set all_clock_gates_1 [get_cell -hier "*_clock_gate"] set all_clock_gates "" set mem_clcok_gates "" foreach_in_collection clk_gate $all_clock_gates_1 { set clk_gate_name [get_object_name $clk_gate] if {[regexp str $clk_gate_name] == 1} { lappend mem_clock_gates $clk_gate_name } else { lappend all_clock_gates $clk_gate_name } }，翻译以上tcl语句

foreach clk_gate_name $all_crm_gates { set clk_source_crg_col [all_fanin -to ${clk_gate_name}/cp -flat -only_cells -startpoints_only ] set clk_source_crg [get_object_name $clk_source_crg_col] set clk_source [get_object_name [remove_from_collection -intersect [all_fanin -to ${clk_source_crg}/CP -flat -startpoints_only] $clk_ports_list]] create_gennerated_clock [get_pins $clk_gate_name/hand_latch/Q] -source ${clk_source_crg}/Q -divide_by 1 if {$clk_source == "core_clk"} { set_all_core_clks [add_to_collection $all_core_clks [get_clock ${clk_gate_name}/hand_latch/Q] ] } }，翻译

这段代码是一个Tcl脚本代码片段，它的作用与前面的代码类似，不同之处在于处理的是列表变量$all_crm_gates中的元素，并且获取输入时钟信号的方式略有不同。具体来说，这段代码同样使用foreach循环遍历$all_crm_...

innovus set_ccopt_property ccopt_merge_clock_gates false

### 设置 ccopt_merge_clock_gates 属性为 false 的方法在 Innovus 中，可以通过命令行使用 set_ccopt_property 来配置特定的设计优化选项。如果需要将 ccopt_merge_clock_gates 属性设置为 false，可以...

b8051f62-1c9a-4166-ad08-0782fc920019_gates_数据结构C语言_AnyDayNow_

Cars on Campus (100point(s)) Zhejiang University has 8 campuses and a lot of gates. From each gate we can collect the in/out times and the plate numbers of the cars crossing the gate. Now with all the...

clone_clock_gates

好的，用户问的是关于clone_clock_gates技术的实现或应用，我需要先理解这个概念。克隆时钟门控（clone clock gates）应该是在数字IC设计中用来优化时钟树的一种技术。根据提供的引用内容，尤其是引用[1]到[4]，主要...

def gru_cell(self, x, state): # Compute gates x_and_state = torch.cat([x, state], dim=1) update_gate = self.conv_update(x_and_state) reset_gate = self.conv_reset(x_and_state) # Add bias to initialise gate as close to identity function update_gate = torch.sigmoid(update_gate + self.gru_bias_init) reset_gate = torch.sigmoid(reset_gate + self.gru_bias_init) # Compute proposal state, activation is defined in norm_act_config (can be tanh, ReLU etc) state_tilde = self.conv_state_tilde(torch.cat([x, (1.0 - reset_gate) * state], dim=1)) output = (1.0 - update_gate) * state + update_gate * state_tilde return output函数是怎么一步步计算的？

1. 将输入数据x和上一时刻的状态state按照列方向（dim=1）进行拼接，得到x_and_state。 2. 将x_and_state输入到两个卷积层中，分别计算更新门（update_gate）和重置门（reset_gate）。 3. 给计算得到的更新门和...

获取设计中所有包含“_clock_gate”，同时包含“rgx_crg”和“vha_crg"两个中的一个，但不包含”str"的所有cell，sdc语句

set all_clock_gates [get_cells -hier -regexp _clock_gate.* -constraints "!(name =~ str) && (name =~ rgx_crg|vha_crg)"] 这将返回一个包含所有符合条件的Clock Gate Cell名称的列表。您可以根据需要...

class BiCLSTMCell(tf.keras.layers.Layer): def init(self, units, kwargs): self.units = units self.state_size = [units, units] super(BiCLSTMCell, self).init(kwargs) def build(self, input_shape): self.kernel = self.add_weight(shape=(input_shape[-1], self.units * 4), initializer='glorot_uniform', name='kernel') self.recurrent_kernel = self.add_weight(shape=(self.units, self.units * 4), initializer='orthogonal', name='recurrent_kernel') self.bias = self.add_weight(shape=(self.units * 4,), initializer='zeros', name='bias') self.ca_dense1 = Dense(self.units, activation='relu') self.ca_dense2 = Dense(self.units, activation='sigmoid') super(BiCLSTMCell, self).build(input_shape) def call(self, inputs, states): h_tm1, c_tm1 = states z = tf.keras.backend.dot(inputs, self.kernel) z += tf.keras.backend.dot(h_tm1, self.recurrent_kernel) z = tf.keras.backend.bias_add(z, self.bias) z0, z1, z2, z3 = tf.split(z, 4, axis=-1) # Channel Attention Mechanism ca = self.ca_dense1(c_tm1) ca = self.ca_dense2(ca) # LSTM Gates input_gate = tf.keras.activations.sigmoid(z0) forget_gate = tf.keras.activations.sigmoid(z1) * ca output_gate = tf.keras.activations.sigmoid(z2) cell_state = tf.keras.activations.tanh(z3) c = forget_gate * c_tm1 + input_gate * cell_state h = output_gate * tf.keras.activations.tanh(c) return h, [h, c] def get_config(self): config = super(BiCLSTMCell, self).get_config() config.update({'units': self.units}) return config

