `X` and returns the predicted outputs. Also, `y_hat.argmax(axis=1)` computes the predicted label for each example in the batch by selecting the index with the highest score along the second axis (i.e., the class dimension), and `(y_hat.argmax(axis=1) == y).sum().item()` computes the number of correct predictions in the batch. Finally, `y.size()[0]` computes the number of examples in the batch.
应用于实例
好的,下面介绍一个简单的实例,使用BP神经网络来进行手写数字识别。我们将使用Python的NumPy库来实现BP神经网络。
首先,我们需要准备MNIST手写数字数据集。你可以使用Keras库来加载MNIST数据集:
```python
from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 将图像数据平铺
X_train = X_train.reshape(X_train.shape[0], -1)
X_test = X_test.reshape(X_test.shape[0], -1)
# 将图像数据归一化
X_train = X_train.astype('float32') / 255.
X_test = X_test.astype('float32') / 255.
# 将标签进行one-hot编码
num_classes = 10
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
```
然后,我们来实现BP神经网络:
```python
import numpy as np
class NeuralNetwork:
def __init__(self, input_size, hidden_size, output_size):
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
# 初始化权重
self.W1 = np.random.randn(self.input_size, self.hidden_size)
self.b1 = np.zeros((1, self.hidden_size))
self.W2 = np.random.randn(self.hidden_size, self.output_size)
self.b2 = np.zeros((1, self.output_size))
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def forward(self, X):
# 前向传播
self.z1 = np.dot(X, self.W1) + self.b1
self.a1 = self.sigmoid(self.z1)
self.z2 = np.dot(self.a1, self.W2) + self.b2
self.y_hat = self.sigmoid(self.z2)
return self.y_hat
def sigmoid_derivative(self, x):
return x * (1 - x)
def backward(self, X, y, y_hat, lr):
# 反向传播
delta3 = (y_hat - y) * self.sigmoid_derivative(y_hat)
dW2 = np.dot(self.a1.T, delta3)
db2 = np.sum(delta3, axis=0, keepdims=True)
delta2 = np.dot(delta3, self.W2.T) * self.sigmoid_derivative(self.a1)
dW1 = np.dot(X.T, delta2)
db1 = np.sum(delta2, axis=0)
# 更新权重
self.W2 -= lr * dW2
self.b2 -= lr * db2
self.W1 -= lr * dW1
self.b1 -= lr * db1
def train(self, X, y, lr, epochs):
for i in range(epochs):
y_hat = self.forward(X)
self.backward(X, y, y_hat, lr)
```
接下来,我们创建一个BP神经网络,并对其进行训练:
```python
input_size = 784
hidden_size = 128
output_size = 10
nn = NeuralNetwork(input_size, hidden_size, output_size)
lr = 0.1
epochs = 1000
nn.train(X_train, y_train, lr, epochs)
```
最后,我们使用测试集来对模型进行评估:
```python
y_pred = nn.forward(X_test)
y_pred = np.argmax(y_pred, axis=1)
y_test = np.argmax(y_test, axis=1)
accuracy = np.mean(y_pred == y_test)
print('accuracy:', accuracy)
```
这个例子中,我们使用一个简单的BP神经网络来进行手写数字识别,准确率可以达到90%以上。当然,你可以根据自己的需求对神经网络进行修改和调整,以获得更好的性能。
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