는 TensorFlow - MNIST 데이터에 개선되지 정확성을 훈련

Question

나는 일반적으로 실행할 수 있습니다 Kaggle의 자리-인식기 problem.Program을 처리 할 수 ​​tensorflow으로 프로그램을 작성하지만 훈련 정확도는 다음과 같은 약 10 %, 항상 낮은 :

을 나는 내 프로그램 뭐가 잘못 됐는지 모른다

def weight_variable(shape): 
    initial = tf.truncated_normal(shape, stddev=0.1) 
    return tf.Variable(initial) 

def bias_variable(shape): 
    initial = tf.constant(0.1, shape=shape) 
    return tf.Variable(initial) 

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

def max_pool_2x2(x): 
    # ksize = [batch, heigh, width, channels], strides=[batch, stride, stride, channels] 
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') 

x = tf.placeholder(tf.float32, [None, 784])  
y_ = tf.placeholder(tf.float32, [None, 10])  
keep_prob = tf.placeholder(tf.float32) 

x_image = tf.placeholder(tf.float32, [None, 28, 28, 1]) 

w_conv1 = weight_variable([5, 5, 1, 32])  
b_conv1 = bias_variable([32]) 
h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1) 
h_pool1 = max_pool_2x2(h_conv1) 

w_conv2 = weight_variable([5, 5, 32, 64])  
b_conv2 = bias_variable([64]) 
h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2) 
h_pool2 = max_pool_2x2(h_conv2) 

w_fc1 = weight_variable([7 * 7 * 64, 1024]) 
b_fc1 = bias_variable([1024]) 
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])  
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1) 
# dropout 
keep_prob = tf.placeholder(tf.float32)  
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 
# softmax 
w_fc2 = weight_variable([1024, 10]) 
b_fc2 = bias_variable([10]) 
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2) 

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices=[1])) 
train_step = tf.train.AdamOptimizer(10e-4).minimize(cross_entropy) 

correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 

def get_batch(i, size, train, label): 
    startIndex = (i * size) % 42000 
    endIndex = startIndex + size 
    batch_X = train[startIndex : endIndex] 
    batch_Y = label[startIndex : endIndex] 
    return batch_X, batch_Y 


data = pd.read_csv('train.csv') 
train_data = data.drop(['label'], axis=1) 
train_data = train_data.values.astype(dtype=np.float32) 
train_data = train_data.reshape(42000, 28, 28, 1) 

label_data = data['label'].tolist() 
label_data = tf.one_hot(label_data, depth=10) 
label_data = tf.Session().run(label_data).astype(dtype=np.float64) 


batch_size = 100        
tf.global_variables_initializer().run() 

for i in range(20000): 
    batch_x, batch_y = get_batch(i, batch_size, train_data, label_data) 
    if i % 100 == 0: 
     train_accuracy = accuracy.eval(feed_dict={x_image: batch_x, y_: batch_y, keep_prob: 1.0}) 
     print("step %d, training accuracy %g" % (i, train_accuracy)) 
    train_step.run(feed_dict={x_image: batch_x, y_: batch_y, keep_prob: 0.9}) 

: 다음

step 0, training accuracy 0.11 
step 100, training accuracy 0.13 
step 200, training accuracy 0.21 
step 300, training accuracy 0.12 
step 400, training accuracy 0.07 
step 500, training accuracy 0.08 
step 600, training accuracy 0.15 
step 700, training accuracy 0.05 
step 800, training accuracy 0.08 
step 900, training accuracy 0.12 
step 1000, training accuracy 0.05 
step 1100, training accuracy 0.09 
step 1200, training accuracy 0.12 
step 1300, training accuracy 0.1 
step 1400, training accuracy 0.08 
step 1500, training accuracy 0.11 
step 1600, training accuracy 0.17 
step 1700, training accuracy 0.13 
step 1800, training accuracy 0.11 
step 1900, training accuracy 0.13 
step 2000, training accuracy 0.07 
…… 

내 코드입니다.

Answer 1

나는 당신이 당신의 bias_variable 기능을 변경 제안 - tf.Variable(tf.constant) 동작합니다이 플러스 우리가 일반적으로 제로 편견을 초기화하는 것이 아니라 0.1 방법을 잘 :이 방법으로 문제가 해결되지 않으면

def bias_variable(shape): 
    return tf.zeros((shape), dtype = tf.float32) 

, 초기화 시도하여 가중치 stddev=0.01