diff --git a/02.recognize_digits/README.cn.md b/02.recognize_digits/README.cn.md index b36211907eafcc12d418b2eb380de10f8cf512c8..7f00676acd2c2ee4caeeb60704c0a9335302aae4 100644 --- a/02.recognize_digits/README.cn.md +++ b/02.recognize_digits/README.cn.md @@ -239,6 +239,37 @@ def multilayer_perceptron(): return prediction ``` +- 卷积池化层：在LeNet-5中会出现多个卷积-池化的操作，为避免代码重复书写，将串联的卷积-池化写成conv_pool函数。 + +```python +def conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act="relu"): + """ + 定义卷积池化层： + 含有一个卷积层和一个池化层 + Args： + input —— 网络输入 + num_filters —— 卷积核的个数 + filter_size —— 卷积核的大小 + pool_size —— 池化核的大小 + pool_stride —— 池化的步长 + act —— 卷积层的激活函数 + + Return: + out -- 经过卷积池化后的特征图 + """ + conv_out = fluid.layers.conv2d( + input=input, + num_filters=num_filters, + filter_size=filter_size, + act=act) + out = fluid.layers.pool2d( + input=conv_out, + pool_size=pool_size, + pool_stride=pool_stride) + return out +``` + + - 卷积神经网络LeNet-5: 输入的二维图像，首先经过两次卷积层到池化层，再经过全连接层，最后使用以softmax为激活函数的全连接层作为输出层。 ```python @@ -254,7 +285,7 @@ def convolutional_neural_network(): img = fluid.data(name='img', shape=[None, 1, 28, 28], dtype='float32') # 第一个卷积-池化层 # 使用20个5*5的滤波器，池化大小为2，池化步长为2，激活函数为Relu - conv_pool_1 = fluid.nets.simple_img_conv_pool( + conv_pool_1 = conv_pool( input=img, filter_size=5, num_filters=20, @@ -264,7 +295,7 @@ def convolutional_neural_network(): conv_pool_1 = fluid.layers.batch_norm(conv_pool_1) # 第二个卷积-池化层 # 使用50个5*5的滤波器，池化大小为2，池化步长为2，激活函数为Relu - conv_pool_2 = fluid.nets.simple_img_conv_pool( + conv_pool_2 = conv_pool( input=conv_pool_1, filter_size=5, num_filters=50, diff --git a/02.recognize_digits/README.md b/02.recognize_digits/README.md index 47691ef026b341e831cb8d828ad5799e3dd5264e..cd9ec16a97797657faa6540b5222727a2914cb9c 100644 --- a/02.recognize_digits/README.md +++ b/02.recognize_digits/README.md @@ -218,6 +218,36 @@ def multilayer_perceptron(): return prediction ``` +-Conv_pool layer: LeNet-5 has multiple convolution-pooling operations. In order to avoid repeated code writing, the convolution-pooling in series is written as conv_pool function. + +```python +def conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act="relu"): + """ + Define convolution-pooling layer： + Conv_pool layer has a convolutional layer and a pooling layer + Args： + input —— Input + num_filters —— The number of filter + filter_size —— The filter size + pool_size —— The pool kernel size + pool_stride —— The pool stride size + act —— Activation type + + Return: + out -- output + """ + conv_out = fluid.layers.conv2d( + input=input, + num_filters=num_filters, + filter_size=filter_size, + act=act) + out = fluid.layers.pool2d( + input=conv_out, + pool_size=pool_size, + pool_stride=pool_stride) + return out + +``` -Convolutional neural network LeNet-5: The input two-dimensional image first passes through two convolutional layers to the pooling layer, then passes through the fully connected layer, and finally fully connection layer with softmax as activation function is used as output layer. ```python @@ -233,7 +263,7 @@ def convolutional_neural_network(): img = fluid.data(name='img', shape=[None, 1, 28, 28], dtype='float32') # the first convolution-pooling layer # Use 20 5*5 filters, the pooling size is 2, the pooling step is 2, and the activation function is Relu. - conv_pool_1 = fluid.nets.simple_img_conv_pool( + conv_pool_1 = conv_pool( input=img, filter_size=5, num_filters=20, @@ -243,7 +273,7 @@ def convolutional_neural_network(): conv_pool_1 = fluid.layers.batch_norm(conv_pool_1) # the second convolution-pooling layer # Use 20 5*5 filters, the pooling size is 2, the pooling step is 2, and the activation function is Relu. - conv_pool_2 = fluid.nets.simple_img_conv_pool( + conv_pool_2 = conv_pool( input=conv_pool_1, filter_size=5, num_filters=50, diff --git a/02.recognize_digits/index.cn.html b/02.recognize_digits/index.cn.html index d3f82cd1a86bbfdb994213846dc1acf31e7fc7d3..6473f70b8bcbd9f19e27518c66dd4917ee1a286f 100644 --- a/02.recognize_digits/index.cn.html +++ b/02.recognize_digits/index.cn.html @@ -281,6 +281,37 @@ def multilayer_perceptron(): return prediction ``` +- 卷积池化层：在LeNet-5中会出现多个卷积-池化的操作，为避免代码重复书写，将串联的卷积-池化写成conv_pool函数。 + +```python +def conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act="relu"): + """ + 定义卷积池化层： + 含有一个卷积层和一个池化层 + Args： + input —— 网络输入 + num_filters —— 卷积核的个数 + filter_size —— 卷积核的大小 + pool_size —— 池化核的大小 + pool_stride —— 池化的步长 + act —— 卷积层的激活函数 + + Return: + out -- 经过卷积池化后的特征图 + """ + conv_out = fluid.layers.conv2d( + input=input, + num_filters=num_filters, + filter_size=filter_size, + act=act) + out = fluid.layers.pool2d( + input=conv_out, + pool_size=pool_size, + pool_stride=pool_stride) + return out +``` + + - 卷积神经网络LeNet-5: 输入的二维图像，首先经过两次卷积层到池化层，再经过全连接层，最后使用以softmax为激活函数的全连接层作为输出层。 ```python @@ -296,7 +327,7 @@ def convolutional_neural_network(): img = fluid.data(name='img', shape=[None, 1, 28, 28], dtype='float32') # 第一个卷积-池化层 # 使用20个5*5的滤波器，池化大小为2，池化步长为2，激活函数为Relu - conv_pool_1 = fluid.nets.simple_img_conv_pool( + conv_pool_1 = conv_pool( input=img, filter_size=5, num_filters=20, @@ -306,7 +337,7 @@ def convolutional_neural_network(): conv_pool_1 = fluid.layers.batch_norm(conv_pool_1) # 第二个卷积-池化层 # 使用50个5*5的滤波器，池化大小为2，池化步长为2，激活函数为Relu - conv_pool_2 = fluid.nets.simple_img_conv_pool( + conv_pool_2 = conv_pool( input=conv_pool_1, filter_size=5, num_filters=50, diff --git a/02.recognize_digits/index.html b/02.recognize_digits/index.html index 04ff588588e54ae953873470f60f7724a6856492..e8057efad11e8c07fadc6091344babba27a64bb2 100644 --- a/02.recognize_digits/index.html +++ b/02.recognize_digits/index.html @@ -260,6 +260,36 @@ def multilayer_perceptron(): return prediction ``` +-Conv_pool layer: LeNet-5 has multiple convolution-pooling operations. In order to avoid repeated code writing, the convolution-pooling in series is written as conv_pool function. + +```python +def conv_pool(input, num_filters, filter_size, pool_size, pool_stride, act="relu"): + """ + Define convolution-pooling layer： + Conv_pool layer has a convolutional layer and a pooling layer + Args： + input —— Input + num_filters —— The number of filter + filter_size —— The filter size + pool_size —— The pool kernel size + pool_stride —— The pool stride size + act —— Activation type + + Return: + out -- output + """ + conv_out = fluid.layers.conv2d( + input=input, + num_filters=num_filters, + filter_size=filter_size, + act=act) + out = fluid.layers.pool2d( + input=conv_out, + pool_size=pool_size, + pool_stride=pool_stride) + return out + +``` -Convolutional neural network LeNet-5: The input two-dimensional image first passes through two convolutional layers to the pooling layer, then passes through the fully connected layer, and finally fully connection layer with softmax as activation function is used as output layer. ```python @@ -275,7 +305,7 @@ def convolutional_neural_network(): img = fluid.data(name='img', shape=[None, 1, 28, 28], dtype='float32') # the first convolution-pooling layer # Use 20 5*5 filters, the pooling size is 2, the pooling step is 2, and the activation function is Relu. - conv_pool_1 = fluid.nets.simple_img_conv_pool( + conv_pool_1 = conv_pool( input=img, filter_size=5, num_filters=20, @@ -285,7 +315,7 @@ def convolutional_neural_network(): conv_pool_1 = fluid.layers.batch_norm(conv_pool_1) # the