res_seq = [[[], []]]代码含义
时间: 2023-08-17 20:11:54 浏览: 53
这行代码定义了一个名为`res_seq`的二维列表,其中该列表只包含一个元素,该元素也是一个二维列表,其中包含两个空列表。可以表示为:
```
res_seq = [
[
[],
[]
]
]
```
其中,`res_seq[0]`表示该二维列表的唯一元素,`res_seq[0][0]`表示该唯一元素的第一个空列表,`res_seq[0][1]`表示该唯一元素的第二个空列表。
相关问题
foreach(page_mode_vseq[i]) begin forever begin `uvm_do_on_with(flp_if_normal_seq, p_sequencer.flp_if_seqr, { mode_seq == page_mode_vseq[i]; pause_seq == pause_vseq; rf_seq == rf_vseq; }) if((p_sequencer.vif.flp_if_vif.ack_finished == 1) || (p_sequencer.vif.flp_if_vif.transmit_disable == 1)) break; end if(p_sequencer.vif.flp_if_vif.transmit_disable == 1) break; end 如果 transmit_disable 为 1,则整个 foreach 循环也会被中断。添加一些代码使得整个foreach循环被中断之后可以重新开始循环
可以将整个 foreach 循环放在一个无限循环中,如果循环被中断,就在外层循环中重新开始,直到所有元素都被遍历完为止。修改后的代码如下所示:
```
forever begin
foreach (page_mode_vseq[i]) begin
forever begin
`uvm_do_on_with(flp_if_normal_seq, p_sequencer.flp_if_seqr,
{mode_seq == page_mode_vseq[i]; pause_seq == pause_vseq; rf_seq == rf_vseq;})
if (p_sequencer.vif.flp_if_vif.ack_finished == 1 || p_sequencer.vif.flp_if_vif.transmit_disable == 1)
break;
end
if (p_sequencer.vif.flp_if_vif.transmit_disable == 1)
break;
end
end
```
这样,当整个 foreach 循环被中断时,就会重新开始执行整个循环,直到遍历完所有元素为止。
S_inputs = Input(shape=(11,), dtype='int32') #(None,600) O_seq = Embedding(5000, 128)(S_inputs) #(None,600,128) cnn1 = Conv1D(256, 3, padding='same', strides=1, activation='relu')(O_seq) cnn1 = MaxPooling1D(pool_size=3)(cnn1) cnn = cnn1 O_seq = GlobalAveragePooling1D()(cnn) #(None,128) print(O_seq.shape) O_seq = Dropout(0.9)(O_seq) outputs = Dense(1, activation='tanh',kernel_regularizer = tf.keras.regularizers.L2())(O_seq) model = Model(inputs=S_inputs, outputs=outputs) opt = SGD(learning_rate=0.1, decay=0.00001) loss = 'categorical_crossentropy' model.compile(loss=loss, optimizer=opt, metrics=['categorical_accuracy']) print('Train...') h = model.fit(Xtrain, ytrain,batch_size=batch_size,validation_split = 0.2,epochs=5) plt.plot(h.history["loss"], label="train_loss") plt.plot(h.history["val_loss"], label="test_loss") plt.legend() plt.show()给这段代码加注释
# 导入模块
from tensorflow.keras.layers import Input, Embedding, Conv1D, MaxPooling1D, GlobalAveragePooling1D, Dropout, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import SGD
import tensorflow as tf
import matplotlib.pyplot as plt
# 定义输入层
S_inputs = Input(shape=(11,), dtype='int32') #(None,600)
# 创建嵌入层
O_seq = Embedding(5000, 128)(S_inputs) #(None,600,128)
# 创建卷积层并进行池化操作
cnn1 = Conv1D(256, 3, padding='same', strides=1, activation='relu')(O_seq)
cnn1 = MaxPooling1D(pool_size=3)(cnn1)
cnn = cnn1
# 全局平均池化
O_seq = GlobalAveragePooling1D()(cnn) #(None,128)
# 添加 dropout 层
O_seq = Dropout(0.9)(O_seq)
# 创建输出层
outputs = Dense(1, activation='tanh',kernel_regularizer = tf.keras.regularizers.L2())(O_seq)
# 定义模型并进行编译
model = Model(inputs=S_inputs, outputs=outputs)
opt = SGD(learning_rate=0.1, decay=0.00001)
loss = 'categorical_crossentropy'
model.compile(loss=loss, optimizer=opt, metrics=['categorical_accuracy'])
# 输出模型结构
model.summary()
# 训练模型
print('Train...')
h = model.fit(Xtrain, ytrain,batch_size=batch_size,validation_split = 0.2,epochs=5)
# 绘制损失函数曲线
plt.plot(h.history["loss"], label="train_loss")
plt.plot(h.history["val_loss"], label="test_loss")
plt.legend()
plt.show()
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