这是一个自定义的双向LSTM单元类，继承了Keras的Layer类。在初始化方法中，定义了该单元的神经元个数和状态大小。在build方法中，定义了该单元的权重矩阵和...最后，在get_config方法中，返回了该单元的参数配置信息。

class LSTMCell(nn.Module): def init(self, input_dim, hidden_dim): super().init() self.input_gate = nn.Linear(input_dim + hidden_dim, hidden_dim) self.forget_gate = nn.Linear(input_dim + hidden_dim, hidden_dim) self.cell_gate = nn.Linear(input_dim + hidden_dim, hidden_dim) self.output_gate = nn.Linear(input_dim + hidden_dim, hidden_dim) def forward(self, x, h_c_prev): h_prev, c_prev = h_c_prev combined = torch.cat([h_prev, x], dim=-1) f_t = torch.sigmoid(self.forget_gate(combined)) i_t = torch.sigmoid(self.input_gate(combined)) c_hat = torch.tanh(self.cell_gate(combined)) o_t = torch.sigmoid(self.output_gate(combined)) c_t = f_t * c_prev + i_t * c_hat h_t = o_t * torch.tanh(c_t) return (h_t, c_t) 分析代码并且教会我具体如何使用

2.前向传播：-将前一个隐藏状态h_prev和当前输入x拼接（torch.cat([h_prev,x],dim=1)）-分别计算遗忘门、输入门、候选细胞状态、输出门-计算新的细胞状态c_next=forget_gate* c_prev+ input_gate* candidate_cell-...

class ConvRNNCell(object): def call(self, inputs, state, scope=None): raise NotImplementedError("Abstract method") @property def state_size(self): raise NotImplementedError("Abstract method") @property def output_size(self): raise NotImplementedError("Abstract method") def zero_state(self, batch_size, dtype): shape = self.shape num_features = self.num_features zeros = tf.zeros([batch_size, shape[0], shape[1], num_features * 2]) return zeros class BasicConvLSTMCell(ConvRNNCell): def init(self, shape, filter_size, num_features, forget_bias=1.0, input_size=None, state_is_tuple=False, activation=tf.nn.tanh): if input_size is not None: logging.warn("%s: The input_size parameter is deprecated.", self) self.shape = shape self.filter_size = filter_size self.num_features = num_features self._forget_bias = forget_bias self._state_is_tuple = state_is_tuple self._activation = activation @property def state_size(self): return (LSTMStateTuple(self._num_units, self._num_units) if self._state_is_tuple else 2 * self._num_units) @property def output_size(self): return self._num_units def call(self, inputs, state, scope='convLSTM'): """Long short-term memory cell (LSTM).""" with tf.variable_scope(scope or type(self).name): # "BasicLSTMCell" # Parameters of gates are concatenated into one multiply for efficiency. if self._state_is_tuple: c, h = state else: c, h = tf.split(state, 2, 3) concat = _conv_linear([inputs, h], self.filter_size, self.num_features * 4, True) # i = input_gate, j = new_input, f = forget_gate, o = output_gate i, j, f, o = tf.split(concat, 4, 3) new_c = (c * tf.nn.sigmoid(f + self._forget_bias) + tf.nn.sigmoid(i) * self._activation(j)) new_h = self._activation(new_c) * tf.nn.sigmoid(o) if self._state_is_tuple: new_state = LSTMStateTuple(new_c, new_h) else: new_state = tf.concat([new_c, new_h], 3) return new_h, new_state filter_size是怎么定义的

### **1. filter_size 的作用** filter_size 指定了卷积核的高度和宽度，用于从输入数据和隐藏状态中提取**空间特征**。例如： - filter_size=3 表示使用 $3 \times 3$ 的卷积核 - filter_size=(5,5) 表示 $...

gate_cell

例如，在 LSTM 中存在三种主要类型的 gates：输入门 (input gate)，遗忘门 (forget gate) 和输出门 (output gate)[^2]。每种 gate 都由 sigmoid 函数计算得出的概率值乘以 tanh 层产生的候选向量组成，从而实现对...

for (const auto& [gate_desc, inputs] : circuit.gates) 中gate_desc显示未定义标识符

让我仔细看看这个代码片段：for (const auto& [gate_desc, inputs] : circuit.gates)。这看起来像是一个结构化绑定的用法。结构化绑定是C++17引入的特性，允许用分解声明来绑定变量到元组或结构体的成员。首先，我...

