问题描述
我正在尝试使用 tf.nn.sparse_softmax_cross_entropy_with_logits 并且我已经按照用户 Olivier Moindrot [here][1] 的回答进行操作，但我遇到了尺寸错误
I'm trying to use tf.nn.sparse_softmax_cross_entropy_with_logits and I have followed the answer by user Olivier Moindrot [here][1] but I'm getting a dimension error
我正在构建一个分割网络，所以输入图像是 200x200，输出图像是 200x200.分类是二元的，所以前景和背景.
I'm building a segmentation network, so the input image is 200x200 and the output image is 200x200. The classification is binary, so foreground and background.
在我构建 CNN 之后 pred = conv_net(x, weights, biases, keep_prob)
After I build the CNN pred = conv_net(x, weights, biases, keep_prob)
pred 看起来像这样
CNN 有几个 conv 层，后跟一个全连接层.全连接层是40000，因为它是200x200展平的.
The CNN has a couple of conv layers followed by a fully connected layer. The fully connected layer is 40000 because it is 200x200 flattened.
根据上面的链接，我像这样重塑了pred...
According to the above link, I reshape pred like so...
(旁注:我也尝试将 tf.pack() 两个 pred —— 像上面一样 —— 打包在一起，但我认为这是错误的)
(side note: I also tried packing tf.pack() two pred's -- like above -- together, but I thought that was wrong)
pred = tf.reshape(pred, [-1, 200, 200, 2])
...所以有2个分类.继续上面的链接...
...so that there are 2 classifications. Continuing the above link...
temp_pred = tf.reshape(pred, [-1,2])
temp_y = tf.reshape(y, [-1])
cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(temp_pred, temp_y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
我有以下占位符和批处理数据...
I have the following placeholders and batch data...
x = tf.placeholder(tf.float32, [None, 200, 200])
y = tf.placeholder(tf.int64, [None, 200, 200])
(Pdb) batch_x.shape
(10, 200, 200)
(Pdb) batch_y.shape
(10, 200, 200)
当我运行训练课程时，我收到以下维度错误:
When I run a training session, I get the following dimension error:
tensorflow.python.framework.errors.InvalidArgumentError: logits first
dimension must match labels size. logits shape=[3200000,2] labels
shape=[400000]
我的完整代码如下所示:
My full code looks like this:
import tensorflow as tf
import pdb
import numpy as np
# Import MINST data
# from tensorflow.examples.tutorials.mnist import input_data
# mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# Parameters
learning_rate = 0.001
training_iters = 200000
batch_size = 10
display_step = 1
# Network Parameters
n_input = 200 # MNIST data input (img shape: 28*28)
n_classes = 2 # MNIST total classes (0-9 digits)
n_output = 40000
#n_input = 200
dropout = 0.75 # Dropout, probability to keep units
# tf Graph input
x = tf.placeholder(tf.float32, [None, n_input, n_input])
y = tf.placeholder(tf.int64, [None, n_input, n_input])
keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)
# Create some wrappers for simplicity
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
# MaxPool2D wrapper
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
padding='SAME')
# Create model
def conv_net(x, weights, biases, dropout):
# Reshape input picture
x = tf.reshape(x, shape=[-1, 200, 200, 1])
# Convolution Layer
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling)
# conv1 = tf.nn.local_response_normalization(conv1)
# conv1 = maxpool2d(conv1, k=2)
# Convolution Layer
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling)
# conv2 = tf.nn.local_response_normalization(conv2)
# conv2 = maxpool2d(conv2, k=2)
# Convolution Layer
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
# # Max Pooling (down-sampling)
# conv3 = tf.nn.local_response_normalization(conv3)
# conv3 = maxpool2d(conv3, k=2)
# return conv3
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Apply Dropout
fc1 = tf.nn.dropout(fc1, dropout)
