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我做了一個前饋單神經元網絡。預測打印0.5,而打印0.0。我對tensorflow很陌生。請幫幫我。這是我的代碼:張量流單神經元前饋網絡
"""
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]]}))
我也手動使這個模型只使用numpy,效果很好。
編輯:我已經嘗試了所有類型的成本函數,包括tf.reduce_mean(預測的標籤)** 2)
標籤取得了理想的結果 - 預測是不是一個有效的錯誤,也許你的意思tf.reduce_mean((標籤 - 預測)** 2)? – lejlot