input_1 = keras.Input(shape=(24, 1)) x = keras.layers.Conv1D(filters=4, kernel_size=11, strides=9, padding='same', activation='relu')(input_1)分析输出
时间: 2023-09-30 20:08:46 浏览: 52
这段代码定义了一个输入shape为(24,1)的一维卷积神经网络。该网络的第一层是一个卷积层,其中卷积核数量为4,卷积核的大小为11,步长为9,padding方式为same,激活函数为ReLU。该卷积层的输入为输入张量input_1。
由于padding方式为same,输入张量的尺寸在经过卷积运算后和输出张量的尺寸相同,即(24,1)。
输出张量的尺寸可以通过以下公式计算:
$$\mathrm{(input\_size - kernel\_size + strides) / strides + 1}$$
其中,input_size为输入张量的尺寸,kernel_size为卷积核的大小,strides为卷积核的步长。根据上述公式,该卷积层的输出张量尺寸为:
$$\mathrm{(24 - 11 + 9) / 9 + 1} = 2$$
因此,该卷积层的输出张量的尺寸为(2,4)。其中,2表示序列长度,4表示卷积核数量。
相关问题
input_1 = keras.Input(shape=(1292, 1)) x = Conv1D(filters=4, kernel_size=11, strides=9, padding='same', activation='relu')(input_1) x = Conv1D(filters=8, kernel_size=9, strides=4, padding='same', activation='relu')(x) x = Conv1D(filters=16, kernel_size=7, strides=4, padding='same', activation='relu')(x) output_1 = keras.layers.Flatten()(x) # TensorSpec(shape=(None, 160) model1 = keras.Model(inputs=input_1, outputs=output_1, name="fs_feature") model1.summary() #怎么改让输出为160维
在上述代码中,最后一层是通过`Flatten()`将多维的输出展平成了一维向量,因此输出的维度为`(None, 160)`,其中`None`表示可以接受任意数量的样本。如果希望输出的维度仍为`(None, 1, 160)`,可以在最后一层的参数中添加`reshape`操作,代码如下所示:
```
input_1 = keras.Input(shape=(1292, 1))
x = Conv1D(filters=4, kernel_size=11, strides=9, padding='same', activation='relu')(input_1)
x = Conv1D(filters=8, kernel_size=9, strides=4, padding='same', activation='relu')(x)
x = Conv1D(filters=16, kernel_size=7, strides=4, padding='same', activation='relu')(x)
x = keras.layers.Flatten()(x)
output_1 = keras.layers.Reshape((1, 160))(x)
model1 = keras.Model(inputs=input_1, outputs=output_1, name="fs_feature")
model1.summary()
```
在上述代码中,`Reshape((1, 160))`将一维向量重新变为了三维张量,其中第一维为样本数量,第二维为1,第三维为160。
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
```