标签：全连接 imshow res step inline extra generate plot ^c
Cam(Class Activation Mapping)是一个很有意思的算法，他能够将神经网络到底在关注什么可视化的表现出来。但同时它的实现却又如此简介，相比NIN，googLenet这些使用GAP（Global Average Pooling）用来代替全连接层，他却将其输出的权重和featuremap相乘，累加，将其用图像表示出来。
其网络架构如下
Class Activation Mapping具体论文
当然Cam的目的并不仅仅是将其表示出来，神经网络所关注的地方，通常就是物体所在的地方，因此它可以辅助训练检测网络。
因此就有了PlacesNet。
网络上基本都是基于AlexNet等网络，其实任何网络，只要加一层全局池化层就可以帮助我们将CNN关注什么表示出来，因此我对Tensorflow官方Mnist的CNN网络进行少量的修改，实现了CAM。只是将最后的全连接层，改为了全局池化层。
CAM的核心公式很简单
\[
S_c = \sum_{k} {W_k^c} {\sum_{x,y} {f_k(x,y)}}
\]
将全局平均池化层输出的权重乘上feature map累加
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)/home/lyn/anaconda3/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module ‘tensorflow.python.framework.fast_tensor_util‘ does not match runtime version 3.6
return f(*args, **kwds)
Extracting /tmp/data/train-images-idx3-ubyte.gz
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz在读入数据后，设定基本的学习参数
# Training Parameters
learning_rate = 0.001
num_steps = 10000
batch_size = 128
display_step = 10
# Network Parameters
num_input = 784 # MNIST data input (img shape: 28*28)
num_classes = 10 # MNIST total classes (0-9 digits)
# tf Graph input
X = tf.placeholder(tf.float32, [None, num_input])
Y = tf.placeholder(tf.int32, [None, num_classes])# 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‘)在实际使用中我们需要获得得feature map，与全局池化层相乘并累加
def conv_layers(x,weights,biases):
x = tf.reshape(x, shape=[-1, 28, 28, 1])
# Convolution Layer
conv1 = conv2d(x, weights[‘wc1‘], biases[‘bc1‘])
# Max Pooling (down-sampling)
conv1 = maxpool2d(conv1, k=2)
# Convolution Layer
conv2 = conv2d(conv1, weights[‘wc2‘], biases[‘bc2‘])
# Max Pooling (down-sampling)
conv2 = maxpool2d(conv2, k=2)
return conv2
def out_layer(conv2,weights,biases):
gap = tf.nn.avg_pool(conv2,ksize=[1,7,7,1],strides=[1,7,7,1],padding="SAME")
gap = tf.reshape(gap,[-1,128])
out = tf.add(tf.matmul(gap, weights[‘out‘]), biases[‘out‘])
return out
def generate_heatmap(conv2,label,weights):
conv2_resized = tf.image.resize_images(conv2,[28,28])
label_w = tf.gather(tf.transpose(weights[‘out‘]),label)
label_w = tf.reshape(label_w,[-1,128,1])
conv2_resized = tf.reshape(conv2_resized,[-1,28*28,128])
classmap = tf.matmul( conv2_resized, label_w )
classmap = tf.reshape( classmap, [-1, 28,28] )
return classmap# 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, 128])),
‘out‘: tf.Variable(tf.random_normal([128, num_classes]))
}
biases = {
‘bc1‘: tf.Variable(tf.random_normal([32])),
‘bc2‘: tf.Variable(tf.random_normal([128])),
‘bd1‘: tf.Variable(tf.random_normal([1024])),
‘out‘: tf.Variable(tf.random_normal([num_classes]))
}
# Construct model
conv2 = conv_layers(X, weights, biases)
logits = out_layer(conv2,weights, biases)
prediction = tf.nn.softmax(logits)
classmap = generate_heatmap(conv2,tf.argmax(prediction,1),weights)
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
logits=logits, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)
# Evaluate model
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()sess = tf.Session()
sess.run(init)
for step in range(1, num_steps+1):
batch_x, batch_y = mnist.train.next_batch(batch_size)
# Run optimization op (backprop)
sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
if step % display_step == 0 or step == 1:
# Calculate batch loss and accuracy
loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,
Y: batch_y})
print("Step " + str(step) + ", Minibatch Loss= " + \
"{:.4f}".format(loss) + ", Training Accuracy= " + \
"{:.3f}".format(acc))
print("Optimization Finished!")
# Calculate accuracy for 256 MNIST test images
print("Testing Accuracy:", \
sess.run(accuracy, feed_dict={X: mnist.test.images[:256],
Y: mnist.test.labels[:256],}
))
sess.run(conv2,feed_dict={X:mnist.test.images[:10],Y:mnist.test.labels[:10]})
classmaps = sess.run(classmap,feed_dict={X:mnist.test.images[:10],Y:mnist.test.labels[:10]})Testing Accuracy: 0.976562for i in range(9):
plt.subplot(33*10+i+1)
plt.axis("off")
plt.imshow(classmaps[i],cmap="gray")
plt.title("label is"+str(np.argmax(mnist.test.labels[i])))
黑色为正权重点，白色为负权重点。
当然由于网络太浅，使用了全局平均池化以后，训练时间大大增长，准确率也不如之前。
标签：全连接 imshow res step inline extra generate plot ^c
原文地址：http://www.cnblogs.com/lynsyklate/p/7966171.html
