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A brief discussion on tensorflow1.0 pooling layer (pooling) and fully connected layer (dense)
A brief discussion on tensorflow1.0 pooling layer (pooling) and fully connected layer (dense)
This article mainly introduces a brief discussion of the pooling layer (pooling) and fully connected layer (dense) of tensorflow 1.0. Now I will share it with you and give you a reference. Let’s take a look together
The pooling layer is defined in tensorflow/python/layers/pooling.py.
There are maximum pooling and mean pooling.
1. tf.layers.max_pooling2d
max_pooling2d( inputs, pool_size, strides, padding='valid', data_format='channels_last', name=None )
inputs: Pooled data.
pool_size: pooled core size (pool_height, pool_width), such as [3, 3]. If the length and width are equal, it can also be set directly to a number, such as pool_size=3.
strides: The sliding stride of pooling. It can be set to two integers like [1,1]. It can also be set directly to a number, such as strides=2
padding: edge padding, 'same' and 'valid' Choose one. The default is valid
data_format: Input data format, the default is channels_last, which is (batch, height, width, channels), it can also be set to channels_first corresponding to (batch, channels, height, width ).
name: The name of the layer.
Example:
pool1=tf.layers.max_pooling2d(inputs=x, pool_size=[2, 2], strides=2)
is usually placed after the convolutional layer, such as:
conv=tf.layers.conv2d( inputs=x, filters=32, kernel_size=[5, 5], padding="same", activation=tf.nn.relu) pool=tf.layers.max_pooling2d(inputs=conv, pool_size=[2, 2], strides=2)
2.tf.layers.average_pooling2d
average_pooling2d( inputs, pool_size, strides, padding='valid', data_format='channels_last', name=None )
The parameters are the same as the previous maximum pooling.
The fully connected dense layer is defined in tensorflow/python/layers/core.py.
3, tf.layers.dense
dense( inputs, units, activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, trainable=True, name=None, reuse=None )
inputs: Input data, 2-dimensional tensor.
units: The number of neural unit nodes in this layer.
activation: activation function.
use_bias: Boolean type, whether to use the bias term.
kernel_initializer: The initializer of the convolution kernel.
bias_initializer: The initializer of the bias term, the default initialization is 0.
kernel_regularizer : Regularization of convolution kernel, optional.
bias_regularizer: Regularization of bias term, optional.
activity_regularizer: Output regularization function.
trainable: Boolean type, indicating whether the parameters of this layer participate in training. If true, the variable is added to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).
name: The name of the layer.
reuse: Boolean type, whether to reuse parameters.
Fully connected layer execution operation outputs = activation(inputs.kernel bias)
If the execution result does not want to be activated, Then set activation=None.
Example:
#全连接层 dense1 = tf.layers.dense(inputs=pool3, units=1024, activation=tf.nn.relu) dense2= tf.layers.dense(inputs=dense1, units=512, activation=tf.nn.relu) logits= tf.layers.dense(inputs=dense2, units=10, activation=None)
You can also regularize the parameters of the fully connected layer:
Copy code The code is as follows:
dense1 = tf.layers.dense(inputs=pool3, units=1024, activation=tf.nn.relu，kernel_regularizer=tf.contrib.layers.l2_regularizer(0.003))
Related recommendations:
A brief discussion on saving and restoring the Tensorflow model
Detailed explanation of the three ways to load data into tensorflow
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