是否可以进行二进制分类？尤其是行人检测，不管是不是行人。我在API中找不到任何有用的教程。
我试着修改了deep MNIST教程中的代码，该教程用于多类分类；我制作了图像，其中行人被标记为1，底片被标记为0，并使用了3个通道（对于颜色，应该不是问题吧？），但准确度却跳得到处都是。
守则
import dataset as input_data
import tensorflow as tf
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)
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, 3, 3, 1],
strides=[1, 2, 2, 1], padding='SAME')
data = input_data.read_data_sets()
sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, input_data.HEIGHT * input_data.WIDTH * 3])
y_ = tf.placeholder("float", shape=[None, 2])
W_conv1 = weight_variable([5, 5, 3, 64])
b_conv1 = bias_variable([64])
x_image = tf.reshape(x, [-1, input_data.WIDTH, input_data.HEIGHT, 3])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
h_norm1 = tf.nn.lrn(h_pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
W_conv2 = weight_variable([5, 5, 64, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_norm1, W_conv2) + b_conv2)
h_norm2 = tf.nn.lrn(h_conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
h_pool2 = max_pool_2x2(h_norm2)
W_fc1 = weight_variable([input_data.HEIGHT / 4 * input_data.WIDTH / 4 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, input_data.HEIGHT / 4 * input_data.WIDTH / 4 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 2])
b_fc2 = bias_variable([2])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-6).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.initialize_all_variables())
for i in range(20000):
batch = data.train.next_batch(50)
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0], y_: batch[1], keep_prob: 1.0})
print "step %d, training accuracy %g" % (i, train_accuracy)
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
print "test accuracy %g" % accuracy.eval(feed_dict={
x: data.test.images, y_: data.test.labels, keep_prob: 1.0})
输出
step 0, training accuracy 0.14
step 100, training accuracy 0.54
step 200, training accuracy 0.28
step 300, training accuracy 0.46
step 400, training accuracy 0.32
step 500, training accuracy 0.52
step 600, training accuracy 0.56
step 700, training accuracy 0.76
step 800, training accuracy 0.66
谢谢，帮忙是事先安排好的。
最佳答案
你绝对应该使用tensorboard来可视化交叉熵、偏差和权重摘要。我想这会让你更清楚地了解正在发生的事情。
尝试使用此代码，然后运行tensorboard：
import dataset as input_data
import tensorflow as tf
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)
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, 3, 3, 1],
strides=[1, 2, 2, 1], padding='SAME')
data = input_data.read_data_sets()
sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, input_data.HEIGHT * input_data.WIDTH * 3])
y_ = tf.placeholder("float", shape=[None, 2])
W_conv1 = weight_variable([5, 5, 3, 64])
b_conv1 = bias_variable([64])
x_image = tf.reshape(x, [-1, input_data.WIDTH, input_data.HEIGHT, 3])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
h_norm1 = tf.nn.lrn(h_pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
W_conv2 = weight_variable([5, 5, 64, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_norm1, W_conv2) + b_conv2)
h_norm2 = tf.nn.lrn(h_conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
h_pool2 = max_pool_2x2(h_norm2)
W_fc1 = weight_variable([input_data.HEIGHT / 4 * input_data.WIDTH / 4 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, input_data.HEIGHT / 4 * input_data.WIDTH / 4 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 2])
b_fc2 = bias_variable([2])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
# Add summary ops to collect data
w_fc2_hist = tf.histogram_summary("weights_fc2", W_fc2)
b_fc2_hist = tf.histogram_summary("bias_fc2", b_fc2)
w_in_hist = tf.histogram_summary("weights_in", W)
b_in_hist = tf.histogram_summary("bias_in", b)
y_hist = tf.histogram_summary("y_conv", y_conv)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
ce_summ = tf.scalar_summary("cross entropy", cross_entropy)
train_step = tf.train.AdamOptimizer(1e-6).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
accuracy_summary = tf.scalar_summary("train accuracy", accuracy)
# Merge all the summaries and write them out to /tmp/tf
merged = tf.merge_all_summaries()
writer = tf.train.SummaryWriter("/tmp/tf", sess.graph_def)
sess.run(tf.initialize_all_variables())
for i in range(20000):
batch = data.train.next_batch(50)
feed={x: batch[0], y_: batch[1], keep_prob: 0.5}
result = sess.run([merged, accuracy, train_step], feed_dict=feed)
if i % 100 == 0: # Record summary data, and the accuracy
summary_str = result[0]
acc = result[1]
writer.add_summary(summary_str, i)
print("Accuracy at step {0}/{1}: {2}%".format(i, 20000, int(acc*100)))
print "test accuracy %g" % accuracy.eval(feed_dict={
x: data.test.images, y_: data.test.labels, keep_prob: 1.0})
关于python - Tensorflow，二进制分类，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/34163953/