基于深度神经网络的手写体数字识别代码实现及结果打印，含数据加载、预处理与可视化

入层的节点数、隐藏层的节点数、输出层的节点数、激活函数的选择、损失函数的选择等。然后使用tensorflow或者pytorch等深度学习框架搭建神经网络模型，进行训练和测试。 以下是一个示例代码，用于实现手写体数字识别： ```python import gzip import numpy as np import matplotlib.pyplot as plt import tensorflow as tf # 加载数据 def load_data(): with gzip.open('mnist_data.gz', 'rb') as f: data = np.frombuffer(f.read(), np.uint8, offset=16) data = data.reshape(-1, 28, 28) return data # 可视化图像 def visualize_data(data): plt.figure(figsize=(10, 10)) for i in range(25): plt.subplot(5, 5, i+1) plt.imshow(data[i], cmap='gray') plt.axis('off') plt.show() # 数据预处理 def preprocess_data(data): data = data.astype('float32') / 255.0 data = (data - np.mean(data)) / np.std(data) return data # 打包数据 def pack_data(data): packed_data = [] for i in range(len(data)): packed_data.append((data[i], i % 10)) # 假设标签就是数字本身 return packed_data # 构建神经网络模型 def build_model(): model = tf.keras.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) return model # 训练模型 def train_model(model, data): x_train = np.array([sample[0] for sample in data]) y_train = np.array([sample[1] for sample in data]) model.fit(x_train, y_train, epochs=10, batch_size=32) # 测试模型 def test_model(model, data): x_test = np.array([sample[0] for sample in data]) y_test = np.array([sample[1] for sample in data]) loss, accuracy = model.evaluate(x_test, y_test) print('Test loss:', loss) print('Test accuracy:', accuracy) # 主函数 def main(): data = load_data() visualize_data(data) preprocessed_data = preprocess_data(data) packed_data = pack_data(preprocessed_data) model = build_model() train_model(model, packed_data) test_model(model, packed_data) if __name__ == '__main__': main() ``` 请注意，上述代码仅为示例，实际应用中可能需要根据具体情况进行调整和优化。