我正在学习 tensorflow,我尝试将基本的 softmax MNIST 示例改编为在我自己的图像集上工作。这是建筑物的航拍照片,我想按屋顶类型对它们进行分类。可以进行 4 种这样的分类。
简单(也许是幼稚的)想法是调整图像的大小(因为它们并不完全相同)并将它们展平。然后更改代码中的张量形状并运行它。当然,它不起作用。首先让我向您展示代码。
# Load csv Data
filenames = []
_answers = []
with open('/home/david/DSG/id_train.csv') as csvfile:
csv_reader = csv.reader(csvfile, delimiter=',')
for row in csv_reader:
one_hot_vec = [0, 0, 0, 0]
one_hot_vec[int(row[1])-1] = 1
_answers.append(np.asarray(one_hot_vec))
filenames.append("/home/david/DSG/roof_images/" + str(row[0]) + ".jpg")
sess = tf.InteractiveSession()
# Image Loading and processing
filename_q = tf.train.string_input_producer(filenames)
reader = tf.WholeFileReader()
key, value = reader.read(filename_q)
__img = tf.image.decode_jpeg(value, channels=1)
_img = tf.expand_dims(tf.image.convert_image_dtype(__img, tf.float32),0)
img = tf.image.resize_nearest_neighbor(_img, [150,150])
# Actual model
x = tf.placeholder(tf.float32, [None, 22500])
W = tf.Variable(tf.zeros([22500, 4]))
b = tf.Variable(tf.zeros([4]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
# Training algorithm
y_ = tf.placeholder(tf.float32, [None, 4])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y,1e-10,1.0)), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# Evaluate model, this checks the results from the y (prediciton matrix) against the known answers (y_)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
coord = tf.train.Coordinator()
init_op = tf.initialize_all_variables()
sess.run(init_op)
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# Loads and process all the images, adding them to an array for later use
images = []
for i in range(8000):
if i % 100 == 0:
print("Processing Images " + str(100*(i+100)/8000) + "% complete")
image = img.eval().flatten()
images.append(image)
# Train our model
for i in range(80):
print("Training the Model " + str(100*(i+1)/80) + "% complete")
batchImages = images[i*100:((i+1)*100)]
batchAnswers = answers[i*100:((i+1)*100)].astype(float)
# Here's a debug line I put in to see what the numbers were
print(sess.run(y, feed_dict={x: batchImages, y_: batchAnswers}))
sess.run(train_step, feed_dict={x: batchImages, y_: batchAnswers})
coord.request_stop()
coord.join(threads)
可以看出,我正在打印来自 softmax 的 y 值。结果是完全看起来像这样的张量[0., 0., 0., 1.]
。我觉得这很奇怪。所以我打印了tf.matmul(x, W) + b
.
结果是这样的:
[[-236.86216736 -272.89904785 59.67744446 450.08377075]
[-327.19482422 -384.06918335 87.47353363 623.79052734]
[-230.79460144 -264.78787231 60.29759598 435.28485107]
[-188.10324097 -212.30155945 53.8230629 346.58175659]
[-180.26617432 -209.45767212 48.90292358 340.82092285]
[-177.13232422 -200.59474182 45.97179413 331.75531006]
[-225.94104004 -258.97390747 61.54353333 423.37136841]
[-259.33599854 -290.73773193 67.69062042 482.38308716]
[-151.53468323 -174.09906006 39.97481537 285.65893555]
[-237.23356628 -272.71789551 65.12500763 444.82647705]
..... you get the idea
[-195.14971924 -221.30851746 53.09790802 363.36032104]
[-157.30508423 -175.47320557 40.4044342 292.37384033]
[-178.94332886 -203.36262512 47.0838356 335.22219849]
[-180.61688232 -200.0609436 45.12242508 335.55541992]
[-145.7559967 -163.06838989 35.25980377 273.56466675]
[-194.07254028 -213.78709412 53.14990997 354.70977783]
[-191.92044067 -219.13395691 49.84062958 361.21377563]]
对于手动计算 softmax 的第一个第二个和第三个元素,您会得到 E-200 数量级的数字,基本上为零。然后是第四个元素的大于 1 的数字。由于所有人都遵循这种模式,显然有些事情是错误的。
现在我检查了输入,我的答案是像这样的一个热向量[0, 1, 0, 0]
,我的图像被展平,值标准化为 0 和 1(浮点数)。就像 MNIST 的例子一样。
我还注意到,在 MNIST 示例中,matmul 的值要小得多。E0 量级。这是因为每张图像上有 784 个元素,而不是 22500 个?这是问题的原因吗?
哎呀,也许由于某种原因这永远不会奏效。我需要一些帮助。
编辑:我决定检查图像大小是否有任何影响,果然 matmul 确实给出了较小的数字。但是它们仍然表现出一种模式,所以我再次通过 softmax 运行它并得到了这个输出:
[[ 2.12474524e-20 1.00000000e+00 1.10456488e-18 0.00000000e+00]
[ 3.22400550e-21 1.00000000e+00 1.24568592e-19 0.00000000e+00]
[ 2.49283055e-28 1.00000000e+00 6.52334536e-26 0.00000000e+00]
[ 4.73190862e-23 1.00000000e+00 3.71980738e-21 0.00000000e+00]
[ 1.11151765e-26 1.00000000e+00 4.14652626e-24 0.00000000e+00]
[ 2.23096276e-22 1.00000000e+00 7.21511359e-21 0.00000000e+00]
[ 1.41888640e-23 1.00000000e+00 2.13637447e-21 0.00000000e+00]
[ 3.55662848e-17 1.00000000e+00 5.14018079e-16 4.06785808e-33]
[ 8.25783417e-26 1.00000000e+00 2.95267040e-23 0.00000000e+00]
[ 1.09395607e-25 1.00000000e+00 3.76775998e-23 0.00000000e+00]
[ 9.34879669e-13 1.00000000e+00 1.07488766e-11 7.21446627e-25]
[ 3.09687017e-34 1.00000000e+00 5.22547065e-31 0.00000000e+00]
[ 2.10362117e-22 1.00000000e+00 1.31067148e-20 0.00000000e+00]
[ 5.86830220e-23 1.00000000e+00 9.55902033e-21 0.00000000e+00]
[ 9.59656235e-17 1.00000000e+00 2.98987045e-15 7.10348533e-32]
[ 2.33712669e-16 1.00000000e+00 3.26934410e-15 1.55066807e-31]
[ 1.09302052e-27 1.00000000e+00 5.34793657e-25 0.00000000e+00]
[ 1.67101349e-25 1.00000000e+00 1.15098012e-22 0.00000000e+00]
[ 4.46111042e-26 1.00000000e+00 1.23599421e-23 0.00000000e+00]
[ 1.31791856e-24 1.00000000e+00 2.25831162e-22 0.00000000e+00]
[ 2.19408324e-12 1.00000000e+00 5.67631081e-11 1.22608556e-23]]
那肯定是有别的问题了。