second convolution-pooling layer # Use 20 5*5 filters, the pooling size is 2, the pooling step is 2, and the activation function is Relu. - conv_pool_2 = fluid.nets.simple_img_conv_pool( + conv_pool_2 = conv_pool( input=conv_pool_1, filter_size=5, num_filters=50, diff --git a/03.image_classification/README.cn.md b/03.image_classification/README.cn.md index 9a66a037279d1e275f48639ac5577c4dfeca6699..53701d3229c1248c3b4058b7ad8588fe1e16ed41 100644 --- a/03.image_classification/README.cn.md +++ b/03.image_classification/README.cn.md @@ -339,7 +339,7 @@ def train_program(): cost = fluid.layers.cross_entropy(input=predict, label=label) avg_cost = fluid.layers.mean(cost) accuracy = fluid.layers.accuracy(input=predict, label=label) - return [avg_cost, accuracy] + return [avg_cost, accuracy, predict] ``` ## Optimizer Function 配置 @@ -384,7 +384,7 @@ feed_order = ['pixel', 'label'] main_program = fluid.default_main_program() star_program = fluid.default_startup_program() -avg_cost, acc = train_program() +avg_cost, acc, predict = train_program() # Test program test_program = main_program.clone(for_test=True) diff --git a/03.image_classification/README.md b/03.image_classification/README.md index bdeea442a277aea218ca6bf2702016b8f8332f58..6471b3398c19f89ead67342559d6fd6f7618d693 100644 --- a/03.image_classification/README.md +++ b/03.image_classification/README.md @@ -339,7 +339,7 @@ def train_program(): cost = fluid.layers.cross_entropy(input=predict, label=label) avg_cost = fluid.layers.mean(cost) accuracy = fluid.layers.accuracy(input=predict, label=label) - return [avg_cost, accuracy] + return [avg_cost, accuracy, predict] ``` ## Optimizer Function Configuration @@ -387,7 +387,7 @@ feed_order = ['pixel', 'label'] main_program = fluid.default_main_program() star_program = fluid.default_startup_program() -avg_cost, acc = train_program() +avg_cost, acc, predict = train_program() # Test program test_program = main_program.clone(for_test=True) diff --git a/03.image_classification/index.cn.html b/03.image_classification/index.cn.html index c41ceca4ba48139a40f4b6973659269c57f784a6..6c53ac5453c9d9cc13f63c8ccfd8a33c7b688d93 100644 --- a/03.image_classification/index.cn.html +++ b/03.image_classification/index.cn.html @@ -381,7 +381,7 @@ def train_program(): cost = fluid.layers.cross_entropy(input=predict, label=label) avg_cost = fluid.layers.mean(cost) accuracy = fluid.layers.accuracy(input=predict, label=label) - return [avg_cost, accuracy] + return [avg_cost, accuracy, predict] ``` ## Optimizer Function 配置 @@ -426,7 +426,7 @@ feed_order = ['pixel', 'label'] main_program = fluid.default_main_program() star_program = fluid.default_startup_program() -avg_cost, acc = train_program() +avg_cost, acc, predict = train_program() # Test program test_program = main_program.clone(for_test=True) diff --git a/03.image_classification/index.html b/03.image_classification/index.html index e30ad0e9d2e381f9e797702ec8a442c809bc01cf..66c1a657e2ee88463dbd1e4706109aaa7d67d554 100644 --- a/03.image_classification/index.html +++ b/03.image_classification/index.html @@ -381,7 +381,7 @@ def train_program(): cost = fluid.layers.cross_entropy(input=predict, label=label) avg_cost = fluid.layers.mean(cost) accuracy = fluid.layers.accuracy(input=predict, label=label) - return [avg_cost, accuracy] + return [avg_cost, accuracy, predict] ``` ## Optimizer Function Configuration @@ -429,7 +429,7 @@ feed_order = ['pixel', 'label'] main_program = fluid.default_main_program() star_program = fluid.default_startup_program() -avg_cost, acc = train_program() +avg_cost, acc, predict = train_program() # Test program test_program = main_program.clone(for_test=True)