用python详细实现3层cnn网络
时间: 2023-10-21 20:04:04 浏览: 42
好的,对于您的问题,我可以提供一个简单的实现例子。以下是一个实现3层CNN网络的Python代码示例:
```
import tensorflow as tf
# 定义一个卷积层
def conv_layer(input_tensor, filters, kernel_size, strides, padding='same', activation=tf.nn.relu):
layer = tf.layers.conv2d(inputs=input_tensor, filters=filters, kernel_size=kernel_size,
strides=strides, padding=padding, activation=activation)
return layer
# 定义一个池化层
def pool_layer(input_tensor):
layer = tf.layers.max_pooling2d(inputs=input_tensor, pool_size=2, strides=2)
return layer
# 定义一个3层CNN网络
def cnn_network(input_tensor):
conv1 = conv_layer(input_tensor, filters=32, kernel_size=3, strides=1)
pool1 = pool_layer(conv1)
conv2 = conv_layer(pool1, filters=64, kernel_size=3, strides=1)
pool2 = pool_layer(conv2)
conv3 = conv_layer(pool2, filters=128, kernel_size=3, strides=1)
pool3 = pool_layer(conv3)
flatten = tf.layers.flatten(pool3)
return flatten
# 初始化参数
input_image = tf.placeholder(tf.float32, [None, 32, 32, 3])
batch_size = 100
# 使用3层CNN网络进行训练
cnn_output = cnn_network(input_image)
dense = tf.layers.dense(inputs=cnn_output, units=64, activation=tf.nn.relu)
logits = tf.layers.dense(inputs=dense, units=10)
loss = tf.losses.softmax_cross_entropy(tf.one_hot(placeholder_label, depth=10), logits)
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
# 在训练集上训练
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(100):
batch_data, batch_label = load_batch_data(batch_size)
_, train_loss = sess.run([train_op, loss], feed_dict={input_image: batch_data, placeholder_label: batch_label})
print("Epoch %d, loss %f" % (i+1, train_loss))
```
以上就是一个简单的3层CNN网络的Python实现,您可以根据自己的需求对代码进行调整。如果您有任何其他问题,请随时问我。