【发布时间】：2018-04-21 18:20:16
【问题描述】：
我正在用 10K 灰度图像训练 CNN。该网络有 6 个卷积层、1 个全连接层和 1 个输出层。
当我开始训练时，损失会非常高，但会稳步下降，但我的准确度从 1.0 开始，并且也会下降。并从 72% 下降到 30% 并再次回升。此外，当我在看不见的图像上运行acc.eval({x: test_images, y: test_lables}) 时，准确率约为 16%。
另外，我有 6 个类，它们都是 one-hot 编码的。
我认为我可能错误地比较了预测输出，但在我的代码中看不到错误...
这是我的代码
pred = convolutional_network(x)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y, logits = pred))
train_op = tf.train.AdamOptimizer(learning_rate=0.01).minimize(loss)
prediction = tf.nn.softmax(pred)
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
acc = tf.reduce_mean(tf.cast(correct, 'float'))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer()) # Initialize all the variables
saver = tf.train.Saver()
time_full_start = time.clock()
print("RUNNING SESSION...")
for epoch in range(num_epochs):
train_batch_x = []
train_batch_y = []
epoch_loss = 0
i = 0
while i < len(images):
start = i
end = i+ batch_size
train_batch_x = images[start:end]
train_batch_y = labels[start:end]
op , ac, loss_value = sess.run([train_op, acc, loss], feed_dict={x: train_batch_x, y: train_batch_y})
epoch_loss += loss_value
i += batch_size
print('Epoch : ', epoch+1, ' of ', num_epochs, ' - Loss for epoch: ', epoch_loss, ' Accuracy: ', ac)
time_full_end = time.clock()
print('Full time elapse:', time_full_end - time_full_start)
print('Accuracy:', acc.eval({x: test_images, y: test_labels}))
save_path = saver.save(sess, MODEL_PATH)
print("Model saved in file: " , save_path)
这是输出
Epoch : 1 of 100 - Loss for epoch: 8.94737603121e+13 Accuracy: 1.0
Epoch : 2 of 100 - Loss for epoch: 212052447727.0 Accuracy: 1.0
Epoch : 3 of 100 - Loss for epoch: 75150603462.2 Accuracy: 1.0
Epoch : 4 of 100 - Loss for epoch: 68164116617.4 Accuracy: 1.0
Epoch : 5 of 100 - Loss for epoch: 18505190718.8 Accuracy: 0.99
Epoch : 6 of 100 - Loss for epoch: 11373286689.0 Accuracy: 0.96
Epoch : 7 of 100 - Loss for epoch: 3129798657.75 Accuracy: 0.07
Epoch : 8 of 100 - Loss for epoch: 374790121.375 Accuracy: 0.58
Epoch : 9 of 100 - Loss for epoch: 105383792.938 Accuracy: 0.72
Epoch : 10 of 100 - Loss for epoch: 49705202.4844 Accuracy: 0.66
Epoch : 11 of 100 - Loss for epoch: 30214170.7909 Accuracy: 0.36
Epoch : 12 of 100 - Loss for epoch: 18653020.5084 Accuracy: 0.82
Epoch : 13 of 100 - Loss for epoch: 14793638.35 Accuracy: 0.39
Epoch : 14 of 100 - Loss for epoch: 10196079.7003 Accuracy: 0.73
Epoch : 15 of 100 - Loss for epoch: 6727522.37319 Accuracy: 0.47
Epoch : 16 of 100 - Loss for epoch: 4593769.05838 Accuracy: 0.68
Epoch : 17 of 100 - Loss for epoch: 3669332.09406 Accuracy: 0.44
Epoch : 18 of 100 - Loss for epoch: 2850924.81662 Accuracy: 0.59
Epoch : 19 of 100 - Loss for epoch: 1780678.12892 Accuracy: 0.51
Epoch : 20 of 100 - Loss for epoch: 1855037.40652 Accuracy: 0.61
Epoch : 21 of 100 - Loss for epoch: 1012934.52827 Accuracy: 0.53
Epoch : 22 of 100 - Loss for epoch: 649319.432669 Accuracy: 0.55
Epoch : 23 of 100 - Loss for epoch: 841660.786938 Accuracy: 0.57
Epoch : 24 of 100 - Loss for epoch: 490148.861691 Accuracy: 0.55
Epoch : 25 of 100 - Loss for epoch: 397315.021568 Accuracy: 0.5
......................
Epoch : 99 of 100 - Loss for epoch: 4412.61703086 Accuracy: 0.57
Epoch : 100 of 100 - Loss for epoch: 4530.96991658 Accuracy: 0.62
Full time elapse: 794.5787720000001
**Test Accuracy: 0.158095**
我已经尝试了多种学习率和网络规模，但似乎可以让它发挥作用。任何帮助将不胜感激
【问题讨论】：
-
你应该随机化订单数据输入你的神经网络。另外，数据有错误吗？特异性你确定每个类都有 1 个二进制值
标签： python tensorflow machine-learning neural-network conv-neural-network