数据集和标签都准备好了，怎么用卷积神经网络进行分类识别，加入混淆矩阵python代码

首先，需要导入相应的库：

import numpy as np
import tensorflow as tf
from tensorflow import keras
from sklearn.metrics import confusion_matrix

然后，需要加载数据集和标签，并进行预处理：

# 加载数据集和标签
train_data = np.load('train_data.npy')
train_labels = np.load('train_labels.npy')
test_data = np.load('test_data.npy')
test_labels = np.load('test_labels.npy')

# 数据预处理
train_data = train_data.astype('float32') / 255
test_data = test_data.astype('float32') / 255
train_labels = keras.utils.to_categorical(train_labels)
test_labels = keras.utils.to_categorical(test_labels)

接下来，构建卷积神经网络模型：

model = keras.Sequential([
    keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=train_data.shape[1:]),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu'),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Flatten(),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

然后，编译模型并训练：

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels, epochs=10, batch_size=32, validation_data=(test_data, test_labels))

最后，使用测试集对模型进行评估，并输出混淆矩阵：

# 对测试集进行评估
test_loss, test_acc = model.evaluate(test_data, test_labels)
print('Test accuracy:', test_acc)

# 输出混淆矩阵
test_pred = model.predict(test_data)
test_pred_labels = np.argmax(test_pred, axis=1)
test_true_labels = np.argmax(test_labels, axis=1)
confusion_mtx = confusion_matrix(test_true_labels, test_pred_labels)
print(confusion_mtx)

完整代码如下：

import numpy as np
import tensorflow as tf
from tensorflow import keras
from sklearn.metrics import confusion_matrix

# 加载数据集和标签
train_data = np.load('train_data.npy')
train_labels = np.load('train_labels.npy')
test_data = np.load('test_data.npy')
test_labels = np.load('test_labels.npy')

# 数据预处理
train_data = train_data.astype('float32') / 255
test_data = test_data.astype('float32') / 255
train_labels = keras.utils.to_categorical(train_labels)
test_labels = keras.utils.to_categorical(test_labels)

# 构建卷积神经网络模型
model = keras.Sequential([
    keras.layers.Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=train_data.shape[1:]),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Conv2D(64, (3, 3), padding='same', activation='relu'),
    keras.layers.MaxPooling2D((2, 2)),
    keras.layers.Flatten(),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

# 编译模型并训练
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels, epochs=10, batch_size=32, validation_data=(test_data, test_labels))

# 对测试集进行评估
test_loss, test_acc = model.evaluate(test_data, test_labels)
print('Test accuracy:', test_acc)

# 输出混淆矩阵
test_pred = model.predict(test_data)
test_pred_labels = np.argmax(test_pred, axis=1)
test_true_labels = np.argmax(test_labels, axis=1)
confusion_mtx = confusion_matrix(test_true_labels, test_pred_labels)
print(confusion_mtx)