请解释一下seq.add(Conv2D(64, 5, activation='relu', padding='same', name='conv1', input_shape=input_dim))
时间: 2024-01-25 16:01:48 浏览: 29
这是一段深度学习中的代码,它使用了 Conv2D 层来实现一个二维卷积。其中,64 是滤波器数量,5 是滤波器大小,'relu' 是激活函数,'same' 是填充方式,'conv1' 是该层的名称,input_dim 是输入的维度。这段代码的作用是对输入的图像进行卷积操作,并使用 ReLU 函数来激活输出。
相关问题
帮我用pytorch改写:def make_generator_model(): model = tf.keras.Sequential() model.add(layers.Input(shape=(100, 12))) model.add(layers.Bidirectional(layers.LSTM(64, return_sequences=True))) model.add(layers.Conv1D(filters=128, kernel_size=16, strides=1, padding='same')) model.add(layers.LeakyReLU()) model.add(layers.Conv1D(filters=64, kernel_size=16, strides=1, padding='same')) model.add(layers.LeakyReLU()) model.add(layers.UpSampling1D(2)) model.add(layers.Conv1D(filters=32, kernel_size=16, strides=1, padding='same')) model.add(layers.LeakyReLU()) model.add(layers.Conv1D(filters=16, kernel_size=16, strides=1, padding='same')) model.add(layers.LeakyReLU()) model.add(layers.UpSampling1D(2)) model.add(layers.Conv1D(filters=1, kernel_size=16, strides=1, padding='same', activation='tanh')) model.add(layers.Permute((2, 1))) return model def make_discriminator_model(): model = tf.keras.Sequential() model.add(layers.Input(shape=(1, 400))) model.add(layers.Permute((2, 1))) model.add(layers.Conv1D(filters=32, kernel_size=16, strides=1, padding='same')) model.add(layers.LeakyReLU()) # model.add(layers.Dropout(0.4)) model.add(layers.Conv1D(filters=64, kernel_size=16, strides=1, padding='same')) model.add(layers.LeakyReLU()) model.add(layers.MaxPool1D(pool_size=2)) model.add(layers.Conv1D(filters=128, kernel_size=16, strides=1, padding='same')) model.add(layers.LeakyReLU()) # model.add(layers.Dropout(0.4)) model.add(layers.Conv1D(filters=256, kernel_size=16, strides=1, padding='same')) model.add(layers.LeakyReLU()) model.add(layers.MaxPool1D(pool_size=2)) model.add(layers.Flatten()) model.add(layers.Dense(1)) return model
以下是使用PyTorch重写的make_generator_model和make_discriminator_model函数:
```python
import torch.nn as nn
def make_generator_model():
model = nn.Sequential(
nn.LSTM(12, 64, bidirectional=True, batch_first=True),
nn.Conv1d(128, 128, kernel_size=16, stride=1, padding=8),
nn.LeakyReLU(),
nn.Conv1d(64, 64, kernel_size=16, stride=1, padding=8),
nn.LeakyReLU(),
nn.Upsample(scale_factor=2),
nn.Conv1d(32, 32, kernel_size=16, stride=1, padding=8),
nn.LeakyReLU(),
nn.Conv1d(16, 16, kernel_size=16, stride=1, padding=8),
nn.LeakyReLU(),
nn.Upsample(scale_factor=2),
nn.Conv1d(1, 1, kernel_size=16, stride=1, padding=8),
nn.Tanh(),
nn.Permute((0, 2, 1))
)
return model
def make_discriminator_model():
model = nn.Sequential(
nn.Conv1d(400, 32, kernel_size=16, stride=1, padding=8),
nn.LeakyReLU(),
nn.Conv1d(32, 64, kernel_size=16, stride=1, padding=8),
nn.LeakyReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Conv1d(64, 128, kernel_size=16, stride=1, padding=8),
nn.LeakyReLU(),
nn.Conv1d(128, 256, kernel_size=16, stride=1, padding=8),
nn.LeakyReLU(),
nn.MaxPool1d(kernel_size=2),
nn.Flatten(),
nn.Linear(256 * 25, 1)
)
return model
```
