vec] ▪ h/c: [num_layer, b, h] ▪ out: [seq, b, h] nn.LSTM nn.LSTMCell ▪ __init__ LSTMCell.forward() ▪ ht, ct = lstmcell(xt, [ht_1, ct_1]) ▪ xt: [b, vec] ▪ ht/ct: [b, h] Single layer Two Layers 下一课时

RNN Layer使用 主讲人：龙良曲 Folded model feature ??@??ℎ + ℎ?@?ℎℎ [0,0,0 … ] x: ??? ???, ????ℎ, ??????? ??? ????ℎ, ??????? ??? @[ℎ????? ???, ??????? ???]?+ ????ℎ, ℎ????? ??? @ ℎ????? ???, ℎ????? ??? ? layers, b, h dim] ▪ out: [seq len, b, h dim] Single layer RNN feature ??@??ℎ 1 + ℎ? 1@?ℎℎ 1 [0,0,0 … ] ℎ? 1@??ℎ 2 + ℎ? 2@?ℎℎ 2 [0,0,0 … ] 2 layer RNN [T, b, h_dim], [layers, b, h_dim] nn.RNNCell

..................................................................................... 11 Multi-Layer Perceptron ...................................................................................... .............................................................. 17 Keras iv Layer ................................................................................................. ..................................................................................... 35 Dense Layer .................................................................................................

saw earlier the points are linearly separable. We can train a model with a single fully connected layer followed by a softmax activation, since it is a binary classification task. An important caveat is fourth step, we train a model which trains the embedding table along with it. We use a single hidden layer network9 with a softmax classification head for this task. The size of the softmax classification apply in our case here. Step 1: Vocabulary Creation In this step, we will use a TextVectorization layer from Tensorflow to create a vocabulary of the most relevant words. It finds the top N words in a dataset

algorithm and the training batch size. Other aspects of the training pipeline like data augmentation, layer and channel configurations can also be parameterized using hyperparameters. For example, when using additional parameters which could be searched as well. transformation parameters in data augmentation layer contribute to performance improvements while others like learning rate, batch size or momentum are Search In this exercise, we will train a model with a pair of hyperparameters: layer size and learning rate. The layer size determines the model size and the learning rate is used by the model optimizer

4.2.3.10 predict_generator . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.2.3.11 get_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.3 函数式 API . . . . . . . . . 4.3.3.10 predict_generator . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.3.3.11 get_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5 关于 Keras 网络层 58 5.1 关于 Keras 一个「节点」，将输入张量连接到输出张量。当多次调用同一个图层时，该图层将拥有多个节点 索引 (0, 1, 2…)。 在之前版本的 Keras 中，可以通过 layer.get_output() 来获得层实例的输出张量，或者通 过 layer.output_shape 来获取其输出形状。现在你依然可以这么做（除了 get_output() 已经 被 output 属性替代）。但是如果一个层与多个输入连接呢？

(Contd.) • The architecture of feedforward neural networks • Input layer, hidden layers (consisting of hidden units), and output layer 7 / 19 Neural Feedforward Networks (Contd.) • We approximate f ∗(x) ∗(x) by learning f(x) from the given training data • In the output layer, f(x) ≈ y for each training data, but the behavior of the other layers is not directly specified by the training data • Learning intermediate layers such that right results can be obtained in the output layer, but the training data do not say what each individual layer should do • The only thing we must provide to the neural network is

2164, -3.2164]]) 其中?和?张量均是矩阵，上述代码实现了一个线性变换的网络层，激活函数为空。一般 地，?(?@? + ?)网络层称为全连接层(Fully Connected Layer)，在 PyTorch 中可以通过 Linear 类直接实现，特别地，当激活函数?为空时，全连接层也称为线性层。比如，通过 Linear 类创建输入 4 个节点，输出 3 个节点的网络层，并通过全连接层的 Processing，简称 NLP)中句子的表示，如评价句 子的是否为正面情绪的情感分类任务网络，如图 4.3 所示。为了能够方便字符串被神经网 络处理，一般将单词通过嵌入层(Embedding Layer)编码为固定长度的向量，比如“a”编码 为某个长度 3 的向量，那么 2 个等长(单词数量为 5)的句子序列可以表示为 shape 为[2,5,3] 的 3 维张量，其中 2 表示句子个数，5 表示单词数量，3 创建卷积神经网络 layer = nn.Conv2d(3, 16, kernel_size=3) out = layer(x) # 前向计算 out.shape # 输出大小 Out[48]: torch.Size([4, 16, 30, 30]) 其中卷积核张量?也是 4 维张量，可以通过 weight 成员变量访问： In [49]: layer.weight.shape

Validation Testing Step 2. torch.nn.Module Load Data torch.nn – Network Layers ● Linear Layer (Fully-connected Layer) nn.Linear(in_features, out_features) Input Tensor * x 32 Output Tensor * x 64 torch.nn – Network Layers ● Linear Layer (Fully-connected Layer) ref: last year's lecture video torch.nn – Neural Network Layers ● Linear Layer (Fully-connected Layer) x2 x1 x3 x32 y2 y1 y3 y64 torch.nn – Network Parameters ● Linear Layer (Fully-connected Layer) >>> layer = torch.nn.Linear(32, 64) >>> layer.weight.shape torch.Size([64, 32]) >>> layer.bias.shape torch.Size([64]) W (64x32)

us consider an arbitrary neural network layer. We can abstract it using a function with an input and parameters such that . In the case of a fully-connected layer, is a 2-D matrix. Further, assume that mobile device. To summarize, compression techniques help to achieve an efficient representation of a layer or a collection of layers, such that it meets the desired tradeoff goals. In the next section we introduce Exercise: Quantization simulation for a single fully connected layer. Can you simulate the compression of a single fully connected layer using quantization? You can leverage the np.random.uniform() function