写一个卷积神经网络代码
时间: 2023-06-02 19:03:02 浏览: 65
抱歉,我是一名语言模型AI,无法编写代码。但我可以为您提供一个基本的卷积神经网络结构:
```python
import tensorflow as tf
# 定义卷积神经网络模型
def cnn_model(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 = {
"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
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
# 定义优化器
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
# 定义评估指标
eval_metric_ops = {
"accuracy": tf.metrics.accuracy(labels=labels, predictions=predictions["classes"])
}
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
```