minimum details to create a complete layer.  Shape of the input data  Number of neurons / units in the layer  Initializers  Regularizers  Constraints  Activations Let us understand the model. Dense is an entry level layer provided by Keras, which accepts the number of neurons or units (32) as its required parameter. If the layer is first layer, then we need to provide Input Shape the next layer. All other parameters are optional.  First parameter represents the number of units (neurons).  input_shape represent the shape of input data.  kernel_initializer represent initializer

基于 PYTHON 的深度学习库 2 from keras.layers import Dense model.add(Dense(units=64, activation='relu', input_dim=100)) model.add(Dense(units=10, activation='softmax')) 在完成了模型的构建后, 可以使用 .compile() 来配置学习过程： model get_output_shape_at(node_index) 关于 KERAS 网络层 59 5.2 核心网络层 5.2.1 Dense [source] keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', 现在模型就会以尺寸为 (*, 16) 的数组作为输入， # 其输出数组的尺寸为 (*, 32) # 在第一层之后，你就不再需要指定输入的尺寸了： model.add(Dense(32)) 参数 • units: 正整数，输出空间维度。 • activation: 激活函数 (详见 activations)。若不指定，则不使用激活函数 (即，“线性” 激活: a(x) = x)。 • use_bias:

the heart of deep learning. AlexNet1 was one of the earliest models to rely on Graphics Processing Units (GPUs) for training, which could 1 Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet for model training and inference have been springing up. For example, Google’s Tensor Processing Units (TPUs) optimize for accelerating large matrix multiplications (specifically they allow massively parallelizing

parameters to structural parameters that can manipulate the structure of a network. The number of dense units, number of convolution channels or the size of convolution kernels can sometimes be 4 Jaderberg, output token is fed as input to the next time step and so on. The figure also shows multiple recurrent units stacked (vertical stack) on top of each other to learn complex relationships between the time steps

performance for common NLP tasks primarily using attention. A transformer replaces the recurrent units in the RNN Encoder-Decoder model with attention layers. It uses two flavors of attention: encoder-decoder a single sequence to compute the relationship between its own elements. It replaces the recurrent units in the encoder and the decoder blocks. Although there are other differences between a RNN Encoder-Decoder

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) by learning

public high- way 30x32 weights into one out of four hidden unit 30 outputs for steering 4 hidden units 30x32 pixels as inputs Feng Li (SDU) Overview September 6, 2023 19 / 57 Example 3: Breast Cancer

活函数。该层需要输入参数：in_units和units，分 别表示输入数和输出数。 class MyLinear(nn.Module): def __init__(self, in_units, units): super().__init__() self.weight = nn.Parameter(torch.randn(in_units, units)) self.bias = nn nn.Parameter(torch.randn(units,)) def forward(self, X): linear = torch.matmul(X, self.weight.data) + self.bias.data return F.relu(linear) 接下来，我们实例化MyLinear类并访问其模型参数。 linear = MyLinear(5, 3) linear 尽管我们已经应用了梯度裁剪等技巧来缓解这 个问题，但是仍需要通过设计更复杂的序列模型来进一步处理它。具体来说，我们将引入两个广泛使用的网 络，即门控循环单元（gated recurrent units，GRU）和 长短期记忆网络（long short‐term memory，LSTM）。 然后，我们将基于一个单向隐藏层来扩展循环神经网络架构。我们将描述具有多个隐藏层的深层架构，并讨 论基于

24 主要的几种神经网络 标准神经网络(NN) 递归神经网络 (RNN) 卷积神经网络(CNN) 25 深度学习的硬件 26 • TPU (Tensor Processing Units) Google Cloud TPU. https://cloud.google.com/tpu NVIDIA V100 TPU v2 TPU v3 Hardware Architecture

pre-trained ResNet50 model with the top (softmax) layer replaced with a new softmax layer with 102 units (one unit for each class). Additionally, we add the recommended resnet preprocessing layer at the