amd tensorflow

(train_step,feed_dict={x_data:rand_x,y_data:rand_y}) Temp_loss=sess.run (loss,feed_dict={x_data:rand_x,y_data:rand_y})#Add a recordloss_rec.append (Temp_loss)#Print if(i+1)%25==0:Print('Step:%d a=%s b=%s'%(I,str (Sess.run (A)), str (Sess.run (b) )))Print('loss:%s'%str (temp_loss))#decimation Factor[slope]=Sess.run (A)Print(slope) [Intercept]=Sess.run (b) Best_fit=[] forIinchX_vals:best_fit.append (Slope*i+intercept)#x_vals shape (none,1)Plt.plot (X_vals,y_vals,'o', label='Data') Plt.plot (X_

=Tf.reduce_mean (Tf.abs (A)) L2_a_loss=Tf.reduce_mean (Tf.square (A)) E1_term=tf.multiply (elastic_p1,l1_a_loss) e2_term=tf.multiply (Elastic_p2,l2_a_loss)#here A is an irregular shape that corresponds to the array form of the 3,1 loss also expands the arrays formLoss=tf.expand_dims (Tf.add (Tf.add (Tf.reduce_mean (Tf.square (y_target-model_out)), e1_term), e2_term), 0)#Initialize Variablesinit=Tf.global_variables_initializer () sess.run (init)#Gradient Descentmy_opt=Tf.train.GradientDescentOpti

(): Image_size= 224Images=TF. Variable (Tf.random_normal ([Batch_size, Image_size, Image_size,3], Dtype=Tf.float32, StdDev=1e-1)) Keep_prob=Tf.placeholder (tf.float32) predictions, Softmax, FC8, p=inference_op (images, keep_prob) init=tf.global_variables_initializer () config=TF. Configproto () Config.gpu_options.allocator_type='BFC'Sess= TF. Session (config=config) sess.run (init) time_tensorflow_run (sess, predictions, {keep_prob:1.0},"Forward") O

1. Overview A feature column is a bridge between the original data and the model. In general, the essence of artificial intelligence is to do weights and offset operations to determine the shape of the model. Before using the TensorFlow version, the data must be processed in a kind and distributed way before it can be used by the artificial intelligence model. The appearance of feature columns makes the work of data processing much easier. 2, the fun

In general, there are two functions for printing tensorflow variables:tf.trainable_variables () and Tf.all_variables ()The difference is:Tf.trainable_variables () refers to the variables that need to be trainedTf.all_variables () refers to all variables In general, we are more concerned with training variables that need to be trained:It is important to note that the entire graph is initialized when the variable name is output First, print the name of

While DIY players are casting their eyes on Intel's six-generation processor this year, AMD, the Intel's nemesis, also listed a new AMD Fast Dragon II X4 870K processor, which is similar to AMD 860K, shielding the core graphics card, the flagship multicore high performance, Recommended for mainstream gamers who need to be equipped with independent graphics. It i

November 9, 2015 Google Open source of the artificial intelligence platform TensorFlow, but also become the 2015 's most popular open source projects. After 12 iterations from v0.1 to v0.12, Google released its version of TensorFlow 1.0 on February 15, 2017, and hosted the first TensorFlow Dev Summit conference in Mountain View, California, USA.

About TensorFlow a very good article, reprinted from the "TensorFlow deep learning, an article is enough" click to open the link Google is not only the leader in big data and cloud computing, but also has a good practice and accumulation in machine learning and deep learning, and at the end of 2015, open Source was used internally by the deep learning framework TensorF