Question

我通过在Tensorflow中构建卷积神经网络模型来进行字符识别。我的模型有2个Conv层，后面是2个完全连接的层。我有大约78K图像来自训练和13K图像进行测试。当我执行模型时，我在测试集上获得了大约92.xx％的准确度。当我在Tensorboard上显示我的准确度和损失曲线时。我有一个垂直线，但我不知道为什么我会这样做？我得到了这样的曲线Accuracy and Cross Entropy curve when viewed on tensorboard。

权重和偏差的分布曲线也显示垂直线Left side shows testing parameters (weights and bias) and right side shows training parameters on first conv layer

对此方面的任何帮助表示高度赞赏。 !!

def conv_layer(input, size_in, size_out, name="conv"):
with tf.name_scope(name):
w = tf.Variable(tf.random_normal([5, 5, size_in, size_out], stddev=0.1), name="W")
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
conv = tf.nn.conv2d(input, w, strides=[1, 1, 1, 1],padding="VALID")
act = tf.nn.relu(conv + b)
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return tf.nn.max_pool(act, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")`

def fc_layer(input, size_in, size_out, name="fc"):
with tf.name_scope(name):
w = tf.Variable(tf.random_normal([size_in, size_out], stddev=0.1), name="W")  # Truncated_normal
b = tf.Variable(tf.constant(0.1, shape=[size_out]), name="B")
act = tf.matmul(input, w) + b
tf.summary.histogram("weights", w)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return act

def model(use_two_conv, use_two_fc):
sess = tf.Session()
x = tf.placeholder(tf.float32, shape=[None, 1024], name="x")
x_image = tf.reshape(x, [-1, 32, 32, 1])
tf.summary.image('input', x_image, 3)
y = tf.placeholder(tf.float32, shape=[None,46], name="labels")

if use_two_conv:
  conv1 = conv_layer(x_image, 1, 4, "conv1")
  conv_out = conv_layer(conv1,4,16,"conv2")    
else:
  conv1 = conv_layer(x_image, 1, 16, "conv1")
  conv_out = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")

flattened = tf.reshape(conv_out, [-1, 5 * 5 * 16])

if use_two_fc:
  fc1 = fc_layer(flattened, 5 * 5 * 16, 200, "fc1")
  relu = tf.nn.relu(fc1)
  tf.summary.histogram("fc1/relu", relu)
  logits = fc_layer(fc1, 200, 46, "fc2")        

else:
  logits = fc_layer(flattened, 5*5*16, 46, "fc")

Answer 1

当我在过去遇到这个问题时，它是使用

的结果

writer.add_summary(current_summary)

而不是

writer.add_summary(current_summary, epoch)

（使用通用变量名称，因为未发布提问者代码的相关部分。）例如，

summary_op = tf.summary.merge_all()
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter("/Whatever/Path", sess.graph)
    for iteration in range(1001):
        if epoch % 100 == 0:
            _, current_summary = sess.run([training_op, summary_op])
            writer.add_summary(current_summary, iteration)
        else:
            _ = sess.run(training_op)

带有Tensorflow的CNN模型

1 个答案: