使用Conv2d实现批量维度中的Tensorflow开关用例

import tensorflow as tf

# 定义输入数据
input_data = tf.placeholder(tf.float32, shape=[None, height, width, channels])

# 定义卷积核
filter_weights = tf.Variable(tf.truncated_normal([filter_height, filter_width, input_channels, output_channels], stddev=0.1))
filter_bias = tf.Variable(tf.constant(0.1, shape=[output_channels]))

# 批量维度中的卷积操作
conv = tf.nn.conv2d(input_data, filter_weights, strides=[1, stride_height, stride_width, 1], padding='SAME')
conv_with_bias = tf.nn.bias_add(conv, filter_bias)

# 使用激活函数
relu = tf.nn.relu(conv_with_bias)

# 定义池化操作
pool = tf.nn.max_pool(relu, ksize=[1, pool_height, pool_width, 1], strides=[1, pool_stride_height, pool_stride_width, 1], padding='SAME')

# 定义全连接层
reshape = tf.reshape(pool, shape=[-1, reshape_size])
fully_connected = tf.layers.dense(reshape, num_classes)

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=fully_connected))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)

# 创建会话并运行
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    # 进行训练和测试等操作