写一个三输入三输出神经网络预测代码
时间: 2023-05-26 19:05:37 浏览: 55
很抱歉,我是一个语言模型AI,无法提供代码,以下是一个基础的三输入三输出的神经网络预测的代码示例,提供给您参考:
```
import tensorflow as tf
# 定义输入和输出的维度大小
input_size = 3
output_size = 3
# 定义神经网络结构
inputs = tf.placeholder(tf.float32, [None, input_size])
weights1 = tf.Variable(tf.random_normal([input_size, 10]))
bias1 = tf.Variable(tf.random_normal([10]))
layer1 = tf.nn.relu(tf.matmul(inputs, weights1) + bias1)
weights2 = tf.Variable(tf.random_normal([10, 10]))
bias2 = tf.Variable(tf.random_normal([10]))
layer2 = tf.nn.relu(tf.matmul(layer1, weights2) + bias2)
weights3 = tf.Variable(tf.random_normal([10, output_size]))
bias3 = tf.Variable(tf.random_normal([output_size]))
outputs = tf.matmul(layer2, weights3) + bias3
# 定义损失函数和优化器
targets = tf.placeholder(tf.float32, [None, output_size])
loss = tf.reduce_mean(tf.square(outputs - targets))
optimizer = tf.train.AdamOptimizer(0.01).minimize(loss)
# 定义训练数据和测试数据
train_data = [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4], [0.3, 0.4, 0.5]]
train_targets = [[0.2, 0.3, 0.4], [0.3, 0.4, 0.5], [0.4, 0.5, 0.6]]
test_data = [[0.4, 0.5, 0.6], [0.5, 0.6, 0.7], [0.6, 0.7, 0.8]]
test_targets = [[0.5, 0.6, 0.7], [0.6, 0.7, 0.8], [0.7, 0.8, 0.9]]
# 定义训练函数和测试函数
def train():
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(1000):
_, loss_val = sess.run([optimizer, loss], feed_dict={inputs: train_data, targets: train_targets})
if i % 100 == 0:
print('Step:', i, 'Loss:', loss_val)
def predict():
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('./'))
pred_val = sess.run(outputs, feed_dict={inputs: test_data})
print('Prediction:', pred_val)
# 进行训练和测试
train()
predict()
```
上述代码中,定义了一个三层的神经网络结构,其中输入层为3个神经元,输出层也为3个神经元。训练数据和测试数据都是3个样本,每个样本有3个输入和3个输出。在训练函数中,使用Adam优化器进行模型训练,每100步输出一次损失。在预测函数中,使用已经训练好的模型进行预测,并输出预测结果。