完成 mnist/cifar 教程后，我想我会通过制作自己的“大”数据集来试验 tensorflow，为了简单起见，我选择了一个黑白椭圆形，它可以独立地改变其高度和宽度0.0-1.0 比例为 28x28 像素图像（其中我有 5000 个训练图像，1000 个测试图像）。
我的代码使用“MNIST 专家”教程作为基础（按比例缩小以提高速度），但我切换了基于平方误差的成本函数，并根据此处的建议，将 sigmoid 函数换成了最终激活层，因为这不是分类，而是两个张量 y_ 和 y_conv 之间的“最佳拟合”。
然而，在超过 100k 次迭代的过程中，损失输出迅速稳定在 400 到 900 之间的振荡（或者，因此，任何给定标签的 0.2-0.3 平均超过 50 个批次中的 2 个标签）所以我想我只是得到噪音。也许我错了，但我希望使用 Tensorflow 对图像进行卷积，以推断出可能有 10 个或更多独立的标记变量。我在这里错过了一些基本的东西吗？
def train(images, labels):
# Import data
oval = blender_input_data.read_data_sets(images, labels)
sess = tf.InteractiveSession()
# Establish placeholders
x = tf.placeholder("float", shape=[None, 28, 28, 1])
tf.image_summary('images', x)
y_ = tf.placeholder("float", shape=[None, 2])
# Functions for Weight Initialization.
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)
# Functions for convolution and pooling
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')
# First Variables
W_conv1 = weight_variable([5, 5, 1, 16])
b_conv1 = bias_variable([16])
# First Convolutional Layer.
h_conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
_ = tf.histogram_summary('weights 1', W_conv1)
_ = tf.histogram_summary('biases 1', b_conv1)
# Second Variables
W_conv2 = weight_variable([5, 5, 16, 32])
b_conv2 = bias_variable([32])
# Second Convolutional Layer
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
_ = tf.histogram_summary('weights 2', W_conv2)
_ = tf.histogram_summary('biases 2', b_conv2)
# Fully connected Variables
W_fc1 = weight_variable([7 * 7 * 32, 512])
b_fc1 = bias_variable([512])
# Fully connected Layer
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*32])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1)+b_fc1)
_ = tf.histogram_summary('weights 3', W_fc1)
_ = tf.histogram_summary('biases 3', b_fc1)
# Drop out to reduce overfitting
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# Readout layer with sigmoid activation function.
W_fc2 = weight_variable([512, 2])
b_fc2 = bias_variable([2])
with tf.name_scope('Wx_b'):
y_conv=tf.sigmoid(tf.matmul(h_fc1_drop, W_fc2)+b_fc2)
_ = tf.histogram_summary('weights 4', W_fc2)
_ = tf.histogram_summary('biases 4', b_fc2)
_ = tf.histogram_summary('y', y_conv)
# Loss with squared errors
with tf.name_scope('diff'):
error = tf.reduce_sum(tf.abs(tf.sub(y_,y_conv)))
diff = (error*error)
_ = tf.scalar_summary('diff', diff)
# Train
with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(1e-4).minimize(diff)
# Merge summaries and write them out.
merged = tf.merge_all_summaries()
writer = tf.train.SummaryWriter('/home/user/TBlogs/oval_logs', sess.graph_def)
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Launch the session.
sess.run(tf.initialize_all_variables())
# Restore variables from disk.
saver.restore(sess, "/home/user/TBlogs/model.ckpt")
for i in range(100000):
batch = oval.train.next_batch(50)
t_batch = oval.test.next_batch(50)
if i%10 == 0:
feed = {x:t_batch[0], y_: t_batch[1], keep_prob: 1.0}
result = sess.run([merged, diff], feed_dict=feed)
summary_str = result[0]
df = result[1]
writer.add_summary(summary_str, i)
print('Difference:%s' % (df)
else:
feed = {x:batch[0], y_: batch[1], keep_prob: 0.5}
sess.run(train_step, feed_dict=feed)
if i%1000 == 0:
save_path = saver.save(sess, "/home/user/TBlogs/model.ckpt")
# Completion
print("Session Done")
我最关心的是张量板如何显示权重几乎没有变化，即使经过数小时的训练和衰减的学习率（尽管代码中没有显示）。我对机器学习的理解是，在对图像进行卷积时，这些层实际上相当于边缘检测层……所以我很困惑为什么它们几乎不应该改变。
我目前的理论是：
1. 我忽略/误解了有关损失函数的一些内容。
2. 我误解了权重是如何初始化/更新的
3. 我严重低估了这个过程需要多长时间......尽管再一次，损失似乎只是在振荡。
任何帮助将不胜感激，谢谢！