a=tf.nn.sigmoid(tf.matmul(x,w1)+b1)#sigmoid激活函数 y=tf.nn.sigmoid(tf.matmul(a,w2)+b2) mse = tf.reduce_mean(tf.square(y_ - y))#损失函数采用均方误差 #train_step = tf.train.AdamOptimizer(eta).minimize(mse)#Adam算法 train_step = tf.train.GradientDescentOptimizer(eta).minimize(mse)#梯度下降法 哪一步是反向传播误差
时间: 2024-04-28 09:26:28 浏览: 6
在这段代码中,反向传播误差是由优化器实现的。在这里,我们可以看到两个优化器:`tf.train.AdamOptimizer()` 和 `tf.train.GradientDescentOptimizer()`。在训练过程中,我们使用 `train_step` 来更新权重参数。在更新权重参数的过程中,优化器会计算出梯度,并根据梯度方向来更新权重参数,这就是反向传播误差的过程。因此,在这段代码中,两个优化器实现的 `train_step` 步骤都包含了反向传播误差的计算。
相关问题
使用tensorflow实现三层的bp神经网络
好的,以下是使用 TensorFlow 实现三层的 BP 神经网络的代码:
```python
import tensorflow as tf
# 定义输入层、隐藏层和输出层的神经元个数
input_size = 784
hidden_size = 256
output_size = 10
# 定义输入、输出和隐藏层的占位符
X = tf.placeholder(tf.float32, [None, input_size])
y = tf.placeholder(tf.float32, [None, output_size])
hidden_layer = tf.placeholder(tf.float32, [None, hidden_size])
# 定义权重和偏置变量
W1 = tf.Variable(tf.random_normal([input_size, hidden_size]))
b1 = tf.Variable(tf.zeros([hidden_size]))
W2 = tf.Variable(tf.random_normal([hidden_size, output_size]))
b2 = tf.Variable(tf.zeros([output_size]))
# 定义前向传播计算图
hidden_layer = tf.nn.sigmoid(tf.matmul(X, W1) + b1)
output_layer = tf.nn.softmax(tf.matmul(hidden_layer, W2) + b2)
# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output_layer, labels=y))
optimizer = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# 定义准确率计算图
correct_prediction = tf.equal(tf.argmax(output_layer, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 训练模型
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(optimizer, feed_dict={X: batch_xs, y: batch_ys})
if i % 100 == 0:
acc = sess.run(accuracy, feed_dict={X: mnist.test.images, y: mnist.test.labels})
print("Step {}, accuracy: {}".format(i, acc))
```
这里我们以 MNIST 手写数字识别数据集为例进行训练和测试。代码中的 `tf.nn.sigmoid` 函数是隐藏层的激活函数,`tf.nn.softmax` 函数是输出层的激活函数,`tf.nn.softmax_cross_entropy_with_logits` 函数是交叉熵损失函数,`tf.train.GradientDescentOptimizer` 函数是梯度下降优化器。在训练过程中,我们使用 `mnist.train.next_batch(100)` 函数从训练数据集中随机抽取 100 个样本进行训练。
给我一段GAN模型的代码
这是一个GAN模型的示例代码:import numpy as np
import tensorflow as tf# 定义输入样本
X_inputs = tf.placeholder(shape=[None, 2], dtype=tf.float32)# 定义生成器
def generator(inputs):
w1 = tf.Variable(tf.random_normal([2, 4]))
b1 = tf.Variable(tf.random_normal([4]))
h1 = tf.nn.sigmoid(tf.matmul(inputs, w1) + b1)
w2 = tf.Variable(tf.random_normal([4, 2]))
b2 = tf.Variable(tf.random_normal([2]))
outputs = tf.nn.sigmoid(tf.matmul(h1, w2) + b2)
return outputs# 定义判别器
def discriminator(inputs):
w1 = tf.Variable(tf.random_normal([2, 4]))
b1 = tf.Variable(tf.random_normal([4]))
h1 = tf.nn.sigmoid(tf.matmul(inputs, w1) + b1)
w2 = tf.Variable(tf.random_normal([4, 1]))
b2 = tf.Variable(tf.random_normal([1]))
outputs = tf.nn.sigmoid(tf.matmul(h1, w2) + b2)
return outputs# 定义生成器和判别器
generator_model = generator(X_inputs)
discriminator_model_real = discriminator(X_inputs)
discriminator_model_fake = discriminator(generator_model)# 定义损失函数
# 生成器损失
g_loss = -tf.reduce_mean(tf.log(discriminator_model_fake))
# 判别器损失
d_loss_real = -tf.reduce_mean(tf.log(discriminator_model_real))
d_loss_fake = -tf.reduce_mean(tf.log(1. - discriminator_model_fake))
d_loss = d_loss_real + d_loss_fake# 定义优化器
learning_rate = 0.001
trainer_g = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator'))
trainer_d = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator'))# 训练
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)for i in range(10000):
# 构造训练数据
x_data = np.random.normal(0, 1, size=[16, 2])
# 训练判别器
sess.run(trainer_d, feed_dict={X_inputs: x_data})
# 训练生成器
sess.run(trainer_g, feed_dict={X_inputs: x_data})
if i % 1000 == 0:
print('step:', i)
generator_data = sess.run(generator_model, feed_dict={X_inputs: x_data})
label_x = np.array([[1, 0] for _ in range(16)])
label_y = np.array([[0, 1] for _ in range(16)])
discriminator_data_x = sess.run(discriminator_model_real, feed_dict={X_inputs: label_x})
discriminator_data_y = sess.run(discriminator_model_real, feed_dict={X_inputs: label_y})
discriminator_data_fake = sess.run(discriminator_model_fake, feed_dict={X_inputs: generator_data})
print('accuracy of real data:', np.mean(discriminator_data_x))
print('accuracy of fake data:', np.mean(discriminator_data_fake))
print('accuracy of generated data:', np.mean(discriminator_data_y))我为您提供了一段GAN模型的代码,希望对您有所帮助。