return tf.add(tf.matmul(fc1, weights['out']), biases['out'])
# Output, class prediction
# output = []
# for i in xrange(2):
# # output.append(tf.nn.softmax(tf.add(tf.matmul(fc1, weights['out']), biases['out'])))
# output.append((tf.add(tf.matmul(fc1, weights['out']), biases['out'])))
#
# return output
# Store layers weight & bias
weights = {
# 5x5 conv, 1 input, 32 outputs
'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
# 5x5 conv, 32 inputs, 64 outputs
'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
# 5x5 conv, 32 inputs, 64 outputs
'wc3': tf.Variable(tf.random_normal([5, 5, 64, 128])),
# fully connected, 7*7*64 inputs, 1024 outputs
'wd1': tf.Variable(tf.random_normal([50*50*64, 1024])),
# 1024 inputs, 10 outputs (class prediction)
'out': tf.Variable(tf.random_normal([1024, n_output]))
}
biases = {
'bc1': tf.Variable(tf.random_normal([32])),
'bc2': tf.Variable(tf.random_normal([64])),
'bc3': tf.Variable(tf.random_normal([128])),
'bd1': tf.Variable(tf.random_normal([1024])),
'out': tf.Variable(tf.random_normal([n_output]))
}
# Construct model
pred = conv_net(x, weights, biases, keep_prob)
pdb.set_trace()
# pred = tf.pack(tf.transpose(pred,[1,2,0]))
pred = tf.reshape(pred, [-1, n_input, n_input, 2])
temp_pred = tf.reshape(pred, [-1,2])
temp_y = tf.reshape(y, [-1])
# Define loss and optimizer
cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(temp_pred, temp_y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# Evaluate model
# correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
temp_pred2 = tf.reshape(pred, [-1,n_input,n_input])
correct_pred = tf.equal(tf.cast(y,tf.float32),tf.sub(temp_pred2,tf.cast(y,tf.float32)))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
summ = tf.train.SummaryWriter('/tmp/logdir/', sess.graph_def)
step = 1
from tensorflow.contrib.learn.python.learn.datasets.scroll import scroll_data
data = scroll_data.read_data('/home/kendall/Desktop/')
# Keep training until reach max iterations
while step * batch_size < training_iters:
batch_x, batch_y = data.train.next_batch(batch_size)
# Run optimization op (backprop)
batch_x = batch_x.reshape((batch_size, n_input, n_input))
batch_y = batch_y.reshape((batch_size, n_input, n_input))
batch_y = np.int64(batch_y)
# y = tf.reshape(y, [-1,n_input,n_input])
pdb.set_trace()
sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, keep_prob: dropout})
if step % display_step == 0:
# Calculate batch loss and accuracy
pdb.set_trace()
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y, keep_prob: 1.})
print "Iter " + str(step*batch_size) + ", Minibatch Loss= " +
"{:.6f}".format(loss) + ", Training Accuracy= " +
"{:.5f}".format(acc)
step += 1
print "Optimization Finished!"
# Calculate accuracy for 256 mnist test images
print "Testing Accuracy:",
sess.run(accuracy, feed_dict={x: data.test.images[:256],
y: data.test.labels[:256],
keep_prob: 1.})
[1]: http://stackoverflow.com/questions/35317029/how-to-implement-pixel-wise-classification-for-scene-labeling-in-tensorflow/37294185?noredirect=1#comment63253577_37294185
推荐答案
让我们忘记 softmax 并在此处使用更简单的 tf.nn.sigmoid_cross_entropy_with_logits:
Let's forget about softmax and use a simpler tf.nn.sigmoid_cross_entropy_with_logits here:
- 使用 sigmoid，每个像素只需要一个预测
- 如果 pred[pixel] > 0.5，则预测为 1
- 如果 pred[pixel]
pred = conv_net(x, weights, biases, keep_prob) # shape [batch_size, 40000] flattened_y = tf.reshape(y, [-1, 40000]) # shape [batch_size, 40000] loss = tf.nn.sigmoid_cross_entropy_with_logits(pred, flattened_y)这篇关于Tensorflow 重塑张量的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！