首页 > TAG信息列表 > TensorShape
基于TensorFlow的深度学习（5）
文章目录 1 维度变换1.1 tf.reshape()1.2 tf.transpose()一个小实例1.3 tf.expand_dims()1.4 tf.squeeze_dim 写在前面：本次实验是在Pycharm里面的console写的，方便交互。 1 维度变换 1.1 tf.reshape() tf.reshape(a, [4,-1, 3]).shape 这里的shape表示的是，直接去计算金卷积神经网络
layers.Conv2D In [1]: import tensorflow as tf ...: from tensorflow.keras import layers ########## x必须ndim=4, shape:[b, h, w, c] In [2]: x = tf.random.normal([1, 32, 32, 3]) In [3]: layer = layers.Conv2D(4, kernel_size=5, strides=1, padding='va卷积神经网络
目录2D ConvolutionKernel sizePadding & StrideChannelsFor instanceLeNet-5Pyramid Architecturelayers.Conv2Dweight & biasnn.conv2dGradient?For instance 2D Convolution Kernel size 矩阵卷积 Padding & Stride 步长2 Channels For instance x: [b,28,2池化与采样
目录OutlineReduce DimsubsampleMax/Avg poolingStridesFor instanceupsampleUpSampling2DReLu Outline Pooling upsample ReLU Reduce Dim subsample Max/Avg pooling stride = 2 Strides stride = 1 For instance import tensorflow as tf from tensorflow.ke