--master_port=29501

"--allow-privileged=true", "--eviction-hard=memory.available%,"--eviction-message='Memory limit exceeded'", "--feature-gates=AllAlpha", "--hostname-override=<node-name>", "--kube-api-burst=10", "--...

做综合报找不到clock gate cell

### 综合报告中与 Clock Gate Cell 相关的内容在数字电路设计过程中，综合工具（Synthesis Tool）扮演着至关重要的角色。它负责将硬件描述语言（如 Verilog 或 VHDL）转换为具体的门级网表（Gate-Level Netlist）...

按照TensorFlow2.11的写法修改这段代码：“class tgcnCell(RNN): """Temporal Graph Convolutional Network """ def call(self, inputs, **kwargs): pass def init(self, num_units, adj, num_nodes, input_size=None, act=tf.nn.tanh, reuse=None): super(tgcnCell, self).init(units=num_units,_reuse=reuse) self._act = act self._nodes = num_nodes self._units = num_units self._adj = [] self._adj.append(calculate_laplacian(adj)) @property def state_size(self): return self._nodes * self._units @property def output_size(self): return self._units def call(self, inputs, state, scope=None): with tf.variable_scope(scope or "tgcn"): with tf.variable_scope("gates"): value = tf.nn.sigmoid( self._gc(inputs, state, 2 * self._units, bias=1.0, scope=scope)) r, u = tf.split(value=value, num_or_size_splits=2, axis=1) with tf.variable_scope("candidate"): r_state = r * state c = self._act(self._gc(inputs, r_state, self._units, scope=scope)) new_h = u * state + (1 - u) * c return new_h, new_h def _gc(self, inputs, state, output_size, bias=0.0, scope=None): inputs = tf.expand_dims(inputs, 2) state = tf.reshape(state, (-1, self._nodes, self._units)) x_s = tf.concat([inputs, state], axis=2) input_size = x_s.get_shape()[2].value x0 = tf.transpose(x_s, perm=[1, 2, 0]) x0 = tf.reshape(x0, shape=[self._nodes, -1]) scope = tf.get_variable_scope() with tf.variable_scope(scope): for m in self._adj: x1 = tf.sparse_tensor_dense_matmul(m, x0) x = tf.reshape(x1, shape=[self._nodes, input_size,-1]) x = tf.transpose(x,perm=[2,0,1]) x = tf.reshape(x, shape=[-1, input_size]) weights = tf.get_variable( 'weights', [input_size, output_size], initializer=tf.contrib.layers.xavier_initializer()) x = tf.matmul(x, weights) # (batch_size * self._nodes, output_size) biases = tf.get_variable( "biases", [output_size], initializer=tf.constant_initializer(bias, dtype=tf.float32)) x = tf.nn.bias_add(x, biases) x = tf.reshape(x, shape=[-1, self._nodes, output_size]) x = tf.reshape(x, shape=[-1, self._nodes * output_size]) return x”

x = tf.reshape(x, [-1, x.shape[-1]]) # [batch*nodes, features+units] # 稀疏矩阵乘法 x = tf.sparse.sparse_dense_matmul(self.laplacian, x) # ✅ 更新稀疏矩阵API x = tf.matmul(x, kernel) # 输出...

langchain4j-anthropic-spring-boot-starter-0.31.0.jar中文文档.zip

1、压缩文件中包含：中文文档、jar包下载地址、Maven依赖、Gradle依赖、源代码下载地址。 2、使用方法：解压最外层zip，再解压其中的zip包，双击【index.html】文件，即可用浏览器打开、进行查看。 3、特殊说明：（1）本文档为人性化翻译，精心制作，请放心使用；（2）只翻译了该翻译的内容，如：注释、说明、描述、用法讲解等；（3）不该翻译的内容保持原样，如：类名、方法名、包名、类型、关键字、代码等。 4、温馨提示：（1）为了防止解压后路径太长导致浏览器无法打开，推荐在解压时选择“解压到当前文件夹”（放心，自带文件夹，文件不会散落一地）；（2）有时，一套Java组件会有多个jar，所以在下载前，请仔细阅读本篇描述，以确保这就是你需要的文件。 5、本文件关键字： jar中文文档.zip,java,jar包,Maven,第三方jar包,组件,开源组件,第三方组件,Gradle,中文API文档,手册,开发手册,使用手册,参考手册。

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b8051f62-1c9a-4166-ad08-0782fc920019_gates_数据结构C语言_AnyDayNow_

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for (const auto& [gate_desc, inputs] : circuit.gates) 中gate_desc显示未定义标识符

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做综合报找不到clock gate cell

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