深入浅出解读卷积神经网络

minibatches=random_mini_batches1(X_train,Y_train,minibatch_size,seed)

for minibatch in minibatches:

(minibatch_X,minibatch_Y)=minibatch

_,minibatch_cost=sess.run([optm,cost],feed_dict={X:minibatch_X,Y:minibatch_Y})

epoch_cost+=minibatch_cost/num_minibatches

if(print_cost==True and epoch % 2==0):

#print("Epoch",'%04d' % (epoch+1),"cost={:.9f}".format(epoch_cost))

print("Cost after epoch %i:%f" % (epoch,epoch_cost))

if(print_cost==True and epoch %1==0):

costs.append(epoch_cost)

print("Train Accuracy:",accuracy.eval({X:X_train,Y:Y_train.T}))

print("Test Accuracy:",accuracy.eval({X:X_test,Y:Y_test.T}))

plt.plot(np.squeeze(costs))

plt.ylabel('cost')

plt.xlabel('iterations(per tens)')

plt.title("learning rate="+str(learning_rate))

plt.show()

parameters=sess.run(parameters)

return parameters

parameters=model(X_train,Y_train,X_test,Y_test)

　　推荐阅读

　　区块链分叉是怎么回事儿？终于懂了

Tech Neo技巧沙龙 | 11月25号，九州云/ZStack与您一路商量云时代收集界线治理实践最终，只有一条链会被保存下>>>详细阅读

地址：http://www.17bianji.com/lsqh/39218.html