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我試圖在TensorFlow上訓練一個非常簡單的模型。模型將一個浮點數作爲輸入,並返回輸入的概率大於0.我使用了一個帶有10個隱藏單元的隱藏層。完整的代碼如下所示:TensorFlow上的簡單網絡
import tensorflow as tf
import random
# Graph construction
x = tf.placeholder(tf.float32, shape = [None,1])
y_ = tf.placeholder(tf.float32, shape = [None,1])
W = tf.Variable(tf.random_uniform([1,10],0.,0.1))
b = tf.Variable(tf.random_uniform([10],0.,0.1))
layer1 = tf.nn.sigmoid(tf.add(tf.matmul(x,W), b))
W1 = tf.Variable(tf.random_uniform([10,1],0.,0.1))
b1 = tf.Variable(tf.random_uniform([1],0.,0.1))
y = tf.nn.sigmoid(tf.add(tf.matmul(layer1,W1),b1))
loss = tf.square(y - y_)
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
# Training
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
N = 1000
while N != 0:
batch = ([],[])
u = random.uniform(-10.0,+10.0)
if u >= 0.:
batch[0].append([u])
batch[1].append([1.0])
if u < 0.:
batch[0].append([u])
batch[1].append([0.0])
sess.run(train_step, feed_dict = {x : batch[0] , y_ : batch[1]})
N -= 1
while(True):
u = raw_input("Give an x\n")
print sess.run(y, feed_dict = {x : [[u]]})
的問題是,我得到非常無關的結果。模型不會學習任何東西並返回不相關的概率。我試圖調整學習速度並更改變量初始化,但我沒有得到任何有用的東西。你有什麼建議嗎?