我正在爲某個任務編寫二元分類器,而不是在輸出層中使用2個神經元我只想使用一個具有S形函數的函數如果它低於0.5,基本上輸出0級,否則基本輸出0。當使用具有單輸出神經元張量流的神經網絡時,損失和準確性爲0
圖像被加載,調整大小爲64x64並展平,以創建問題的傳真)。數據加載代碼將在最後出現。我創建佔位符。
並定義模型如下。
def create_model_linear(data):
fcl1_desc = {'weights': weight_variable([4096,128]), 'biases': bias_variable([128])}
fcl2_desc = {'weights': weight_variable([128,1]), 'biases': bias_variable([1])}
fc1 = tf.nn.relu(tf.matmul(data, fcl1_desc['weights']) + fcl1_desc['biases'])
fc2 = tf.nn.sigmoid(tf.matmul(fc1, fcl2_desc['weights']) + fcl2_desc['biases'])
return fc2
功能weight_variable
和bias_variable
簡單地返回給定形狀的tf.Variable()
。 (代碼也是最後的。)
然後我定義瞭如下的訓練函數。
def train(x, hm_epochs):
prediction = create_model_linear(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = prediction, labels = y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
batch_size = 100
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(hm_epochs):
epoch_loss = 0
i = 0
while i < len(train_x):
start = i
end = i + batch_size
batch_x = train_x[start:end]
batch_y = train_y[start:end]
_, c = sess.run([optimizer, cost], feed_dict = {x:batch_x, y:batch_y})
epoch_loss += c
i+=batch_size
print('Epoch', epoch+1, 'completed out of', hm_epochs,'loss:',epoch_loss)
correct = tf.greater(prediction,[0.5])
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
i = 0
acc = []
while i < len(train_x):
acc +=[accuracy.eval({x:train_x[i:i+1000], y:train_y[i:i + 1000]})]
i+=1000
print sum(acc)/len(acc)
的train(x, 10)
輸出是
( '時代',1 '完成了的',10, '損失:',0.0) ( '時代',2「,完成('Epoch',3,''out of',10,'loss:',0.0) ('Epoch',4,'out of','out of',10,'loss:',0.0) 10,'loss:',0.0) ('Epoch',5,'completed out',10,'loss:',0.0) ('Epoch',6''out of',10,'loss :',0.0) ('Epoch',7,'out of',10,'loss:',0.0)('Epoch',8,'out of',10,'loss:',0.0) ('Epoch',9,'completed out',10,'loss:',0.0) ('Epoch' ',10,'out of',10,'loss:',0.0)
0.0 我錯過了什麼?
這裏是所有實用功能的承諾代碼:
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def getLabel(wordlabel):
if wordlabel == 'Class_A':
return [1]
elif wordlabel == 'Class_B':
return [0]
else:
return -1
def loadImages(pathToImgs):
images = []
labels = []
filenames = os.listdir(pathToImgs)
imgCount = 0
for i in tqdm(filenames):
wordlabel = i.split('_')[1]
oneHotLabel = getLabel(wordlabel)
img = cv2.imread(pathToImgs + i,cv2.IMREAD_GRAYSCALE)
if oneHotLabel != -1 and type(img) is np.ndarray:
images += [cv2.resize(img,(64,64)).flatten()]
labels += [oneHotLabel]
imgCount+=1
print imgCount
return (images,labels)
我認爲,因爲你用乙狀結腸和輸出層神經元1,你應該使用tf.nn.sigmoid_cross_entropy_with_logits代替tf.nn.softmax_cross_entropy_with_logits。 –
其實就是這樣。不能相信我錯過了。我也應該從最後一層刪除sifmoid –
我很高興它幫助解決了這個問題!然後,我將發表我的評論作爲一個單獨的答案。 –