TensorFlow 构建卷积神经网络

TensorFlow 是一个开源的机器学习框架，可以用于构建卷积神经网络（Convolutional Neural Networks, CNNs）。下面是一个使用 TensorFlow 构建卷积神经网络的基本步骤：

1. 导入 TensorFlow 库:

import tensorflow as tf

2. 定义输入数据的占位符:

x = tf.placeholder(tf.float32, shape=[None, input_height, input_width, input_channels], name='x')
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')

3. 定义卷积层:

conv_layer = tf.layers.conv2d(inputs=x,
                              filters=num_filters,
                              kernel_size=filter_size,
                              strides=stride_size,
                              padding='valid',
                              activation=tf.nn.relu)

4. 定义池化层:

pool_layer = tf.layers.max_pooling2d(inputs=conv_layer,
                                     pool_size=pool_size,
                                     strides=stride_size)

5. 添加全连接层:

flatten_layer = tf.layers.flatten(inputs=pool_layer)
fc_layer = tf.layers.dense(inputs=flatten_layer, units=num_fc_units, activation=tf.nn.relu)

6. 添加输出层:

output_layer = tf.layers.dense(inputs=fc_layer, units=num_classes, activation=None)
y_pred = tf.nn.softmax(output_layer)

7. 定义损失函数:

cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=output_layer, labels=y_true)
loss = tf.reduce_mean(cross_entropy)

8. 定义优化器并