tensorflow neural network

Case code: #建立抽象模型x = Tf.placeholder (Tf.float32, [None, 784])y = Tf.placeholder (Tf.float32, [None, ten]) #实际分布的概率值w = tf. Variable (Tf.zeros ([784, 10])b = tf. Variable (Tf.zeros (10))A = Tf.nn.softmax (Tf.matmul (x, W) + b) #基于softmax多分类得到的预测概率#定义损失函数和训练方法Cross_entropy = Tf.reduce_mean (-tf.reduce_sum (Y * tf.log (a), reduction_indices=[1])) #交叉熵Optimizer = Tf.train.GradientDescentOptimizer (0.5) #梯度下降优化算法, learning step is 0.5Train = Optimizer.minimize (cross_entropy) #训练目标: Minimizing loss

Original address: http://www.sohu.com/a/198477100_633698 The text extracts from the vernacular depth study and TensorFlow With the continuous research and attempt on neural network technology, many new network structures or models are born every year. Most of these models have the characteristics of classical

Https://zhuanlan.zhihu.com/p/24720659?utm_source=tuicoolutm_medium=referral Author: YjangoLink: https://zhuanlan.zhihu.com/p/24720659Source: KnowCopyright belongs to the author. Commercial reprint please contact the author to obtain authorization, non-commercial reprint please indicate the source. Everyone seems to be called recurrent neural networks is a circular neural

Main reference: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ RNN (recurrent neuralnetworks, cyclic neural network) For a common neural network, the previous information does not have an impact on the current understanding, for example, reading an article, we need to use the vocabulary learned before, and t

between the filter parameters are not the same.) Sharing the parameters of the filter allows the content in the image to be unaffected by the position. Take mnist handwritten numeral recognition as an example, whether the number "1" appears in the upper left or bottom right corner, the type of picture is unchanged. Sharing the parameters of the convolution filter can also drastically reduce the parameters on the neural

through its axis bursts send a faint current to other neurons. This is a nerve that connects to the input nerve or to another neuron's dendrites, and the neuron then receives the message to do some calculations. It has the potential to transmit its own messages on the axon to other neurons. This is the model of all human thinking: our neurons compute the messages we receive and pass information to other neurons. This is how we feel and how our muscles work, and if you want to live a muscle, it

)] (https://arxiv.org/abs/ 1606.05897v1.pdf). Neural style becomes a very interesting deep learning application: Enter a picture representing the content and a picture representing the style, and the deep Learning Network will output a new piece of work that blends this style and content. TensorFlow is the most popular deep learning framework for Google Open sour

The biggest problem with full-attached neural networks (Fully connected neural network) is that there are too many parameters for the full-connection layer. In addition to slowing down the calculation, it is easy to cause overfitting problems. Therefore, a more reasonable neural ne