Tensorflow中有很多的优化器,其中比较常用的有三种 tf.train.GradientDescentOptimizer、tf.train.MomentumOptimizer、tf.train.AdamOptimizer大家可以了解了解 learning_rate学习率的学习有问题 learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE, global_step, mnist.train.num_examples / BATCH_SIZE, LEARNI...
num_batch = int(mnist.train.num_examples/batch_size)+1 batch_example,batch_label = mnist.train.next_batch(batch_size) # plt.scatter(batch_example,batch_label,c='r') # plt.show() print(batch_label.shape,batch_example.shape) for i in range(num_batch): feed_seed = {x:batch_example...
=2051:raiseValueError('Invalid magic number%din MNIST image file:%s'%(magic,filename))num_images=_read32(bytestream)rows=_read32(bytestream)cols=_read32(bytestream)buf=bytestream.read(rows*cols*num_images)data=numpy.frombuffer(buf,dtype=numpy.uint8)data=data.reshape(num_imag...
# The normalized image pixels value can be more convenient when training.# dtype=np.int64 would be more general when applying to Tensorflow.self.img_train=img_train[0:self.main_train_num]/255.0self.lab_train=lab_train[0:self.main_train_num].astype(np.int)self.img_train_val=img_train[se...
importtensorflowastfimportmatplotlib.pyplotaspltimportnumpyasnpfromtensorflow.examples.tutorials.mnistimportinput_data 首先导入必备的一些包以及MNIST手写体识别包 mnist=input_data.read_data_sets("MNIST_data/",one_hot=True)trainimgs,trainlabels,testimgs,testlabels=mnist.train.images,mnist.train.labels,mnis...
可能是由于以下几个原因: 1. 数据质量不佳:神经网络的训练结果受到输入数据的影响,如果数据质量不好,包括噪声、缺失值或者标签错误等,都会导致神经网络的精度降低。解决方法是对数据进行清洗、预处理和标...
@propertydefnum_examples(self):returnself._num_examples @propertydefepochs_completed(self):returnself._epochs_completeddefnext_batch(self,batch_size,fake_data=False):"""Return the next `batch_size` examples from this data set."""iffake_data:fake_image=[1]*784ifself.one_hot:fake_label=[1...