图像分类的CNN模型不收敛，基于Tensorflow

Question

我尝试训练一个CNN模型，2个类，它基于张量流来进行图像分类。

我对时代，学习率，批量大小和CNN大小做了很多修改，但没有任何作用。

关于数据

86（标签：0）+ 63（标签：1）图片

形状：（128,128）

关于当前参数

learning_rate = 0.00005（我已尝试从0.00000001到0.8 ......）

批量大小= 30 （我也试过5到130）

epoch = 20

关于网络

def weight_variable(shape):

    initial = tf.truncated_normal(shape, stddev = 0.1, dtype = tf.float32)
    return tf.Variable(initial)


def bias_variable(shape):

    initial = tf.constant(0.1, shape = shape, dtype = tf.float32)
    return tf.Variable(initial)


def conv2d(x, W):

    #(input, filter, strides, padding)
    #[batch, height, width, in_channels]
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):

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

def cnn_model():

    epochs = 20
    batch_size = 30
    learning_rate = 0.00005
    hidden = 2
    cap_c = 86
    cap_h = 63
    num = cap_c + cap_h
    image_size = 128
    label_size = 2

    print ((num//(batch_size)) * epochs)
    train_loss = np.empty((num//(batch_size)) * epochs)
    train_acc = np.empty((num//(batch_size)) * epochs)

    x = tf.placeholder(tf.float32, shape = [None, image_size, image_size])
    y = tf.placeholder(tf.float32, shape = [None, label_size])

    weight_balance = tf.constant([0.1])

    X_train_ = tf.reshape(x, [-1, image_size, image_size, 1])

    #First layer
    W_conv1 = weight_variable([5, 5, 1, 4])
    b_conv1 = bias_variable([4])

    h_conv1 = tf.nn.relu(conv2d(X_train_, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

#    #Second layer
#    W_conv2 = weight_variable([5, 5, 4, 8])
#    b_conv2 = bias_variable([8])
#    
#    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
#    h_pool2 = max_pool_2x2(h_conv2)
#    
#    Third layer
#    W_conv3 = weight_variable([5, 5, 8, 16])
#    b_conv3 = bias_variable([16])
#    
#    h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
#    h_pool3 = max_pool_2x2(h_conv3)

    #Full connect layer
    W_fc1 = weight_variable([64 * 64 * 4, hidden])
    b_fc1 = bias_variable([hidden])

    h_pool2_flat = tf.reshape(h_pool1, [-1, 64 * 64 * 4])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    #Output_Softmax

    W_fc2 = weight_variable([hidden, label_size])
    b_fc2 = bias_variable([label_size])

    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

    print y_conv.shape



    #Train
    loss = tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(y, y_conv, weight_balance))
    optimize = tf.train.AdamOptimizer(learning_rate).minimize(loss)

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

关于结果

损失不会收敛，也不准确。

我不知道我的CNN模型是否不适合我的数据？ 或

网络的激活功能和丢失功能不合适？

真的，谢谢你

Answer 1

代码有几个问题：

1. 您正在最后一个图层上应用softmax，然后调用tf.nn.weighted_cross_entropy_with_logits，而sigmoid又会激活Xavier，因此您需要激活两次。
2. 要初始化权重，请使用Variance_scaling或tf.layers API以加快收敛速度​​。最好使用Ozone来实现模型，因为默认设置遵循最佳实践。