TensorFlow - トレーニングの精度は次のような約10％、私はKaggleの桁-認識器problem.Programが正常に実行することができます処理するためtensorflowでプログラムを書きますが、トレーニング精度が常に低いMNISTデータに

Question

を改善しない：

私は私のプログラムが悪いのか分からない

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}) 

：後

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 
…… 

は私のコードです。

Answer 1

私はあなたがあなたのbias_variable機能変更を提案 - どのようtf.Variable(tf.constant)振る舞い、プラス我々は通常ゼロにバイアスを初期化することを確認していない、いない0.1：あなたを初期化しようと、この問題が解決しない場合は

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

をウェイトをstddev=0.01