tensorflow一個數字輸出

Question

我是tensorflow的新手，我瀏覽了教程併成功執行了它們。 現在我必須解決一個問題，其中我的輸出不應該像MNIST標籤（1-10）那樣是絕對的。我想計算圖像中的對象，因此我只需要一個數值輸出值，因爲結果可以在0-300 +之間的範圍內，所以在單熱矢量中編碼的結果不適用。

我的代碼在下面（大部分是從MNIST tut複製的）它工作正常，如果我有多個類和標籤編碼在一個熱矢量。

我想我必須調整的成本函數：

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(prediction, y))

，但我不知道怎麼樣。任何人都可以幫助我 所以預測返回一個值，y（地面實況）也是一個值，例如。[5]爲5個對象。

### CNN CONFIG 


n_classes = 1 
batch_size = 100 

x = tf.placeholder('float', [None, 16384]) # 128*128 = 16384 28*28 = 784 
y = tf.placeholder('float') 

keep_rate = 0.8 
keep_prob = tf.placeholder(tf.float32) 


def conv2d(x, W): 
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 


def maxpool2d(x): 
    #      size of window   movement of window 
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') 


def convolutional_neural_network(x): 
    weights = {'W_conv1': tf.Variable(tf.random_normal([5, 5, 1, 32])), 
       'W_conv2': tf.Variable(tf.random_normal([5, 5, 32, 64])), 
       'W_fc': tf.Variable(tf.random_normal([32 * 32 * 64, 1024])), 
       'out': tf.Variable(tf.random_normal([1024, n_classes]))} 

    biases = {'b_conv1': tf.Variable(tf.random_normal([32])), 
       'b_conv2': tf.Variable(tf.random_normal([64])), 
       'b_fc': tf.Variable(tf.random_normal([1024])), 
       'out': tf.Variable(tf.random_normal([n_classes]))} 

    x = tf.reshape(x, shape=[-1, 128, 128, 1]) 

    conv1 = tf.nn.relu(conv2d(x, weights['W_conv1']) + biases['b_conv1']) 
    conv1 = maxpool2d(conv1) 

    conv2 = tf.nn.relu(conv2d(conv1, weights['W_conv2']) + biases['b_conv2']) 
    conv2 = maxpool2d(conv2) 

    #fc = tf.reshape(conv2, [-1, 7 * 7 * 64]) 
    fc = tf.reshape(conv2, [-1, 32 * 32 * 64]) 
    fc = tf.nn.relu(tf.matmul(fc, weights['W_fc']) + biases['b_fc']) 
    #fc = tf.nn.dropout(fc, keep_rate) 

    output = tf.matmul(fc, weights['out']) + biases['out'] 

    return output 


def train_neural_network(x): 
    prediction = convolutional_neural_network(x) 
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(prediction, y)) 
    optimizer = tf.train.AdamOptimizer().minimize(cost) 

    #saver 
    saver = tf.train.Saver() 

    hm_epochs = 50 
    with tf.Session() as sess: 
     sess.run(tf.initialize_all_variables()) 

     for epoch in range(hm_epochs): 
      epoch_loss = 0 
      logging.debug('Epoch: ' + str(epoch) +' started') 

      for i in range(int(len(train_database['images'])/batch_size)): 
       epoch_x, epoch_y = getNextBatch(train_database, (i + 1) * batch_size) 

       _, c = sess.run([optimizer, cost], feed_dict={x: epoch_x, y: epoch_y}) 
       epoch_loss += c 

      print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', epoch_loss) 

Answer 1

的標準方法是使用一個均方誤差：

cost = tf.reduce_mean(tf.square(prediction - y))