Python tensorflow中的单神经元前馈网络
我制作了一个前馈单神经元网络。预测打印0.5,而它应该打印0.0。我对tensorflow很陌生。请帮帮我。这是我的代码:Python tensorflow中的单神经元前馈网络,python,machine-learning,tensorflow,feed-forward,Python,Machine Learning,Tensorflow,Feed Forward,我制作了一个前馈单神经元网络。预测打印0.5,而它应该打印0.0。我对tensorflow很陌生。请帮帮我。这是我的代码: """ O---(w1)-\ \ O---(w2)-->Sum ---> Sigmoid ---> O 3 inputs and 1 output / O---(w3)-/ | Input | Output Example 1 | 0 0 1 | 0 Exam
"""
O---(w1)-\
\
O---(w2)-->Sum ---> Sigmoid ---> O 3 inputs and 1 output
/
O---(w3)-/
| Input | Output
Example 1 | 0 0 1 | 0
Example 2 | 1 1 1 | 1
Example 3 | 1 0 1 | 1
Exmaple 4 | 0 1 1 | 0
"""
import tensorflow as tf
features = tf.placeholder(tf.float32, [None, 3])
labels = tf.placeholder(tf.float32, [None])
#Random weights
W = tf.Variable([[-0.16595599], [0.44064899], [-0.99977125]], tf.float32)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
predict = tf.nn.sigmoid(tf.matmul(features, W))
error = labels - predict
# Training
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(error)
for i in range(10000):
sess.run(train, feed_dict={features: [[0, 1, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]], labels: [0, 1, 1, 0]})
training_cost = sess.run(error, feed_dict={features: [[0, 1, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]], labels: [0, 1, 1, 0]})
print('Training cost = ', training_cost, 'W = ', sess.run(W))
print(sess.run(predict, feed_dict={features:[[0, 1, 1]]}))
我还手工制作了这个模型,只使用numpy,效果很好
编辑:我尝试过所有类型的成本函数,包括 你犯了两个错误
(a) 您原来的错误函数优化了错误的目标
(b) 你的目标向量被转置了
下面的行使其可见
打印(sess.run)(预测标签、,
feed_dict={features:[[0,1,1],[1,1,1],[1,0,1],[0,1,1]],
标签:[0,1,1,0]}))
结果是一个4x4矩阵
您可以使用以下代码获得所需的结果
import tensorflow as tf
features = tf.placeholder(tf.float32, [None, 3])
labels = tf.placeholder(tf.float32, [None,1])
#Random weights
W = tf.Variable([[10.0], [000.0], [0.200]], tf.float32)
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
predict = tf.nn.sigmoid(tf.matmul(features, W))
print(sess.run(predict, feed_dict={features:[[0, 1, 1]]}))
lbls= [[0], [1], [1], [0]]
print(sess.run(predict,
feed_dict={features: [[0, 1, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]], labels:lbls}))
# error = labels - predict
error = tf.reduce_mean((labels - predict)**2)
# Training
optimizer = tf.train.GradientDescentOptimizer(10)
train = optimizer.minimize(error)
for i in range(100):
sess.run(train,
feed_dict={features: [[0, 1, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]], labels: lbls})
training_cost = sess.run(error,
feed_dict={features: [[0, 1, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]],
labels: lbls})
classe = sess.run((labels-predict),
feed_dict={features: [[0, 1, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]],
labels: lbls})
print('Training cost = ', training_cost, 'W = ', classe)
print(sess.run(predict,
feed_dict={features: [[0, 1, 1], [1, 1, 1], [1, 0, 1], [0, 1, 1]]}))
labels-predict不是一个有效的错误,可能您的意思是tf.reduce_-mean((labels-predict)**2)?