CNN关于图像分类的模型不是收敛的，它是基于Tensorflow的

Question

我尝试训练一个 CNN 模型，2 个类，它基于张量流进行图像分类。

我已经尝试了对epoch，学习率，批量大小和CNN大小进行了很多修改，但没有任何效果。

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(我尝试过从 0.00000001 到 0.8...

批量大小 = 30(我也尝试过从 5 到 130(

纪元 = 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

损失不是收敛的，也是精度。

我不知道我的CNN模型是否不适合我的数据？ 或

网络的激活功能和损失功能不合适？

真的谢谢你

Answer 1

代码有几个问题：

1. 您在最后一层应用softmax，然后调用tf.nn.weighted_cross_entropy_with_logits，而又应用sigmoid激活，因此您应用了两次激活。
2. 要初始化权重，请使用Xavier或Variance_scaling以加快收敛速度。最好在实现模型时使用tf.layers API，因为其默认设置遵循最佳做法。