Tensorflow 简明教程
TensorFlow - Single Layer Perceptron
要理解单层感知器,重要的是理解人工智能神经网络 (ANN)。人工智能神经网络是一种信息处理系统,其机制源于生物神经回路的功能。一个人工神经网络拥有许多彼此之间相连的处理单元。以下为人工神经网络的示意图 −
该图表表明隐藏单元与外部层进行通信。而输入和输出单元仅通过网络的隐藏层进行通信。
节点的连接模式、层总数和输入与输出之间的节点级别以及每层神经元的数量定义了神经网络的架构。
有两种类型的架构。这些类型关注以下的人工神经网络功能 −
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Single Layer Perceptron
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Multi-Layer Perceptron
Single Layer Perceptron
单层感知器是创建的第一种提出的神经模型。神经元的局部存储器的内容包含一个权向量。单层感知器的计算在输入向量的总和计算上进行,每个向量值都乘以权向量的相应元素。输出中显示的值将是激活函数的输入。
让我们关注于使用 TensorFlow 对图像分类问题实现单层感知器。通过表示“逻辑回归”来说明单层感知器是最好的示例。
现在,让我们考虑训练逻辑回归的以下基本步骤 −
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在训练开始时,使用随机值对权重进行初始化。
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对于训练集的每个元素,都会计算所需的输出与实际输出之间的差值的误差。计算出来的误差用于调整权重。
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重复此流程,直到在整个训练集上产生的误差不大于指定的阈值,或者直到达到最大迭代次数。
用于评估逻辑回归的完整代码如下所述 −
# 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}))