使用深度神经网络对手写数字图像进行识别并打印结果的代码实现，涉及数据加载、预处理及可视化

首先，我们可以使用深度神经网络来实现手写体数字识别。以下是一个示例代码： ```python import gzip import numpy as np import matplotlib.pyplot as plt # 加载数据 def load_data(): with gzip.open('mnist.pkl.gz', 'rb') as f: train_data, valid_data, test_data = pickle.load(f, encoding='latin1') return train_data, valid_data, test_data # 可视化图像 def visualize_data(images, labels): fig, axes = plt.subplots(2, 5, figsize=(10, 4)) for i, ax in enumerate(axes.flat): ax.imshow(images[i].reshape(28, 28), cmap='binary') ax.set_xticks([]) ax.set_yticks([]) ax.set_title(labels[i]) plt.show() # 标准化图像像素 def normalize_data(data): mean = np.mean(data) std = np.std(data) normalized_data = (data - mean) / std return normalized_data # 打包样本 def pack_samples(images, labels): samples = [(image, label) for image, label in zip(images, labels)] return samples # 设计神经网络参数 input_size = 784 # 输入层大小（28x28） hidden_size = 128 # 隐藏层大小 output_size = 10 # 输出层大小（0-9的数字） # 加载数据 train_data, valid_data, test_data = load_data() # 可视化部分数据 visualize_data(train_data[0][:10], train_data[1][:10]) # 标准化数据 train_images = normalize_data(train_data[0]) train_labels = train_data[1] # 打包样本 train_samples = pack_samples(train_images, train_labels) # MLP神经网络 class MLP: def __init__(self, input_size, hidden_size, output_size): self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size # 初始化权重和偏置 self.weights1 = np.random.randn(self.input_size, self.hidden_size) self.bias1 = np.zeros(self.hidden_size) self.weights2 = np.random.randn(self.hidden_size, self.output_size) self.bias2 = np.zeros(self.output_size) def forward(self, x): # 前向传播 self.hidden_layer = np.dot(x, self.weights1) + self.bias1 self.hidden_activation = self.sigmoid(self.hidden_layer) self.output_layer = np.dot(self.hidden_activation, self.weights2) + self.bias2 self.output_activation = self.softmax(self.output_layer) return self.output_activation def sigmoid(self, x): return 1 / (1 + np.exp(-x)) def softmax(self, x): exps = np.exp(x - np.max(x)) return exps / np.sum(exps, axis=1, keepdims=True) def train(self, x, y, learning_rate=0.1): # 反向传播和参数更新 output = self.forward(x) error = output - y grad_output = error / x.shape[0] grad_weights2 = np.dot(self.hidden_activation.T, grad_output) grad_bias2 = np.sum(grad_output, axis=0) grad_hidden = np.dot(grad_output, self.weights2.T) * self.sigmoid_derivative(self.hidden_layer) grad_weights1 = np.dot(x.T, grad_hidden) grad_bias1 = np.sum(grad_hidden, axis=0) self.weights2 -= learning_rate * grad_weights2 self.bias2 -= learning_rate * grad_bias2 self.weights1 -= learning_rate * grad_weights1 self.bias1 -= learning_rate * grad_bias1 def sigmoid_derivative(self, x): return self.sigmoid(x) * (1 - self.sigmoid(x)) # 创建MLP模型 model = MLP(input_size, hidden_size, output_size) # 训练模型 for epoch in range(10): for image, label in train_samples: image = image.reshape(1, -1) label = np.eye(output_size)[label].reshape(1, -1) model.train(image, label) # 测试模型 test_images = normalize_data(test_data[0]) test_labels = test_data[1] correct = 0 for image, label in zip(test_images, test_labels): image = image.reshape(1, -1) output = model.forward(image) prediction = np.argmax(output) if prediction == label: correct += 1 accuracy = correct / len(test_labels) print("测试集准确率：", accuracy) ``` 这段代码使用了MNIST数据集进行手写体数字识别。首先，通过`load_data`函数加载数据集，然后使用`visualize_data`函数可视化部分数据。接下来，使用`normalize_data`函数对图像像素进行标准化处理。然后，使用`pack_samples`函数将图像和标签打包成样本。接着，定义了一个MLP类作为神经网络模型，包括前向传播、反向传播和参数更新的方法。最后，创建MLP模型并进行训练和测试。 请注意，这只是一个简单的示例代码，实际应用中可能需要进行更多的调优和改进。