卷积操作的参数量计算
卷积操作的参数量计算
1.计算一维的卷积参数：
输入：
model = Sequential()
model.add(layers.Conv1D(64, 15, strides=2,input_shape=(178, 1), use_bias=False))
model.add(layers.ReLU())
model.add(layers.Conv1D(64, 3))
model.add(layers.Conv1D(64, 3, strides=2))
model.add(layers.ReLU())
model.add(layers.Conv1D(64, 3))
model.add(layers.Conv1D(64, 3, strides=2)) # [None, 54, 64]
model.add(layers.BatchNormalization())
model.add(layers.LSTM(64, dropout=0.5, return_sequences=True))
model.add(layers.LSTM(64, dropout=0.5, return_sequences=True))
model.add(layers.LSTM(32))
model.add(layers.Dense(5, activation="softmax"))
model.summary()
_________________________________________________________________
输出：
Model: "sequential"
Layer (type) Output Shape Param #
=================================================================
conv1d (Conv1D) (None, 82, 64) 960
_________________________________________________________________
re_lu (ReLU) (None, 82, 64) 0
_________________________________________________________________
conv1d_1 (Conv1D) (None, 80, 64) 12352
_________________________________________________________________
conv1d_2 (Conv1D) (None, 39, 64) 12352
_________________________________________________________________
re_lu_1 (ReLU) (None, 39, 64) 0
_________________________________________________________________
conv1d_3 (Conv1D) (None, 37, 64) 12352
_________________________________________________________________
conv1d_4 (Conv1D) (None, 18, 64) 12352
_________________________________________________________________
batch_normalization (BatchNo (None, 18, 64) 256
_________________________________________________________________
lstm (LSTM) (None, 18, 64) 33024
_________________________________________________________________
lstm_1 (LSTM) (None, 18, 64) 33024
_________________________________________________________________
lstm_2 (LSTM) (None, 32) 12416
_________________________________________________________________
dense (Dense) (None, 5) 165
=================================================================
计算一维的卷积参数：
①第一层：model.add(layers.Conv1D(64, 15, strides=2,input_shape=(178, 1), use_bias=False))
可以看出这个没有偏置b，因此计算 输出通道 * （卷积核面积 * 输入通道+偏置），即64 * （15 *1 *1 +0）=960，
②第二层：model.add(layers.Conv1D(64, 3))
参数：64 * （3 *1 *64 +1）=12352
2.计算二维的卷积核：
输入（部分）：卷积核大小（3*3）
self.h1 = cnn_cell(32, self.inputs)
self.h2 = cnn_cell(64, self.h1)
self.h3 = cnn_cell(128, self.h2)
self.h4 = cnn_cell(128, self.h3, pool=False)
the_inputs (InputLayer) (None, None, 200, 1) 0
输出（部分）：
conv2d_1 (Conv2D) (None, None, 200, 32 320 the_inputs[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, None, 200, 32 128 conv2d_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, None, 200, 32 9248 batch_normalization_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, None, 200, 32 128 conv2d_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, None, 100, 32 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, None, 100, 64 18496 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, None, 100, 64 256 conv2d_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, None, 100, 64 36928 batch_normalization_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, None, 100, 64 256 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, None, 50, 64) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, None, 50, 128 73856 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, None, 50, 128 512 conv2d_5[0][0]
计算二维卷积参数操作一样：
①第一层： 输出通道 * （卷积核面积 * 输入通道+偏置），即32 * （3 *3 *1+1）=320
②第二层：64 * （3 * 3 *32 +1）=18496
以下计算是类似的。。。。。。。。。