请注意,这里使用了PyTorch中的模块替换了TensorFlow中的模块。在PyTorch中,nn.LSTM和nn.Conv1d的默认输入格式是(batch_size, seq_len, input_size)和(batch_size, input_channels, seq_len)。另外,使用nn.Linear替换了最后的全连接层。
def model(self): num_classes = self.config.get("CNN_training_rule", "num_classes") seq_length = self.config.get("CNN_training_rule", "seq_length") conv1_num_filters = self.config.get("CNN_training_rule", "conv1_num_filters") conv1_kernel_size = self.config.get("CNN_training_rule", "conv1_kernel_size") conv2_num_filters = self.config.get("CNN_training_rule", "conv2_num_filters") conv2_kernel_size = self.config.get("CNN_training_rule", "conv2_kernel_size") hidden_dim = self.config.get("CNN_training_rule", "hidden_dim") dropout_keep_prob = self.config.get("CNN_training_rule", "dropout_keep_prob") model_input = keras.layers.Input((seq_length,1), dtype='float64') # conv1形状[batch_size, seq_length, conv1_num_filters] conv_1 = keras.layers.Conv1D(conv1_num_filters, conv1_kernel_size, padding="SAME")(model_input) conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1) max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2) full_connect = keras.layers.Dense(hidden_dim)(max_poolinged) droped = keras.layers.Dropout(dropout_keep_prob)(full_connect) relued = keras.layers.ReLU()(droped) model_output = keras.layers.Dense(num_classes, activation="softmax")(relued) model = keras.models.Model(inputs=model_input, outputs=model_output) # model.compile(loss="categorical_crossentropy", # optimizer="adam", # metrics=["accuracy"]) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary()) return model给这段代码每行加上注释
```
def model(self):
# 获取配置文件中的参数
num_classes = self.config.get("CNN_training_rule", "num_classes") #分类数
seq_length = self.config.get("CNN_training_rule", "seq_length") #序列长度
conv1_num_filters = self.config.get("CNN_training_rule", "conv1_num_filters") #第一层卷积核数量
conv1_kernel_size = self.config.get("CNN_training_rule", "conv1_kernel_size") #第一层卷积核大小
conv2_num_filters = self.config.get("CNN_training_rule", "conv2_num_filters") #第二层卷积核数量
conv2_kernel_size = self.config.get("CNN_training_rule", "conv2_kernel_size") #第二层卷积核大小
hidden_dim = self.config.get("CNN_training_rule", "hidden_dim") #全连接层隐藏层大小
dropout_keep_prob = self.config.get("CNN_training_rule", "dropout_keep_prob") #dropout保留率
# 定义模型输入
model_input = keras.layers.Input((seq_length,1), dtype='float64')
# 第一层卷积
conv_1 = keras.layers.Conv1D(conv1_num_filters, conv1_kernel_size, padding="SAME")(model_input)
# 第二层卷积
conv_2 = keras.layers.Conv1D(conv2_num_filters, conv2_kernel_size, padding="SAME")(conv_1)
# 全局最大池化
max_poolinged = keras.layers.GlobalMaxPool1D()(conv_2)
# 全连接层
full_connect = keras.layers.Dense(hidden_dim)(max_poolinged)
# dropout层
droped = keras.layers.Dropout(dropout_keep_prob)(full_connect)
# relu激活层
relued = keras.layers.ReLU()(droped)
# 输出层
model_output = keras.layers.Dense(num_classes, activation="softmax")(relued)
# 定义模型
model = keras.models.Model(inputs=model_input, outputs=model_output)
# 编译模型
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# 打印模型结构
print(model.summary())
return model
```
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