python - 预测同一类的二元分类

Question

早上好，我正在实现两个独家单热编码标签的二进制分类模型。我终于到了程序输出一些东西的阶段，但遗憾的是它一直在预测同一个类。有一次它没有这样做，所以我想知道问题是否在于程序很容易衰减到损失函数的局部最小值。或者问题可能是 softmax 激活的饱和。变量是用 tf.truncated_normal 初始化的，我一直在尝试减少标准偏差，以防问题确实是 softmax 的饱和。图像是 RGB。此外，我的计算机不是最强大的，我正在运行 50-100 个图像的批次（它们相当大，480*704），以及大约 20-40 个时期。

模型本身是：

with tf.Graph().as_default():
    print("Creating Graph [{}]".format(datetime.datetime.now().strftime("%H:%M:%S")))
    x = tf.placeholder(tf.float32, [None, 480, 704, 3])
    y_true = tf.placeholder(tf.float32, [None, 2])
    is_training = tf.placeholder(tf.bool, [])

    with tf.name_scope("Conv_layers"):
        conv_1 = conv_layer(x, [5, 5, 3, 2])
        conv_pool_1 = max_pool_4x4(conv_1)

        conv_2 = conv_layer(conv_pool_1, [5, 5, 2, 4])
        conv_pool_2 = max_pool_2x2(conv_2)

        conv_3 = conv_layer(conv_pool_2, [5, 5, 4, 8])
        conv_pool_3 = max_pool_2x2(conv_3)

        conv_4 = conv_layer(conv_pool_3, [5, 5, 8, 16])
        conv_pool_4 = max_pool_2x2(conv_4)

    to_flat = tf.reshape(conv_pool_4, [-1, 22*15*16])
    full_1 = full_layer(to_flat, 1024)

    y_conv = full_layer(full_1, 2)
    y_conv = tf.cond(is_training, lambda: tf.identity(y_conv), lambda: tf.nn.softmax(y_conv))

损失函数和准确率：

cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_true * tf.log(y_conv), reduction_indices=[1]))
    train_step = tf.train.AdamOptimizer(0.03).minimize(cross_entropy)
    correct_pred = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_true, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

使用的函数：

def weight_variables(shape):
    initializer = tf.truncated_normal(shape=shape, stddev=0.05)
    return tf.Variable(initializer)

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):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

def max_pool_4x4(x):
    return tf.nn.max_pool(x, ksize=[1, 4, 4, 1], strides=[1, 4, 4, 1], padding='SAME')


def conv_layer(input, shape):
    W = weight_variables(shape=shape)
    b = bias_variable([shape[3]])
    return tf.nn.relu(conv2d(input, W))


def full_layer(input, size):
    in_size = int(input.get_shape()[1])
    W = weight_variables([in_size, size])
    b = bias_variable([size])
    return tf.matmul(input, W) + b

典型预测的一部分是：

[[0.28597853 0.71402144]
 [0.28610235 0.71389765]
 [0.28605604 0.713944  ]
 [0.28603107 0.71396893]
 [0.28613603 0.7138639 ]
 [0.2860006  0.7139994 ]
 [0.28612924 0.71387076]
 [0.28628975 0.71371025]
 [0.28614312 0.7138569 ]
 [0.28609362 0.71390635]
 [0.28626445 0.7137355 ]
 [0.28617397 0.71382606]]

增加卷积层的大小导致我的模型输出如下内容：

[[0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]
 [0. 1.]]

标签： pythontensorflowmachine-learningcross-entropy