<code data-enlighter-language="python" class="EnlighterJSRAW">model = Sequential() <em># 输入: 3 通道 100x100 像素图像 -> (100, 100, 3) 张量。</em> <em># 使用 32 个大小为 3x3 的卷积滤波器。</em> model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3))) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax'))</code>
深度学习之-LeNET模型的结构
mu = 0
sigma = 0.1
weights = {
'wc1': tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma)),
#truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma)
'wc2': tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma)),
'wf1': tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma)),
'wf2': tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma)),
'out': tf.Variable(tf.truncated_normal(shape=(84, 10), mean = mu, stddev = sigma))}
biases = {
'bc1': tf.Variable(tf.zeros([6])),
'bc2': tf.Variable(tf.zeros([16])),
'bf1': tf.Variable(tf.zeros([120])),
'bf2': tf.Variable(tf.zeros([84])),
'out': tf.Variable(tf.zeros([10]))}
#Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.
out1 = tf.nn.conv2d(x, weights['wc1'], strides=[1, 1, 1, 1], padding='VALID')
out1 = tf.nn.bias_add(out1,biases['bc1'])
#Activation.
out1 = tf.nn.relu(out1)
#Pooling. Input = 28x28x6. Output = 14x14x6.
out1 = tf.nn.max_pool(
out1,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='VALID')
#Layer 2: Convolutional. Output = 10x10x16.
out2 = tf.nn.conv2d(out1, weights['wc2'], strides=[1, 1, 1, 1], padding='VALID')
out2 = tf.nn.bias_add(out2,biases['bc2'])
#Activation.
out2 = tf.nn.relu(out2)
#Pooling. Input = 10x10x16. Output = 5x5x16.
out2 = tf.nn.max_pool(
out2,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='VALID')
#Flatten. Input = 5x5x16. Output = 400.
flat = flatten(out2)
#Layer 3: Fully Connected. Input = 400. Output = 120.
out3 = tf.matmul(flat,weights['wf1'])
out3 = tf.nn.bias_add(out3,biases['bf1'])
#Activation.
out3 = tf.nn.relu(out3)
#Layer 4: Fully Connected. Input = 120. Output = 84.
out4 = tf.matmul(out3,weights['wf2'])
out4 = tf.nn.bias_add(out4,biases['bf2'])
#Activation.
out4 = tf.nn.relu(out4)
#Layer 5: Fully Connected. Input = 84. Output = 10.
logits = tf.matmul(out4,weights['out'])
logits = tf.nn.bias_add(logits,biases['out'])
return logits
Pickle模块的用法
pickle.dump( data, open( "save.p", "wb" ) )data= pickle.load( open( "save.p", "rb" ) )
用Python处理矩阵运算
numpy格式的矩阵乘以一个标量则每个元素都乘以这个标量。
numpy格式的矩阵加上一个标量则每个元素都加上这个标量。
numpy的一般用法
numpy.matmul()的用法：
https://numpy.org/doc/stable/reference/generated/numpy.matmul.html
为计算矢量的运算函数，输入需要为矢量格式。
区别于numpy.dot()
rgb转化为灰度图像（input: (32,32,3), output: (32,32)）
from skimage import color
X_train_gray=color.rgb2gray(X_train[0])
用numpy添加所需维度(input:(32,32), output:(1,32,32,1))：
X_train_gray=X_train_gray.reshape(1,32,32,1)
用Let’s Encrypt配置https网站
以Ubuntu 18.04 LTS + Apache2为例：
python的一些小技巧
if语句的另外一种写法：
is_correct_string = 'Yes' if output == correct_output else 'No'
小桔灯博客开始了！
正如冰心先生的文章中的小桔灯一样，希望这里能成为在昏暗中照亮前行道路的一盏小小的亮灯！