单层感知机和多层感知机_TensorFlow-单层感知器
单层感知机和多层感知机
TensorFlow-单层感知器 (TensorFlow - Single Layer Perceptron)
For understanding single layer perceptron, it is important to understand Artificial Neural Networks (ANN). Artificial neural networks is the information processing system the mechanism of which is inspired with the functionality of biological neural circuits. An artificial neural network possesses many processing units connected to each other. Following is the schematic representation of artificial neural network −
对于理解单层感知器，重要的是理解人工神经网络(ANN)。 人工神经网络是一种信息处理系统，其机制受到生物神经电路功能的启发。 人工神经网络拥有许多相互连接的处理单元。 以下是人工神经网络的示意图-
The diagram shows that the hidden units communicate with the external layer. While the input and output units communicate only through the hidden layer of the network.
该图显示隐藏的单元与外部层通信。 输入和输出单元仅通过网络的隐藏层进行通信。
The pattern of connection with nodes, the total number of layers and level of nodes between inputs and outputs with the number of neurons per layer define the architecture of a neural network.
与节点的连接模式，输入和输出之间的层总数和节点级别以及每层神经元的数量定义了神经网络的体系结构。
There are two types of architecture. These types focus on the functionality artificial neural networks as follows −
有两种类型的体系结构。 这些类型专注于人工神经网络的功能，如下所示-
- Single Layer Perceptron 单层感知器
- Multi-Layer Perceptron 多层感知器
单层感知器 (Single Layer Perceptron)
Single layer perceptron is the first proposed neural model created. The content of the local memory of the neuron consists of a vector of weights. The computation of a single layer perceptron is performed over the calculation of sum of the input vector each with the value multiplied by corresponding element of vector of the weights. The value which is displayed in the output will be the input of an activation function.
单层感知器是创建的第一个提出的神经模型。 神经元局部记忆的内容由权重向量组成。 单层感知器的计算是在输入向量的总和的计算上进行的，每个输入向量的值乘以权重向量的相应元素。 输出中显示的值将是**功能的输入。
Let us focus on the implementation of single layer perceptron for an image classification problem using TensorFlow. The best example to illustrate the single layer perceptron is through representation of “Logistic Regression”.
让我们专注于使用TensorFlow解决图像分类问题的单层感知器的实现。 展示单层感知器的最佳示例是通过“逻辑回归”的表示。
Now, let us consider the following basic steps of training logistic regression −
现在，让我们考虑以下训练逻辑回归的基本步骤-
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The weights are initialized with random values at the beginning of the training.
权重在训练开始时用随机值初始化。
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For each element of the training set, the error is calculated with the difference between desired output and the actual output. The error calculated is used to adjust the weights.
对于训练集的每个元素，将使用期望输出与实际输出之间的差来计算误差。 计算出的误差用于调整权重。
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The process is repeated until the error made on the entire training set is not less than the specified threshold, until the maximum number of iterations is reached.
重复该过程，直到对整个训练集进行的错误不少于指定的阈值为止，直到达到最大迭代次数为止。
The complete code for evaluation of logistic regression is mentioned below −
下面提到用于评估逻辑回归的完整代码-
# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
import tensorflow as tf
import matplotlib.pyplot as plt
# Parameters
learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1
# tf Graph Input
x = tf.placeholder("float", [None, 784]) # mnist data image of shape 28*28 = 784
y = tf.placeholder("float", [None, 10]) # 0-9 digits recognition => 10 classes
# Create model
# Set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# Construct model
activation = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
# Minimize error using cross entropy
cross_entropy = y*tf.log(activation)
cost = tf.reduce_mean\ (-tf.reduce_sum\ (cross_entropy,reduction_indices = 1))
optimizer = tf.train.\ GradientDescentOptimizer(learning_rate).minimize(cost)
#Plot settings
avg_set = []
epoch_set = []
# Initializing the variables init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Training cycle
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
# Loop over all batches
for i in range(total_batch):
batch_xs, batch_ys = \ mnist.train.next_batch(batch_size)
# Fit training using batch data sess.run(optimizer, \ feed_dict = {
x: batch_xs, y: batch_ys})
# Compute average loss avg_cost += sess.run(cost, \ feed_dict = {
x: batch_xs, \ y: batch_ys})/total_batch
# Display logs per epoch step
if epoch % display_step == 0:
print ("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
avg_set.append(avg_cost) epoch_set.append(epoch+1)
print ("Training phase finished")
plt.plot(epoch_set,avg_set, 'o', label = 'Logistic Regression Training phase')
plt.ylabel('cost')
plt.xlabel('epoch')
plt.legend()
plt.show()
# Test model
correct_prediction = tf.equal(tf.argmax(activation, 1), tf.argmax(y, 1))
# Calculate accuracy
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print
("Model accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
输出量 (Output)
The above code generates the following output −
上面的代码生成以下输出-
The logistic regression is considered as a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal or independent variables.
逻辑回归被认为是一种预测分析。 Logistic回归用于描述数据并解释一个因变量和一个或多个名义或自变量之间的关系。
翻译自: https://www.tutorialspoint.com/tensorflow/tensorflow_single_layer_perceptron.htm
单层感知机和多层感知机
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