卷积神经网络简单代码

卷积神经网络（Convolutional Neural Network，CNN）是一种常见的深度学习模型，广泛应用于图像识别、语音识别等领域。下面是一个简单的CNN模型代码示例：

import tensorflow as tf

# 定义CNN模型
def cnn_model_fn(features, labels, mode):
    # 输入层
    input_layer = tf.reshape(features["x"], [-1, 28, 28, 1])
    # 卷积层1
    conv1 = tf.layers.conv2d(
        inputs=input_layer,
        filters=32,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu)
    # 池化层1
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
    # 卷积层2
    conv2 = tf.layers.conv2d(
        inputs=pool1,
        filters=64,
        kernel_size=[5, 5],
        padding="same",
        activation=tf.nn.relu)
    # 池化层2
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
    # 全连接层
    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
    dropout = tf.layers.dropout(inputs=dense, rate=0.4, training=mode == tf.estimator.ModeKeys.TRAIN)
    # 输出层
    logits = tf.layers.dense(inputs=dropout, units=10)

    predictions = {
        # 生成预测结果
        "classes": tf.argmax(input=logits, axis=1),
        "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
    }
    if mode == tf.estimator.ModeKeys.PREDICT:
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

    # 定义损失函数和优化器
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
    train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())

    # 定义评估指标
    eval_metric_ops = {
        "accuracy": tf.metrics.accuracy(labels=labels,
                                         predictions=predictions["classes"])
    }
    return tf.estimator.EstimatorSpec(mode=mode,
                                      predictions=predictions,
                                      loss=loss,
                                      train_op=train_op,
                                      eval_metric_ops=eval_metric_ops)

# 加载MNIST数据集
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
train_data = mnist.train.images  # Returns np.array
train_labels = np.asarray(mnist.train.labels, dtype=np.int32)
eval_data = mnist.test.images  # Returns np.array
eval_labels = np.asarray(mnist.test.labels, dtype=np.int32)

# 创建Estimator
mnist_classifier = tf.estimator.Estimator(model_fn=cnn_model_fn)

# 训练模型
train_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": train_data},
    y=train_labels,
    batch_size=100,
    num_epochs=None,
    shuffle=True)
mnist_classifier.train(input_fn=train_input_fn, steps=20000)

# 评估模型
eval_input_fn = tf.estimator.inputs.numpy_input_fn(
    x={"x": eval_data},
    y=eval_labels,
    num_epochs=1,
    shuffle=False)
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)

以上代码使用TensorFlow实现了一个简单的CNN模型，用于对MNIST手写数字数据集进行图像分类。在这个模型中，我们使用了两个卷积层、两个池化层和一个全连接层，最后输出10个类别的概率分布。这个模型可以在TensorFlow中进行训练和评估。