这是我训练的歧视性网络，所以我可以在生成网络中使用它。我在具有2个特征的数据集上进行了训练，并进行了二元分类。 1 =冥想0 =不冥想。 （数据集来自siraj raval的视频之一）。
由于某些原因，输出层（ol）在每个测试用例中总是输出[1]。
我的数据集：https://drive.google.com/open?id=0B5DaSp-aTU-KSmZtVmFoc0hRa3c
import pandas as pd
import tensorflow as tf
data = pd.read_csv("E:/workspace_py/datasets/simdata/linear_data_train.csv")
data_f = data.drop("lbl", axis = 1)
data_l = data.drop(["f1", "f2"], axis = 1)
learning_rate = 0.01
batch_size = 1
n_epochs = 30
n_examples = 999 # This is highly unsatisfying >:3
n_iteration = int(n_examples/batch_size)
features = tf.placeholder('float', [None, 2], name='features_placeholder')
labels = tf.placeholder('float', [None, 1], name = 'labels_placeholder')
weights = {
'ol': tf.Variable(tf.random_normal([2, 1], stddev= -12), name = 'w_ol')
}
biases = {
'ol': tf.Variable(tf.random_normal([1], stddev=-12), name = 'b_ol')
}
ol = tf.nn.sigmoid(tf.add(tf.matmul(features, weights['ol']), biases['ol']), name = 'ol')
loss = -tf.reduce_sum(labels*tf.log(ol), name = 'loss') # cross entropy
train = tf.train.AdamOptimizer(learning_rate).minimize(loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for epoch in range(n_epochs):
ptr = 0
for iteration in range(n_iteration):
epoch_x = data_f[ptr: ptr + batch_size]
epoch_y = data_l[ptr: ptr + batch_size]
ptr = ptr + batch_size
_, err = sess.run([train, loss], feed_dict={features: epoch_x, labels:epoch_y})
print("Loss @ epoch ", epoch, " = ", err)
print("Testing...\n")
data = pd.read_csv("E:/workspace_py/datasets/simdata/linear_data_eval.csv")
test_data_l = data.drop(["f1", "f2"], axis = 1)
test_data_f = data.drop("lbl", axis = 1)
#vvvHERE
print(sess.run(ol, feed_dict={features: test_data_f})) #<<<HERE
#^^^HERE
saver = tf.train.Saver()
saver.save(sess, save_path="E:/workspace_py/saved_models/meditation_disciminative_model.ckpt")
sess.close()
输出：
2017-10-11 00:49:47.453721: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-10-11 00:49:47.454212: W C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2017-10-11 00:49:49.608862: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 960M
major: 5 minor: 0 memoryClockRate (GHz) 1.176
pciBusID 0000:01:00.0
Total memory: 4.00GiB
Free memory: 3.35GiB
2017-10-11 00:49:49.609281: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:976] DMA: 0
2017-10-11 00:49:49.609464: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:986] 0: Y
2017-10-11 00:49:49.609659: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0)
Loss @ epoch 0 = 0.000135789
Loss @ epoch 1 = 4.16049e-05
Loss @ epoch 2 = 1.84776e-05
Loss @ epoch 3 = 9.41758e-06
Loss @ epoch 4 = 5.24522e-06
Loss @ epoch 5 = 2.98024e-06
Loss @ epoch 6 = 1.66893e-06
Loss @ epoch 7 = 1.07288e-06
Loss @ epoch 8 = 5.96047e-07
Loss @ epoch 9 = 3.57628e-07
Loss @ epoch 10 = 2.38419e-07
Loss @ epoch 11 = 1.19209e-07
Loss @ epoch 12 = 1.19209e-07
Loss @ epoch 13 = 1.19209e-07
Loss @ epoch 14 = -0.0
Loss @ epoch 15 = -0.0
Loss @ epoch 16 = -0.0
Loss @ epoch 17 = -0.0
Loss @ epoch 18 = -0.0
Loss @ epoch 19 = -0.0
Loss @ epoch 20 = -0.0
Loss @ epoch 21 = -0.0
Loss @ epoch 22 = -0.0
Loss @ epoch 23 = -0.0
Loss @ epoch 24 = -0.0
Loss @ epoch 25 = -0.0
Loss @ epoch 26 = -0.0
Loss @ epoch 27 = -0.0
Loss @ epoch 28 = -0.0
Loss @ epoch 29 = -0.0
Testing...
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Saving model...
[Finished in 57.9s]
答案 0 :(得分：1)
首先，这不是有效的交叉熵损失。您使用的等式仅适用于2个或更多输出。只需一个sigmoid输出就可以了
-tf.reduce_sum(labels*tf.log(ol) + (1-labels)*tf.log(1-ol), name = 'loss')
否则最佳解决方案是始终回答＆＃34; 1＆＃34; （现在正在发生）。
为什么？
请注意，标签只有0或1，而您的整个损失是预测的标签和对数的乘积。因此，当true label为0时，无论您的预测如何，您的损失都为0，因为0 * log（x）= 0，无论x是什么（只要定义了log（x））。因此，您的模型只会因为没有预测＆＃34; 1＆＃34;而受到惩罚。什么时候应该，所以它学会一直输出1。
你正在为正态分布提供负面的stddev，而你不应该这样做（除非这是random_normal的一些未记录的功能，但根据文档它应该接受一个正浮点数，而你应该提供一个小数字。）
像这样（以天真的方式）计算交叉熵不是数值稳定的，请看一下tf.sigmoid_cross_entropy_with_logits。
您没有置换您的数据集，因此您始终以相同的顺序处理数据，这可能会产生不良后果（损失的周期性增加，更难收敛或缺乏收敛）。