Tensorflow，二進制分類

Question

是否可以進行二元分類？ 特別是對於行人檢測，無論是否是行人。 我在API中找不到任何內容，也沒有找到任何好的教程。 我嘗試調整深度MNIST教程中的代碼，該教程用於多類分類; 我制作了帶有行人的圖像，標記為1，負面帶有0，並使用3個通道（對於顏色，應該不是問題嗎？），但精度只是跳到了整個地方。

編碼

    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})

輸出

    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

謝謝你的幫助。

Answer 1

你絕對應該使用張量板來顯示交叉熵，偏差和權重摘要。 我認為它會讓你更好地了解正在發生的事情。

嘗試使用此代碼，然后運行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})