网络结构来自https://github.com/nfmcclure/...

Conv1D

import numpy as np
import keras

# 固定随机数种子以复现结果
seed=13
np.random.seed(seed)

# 创建 1 维向量，并扩展维度适应 Keras 对输入的要求， data_1d 的大小为 (1, 25, 1)
data_1d = np.random.normal(size=25)
data_1d = np.expand_dims(data_1d, 0)
data_1d = np.expand_dims(data_1d, 2)

# 定义卷积层
filters = 1 # 卷积核数量为 1
kernel_size = 5 # 卷积核大小为 5
convolution_1d_layer = keras.layers.convolutional.Conv1D(filters, kernel_size, strides=1, padding='valid', input_shape=(25, 1), activation="relu", name="convolution_1d_layer")

# 定义最大化池化层
max_pooling_layer = keras.layers.MaxPool1D(pool_size=5, strides=1, padding="valid", name="max_pooling_layer")

# 平铺层，调整维度适应全链接层
reshape_layer = keras.layers.core.Flatten(name="reshape_layer")

# 定义全链接层
full_connect_layer = keras.layers.Dense(5, kernel_initializer=keras.initializers.RandomNormal(mean=0.0, stddev=0.1, seed=seed), bias_initializer="random_normal", use_bias=True, name="full_connect_layer")

# 编译模型
model = keras.Sequential()
model.add(convolution_1d_layer)
model.add(max_pooling_layer)
model.add(reshape_layer)
model.add(full_connect_layer)

# 打印 full_connect_layer 层的输出
output = keras.Model(inputs=model.input, outputs=model.get_layer('full_connect_layer').output).predict(data_1d)
print(output)

# 打印网络结构
print(model.summary())

最终输出如下

======================卷积结果=========================
[[-0.0131043  -0.11734447  0.13395447 -0.75453871 -0.69782442]]
======================网络结构=========================
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
convolution_1d_layer (Conv1D (None, 21, 1)             6         
_________________________________________________________________
max_pooling_layer (MaxPoolin (None, 17, 1)             0         
_________________________________________________________________
reshape_layer (Flatten)      (None, 17)                0         
_________________________________________________________________
full_connect_layer (Dense)   (None, 5)                 90        
=================================================================
Total params: 96
Trainable params: 96
Non-trainable params: 0
_________________________________________________________________
None

Conv2D

data_size = [10, 10]
data_2d = np.random.normal(size=data_size)
data_2d = np.expand_dims(data_2d, 0)
data_2d = np.expand_dims(data_2d, 3)
print data_2d.shape

# 定义卷积层
conv_size = 2
conv_stride_size = 2
convolution_2d_layer = keras.layers.Conv2D(filters=1, kernel_size=(conv_size, conv_size), strides=(conv_stride_size, conv_stride_size), input_shape=(data_size[0], data_size[0], 1))
# convolution_2d_layer = keras.layers.Conv2D(filter=1, kernel_size=kernel, strides=[1,1], padding="valid", activation="relu", name="convolution_2d_layer", input_shape=(1, data_size[0], data_size[0]))


# 定义最大化池化层
pooling_size = (2, 2)
max_pooling_2d_layer = keras.layers.MaxPool2D(pool_size=pooling_size, strides=1, padding="valid", name="max_pooling_2d_layer")

# 平铺层，调整维度适应全链接层
reshape_layer = keras.layers.core.Flatten(name="reshape_layer")

# 定义全链接层
full_connect_layer = keras.layers.Dense(5, kernel_initializer=keras.initializers.RandomNormal(mean=0.0, stddev=0.1, seed=seed), bias_initializer="random_normal", use_bias=True, name="full_connect_layer")

model_2d = keras.Sequential()
model_2d.add(convolution_2d_layer)
model_2d.add(max_pooling_2d_layer)
model_2d.add(reshape_layer)
model_2d.add(full_connect_layer)

# 打印 full_connect_layer 层的输出
output = keras.Model(inputs=model_2d.input, outputs=model_2d.get_layer('full_connect_layer').output).predict(data_2d)
print("======================卷积结果=========================")
print(output)

# 打印网络结构
print("======================网络结构=========================")
print(model_2d.summary())

输出

======================卷积结果=========================
[[ 0.30173036 -0.10435719 -0.03354734  0.24000235 -0.09962128]]
======================网络结构=========================
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 5, 5, 1)           5         
_________________________________________________________________
max_pooling_2d_layer (MaxPoo (None, 4, 4, 1)           0         
_________________________________________________________________
reshape_layer (Flatten)      (None, 16)                0         
_________________________________________________________________
full_connect_layer (Dense)   (None, 5)                 85        
=================================================================
Total params: 90
Trainable params: 90
Non-trainable params: 0
_________________________________________________________________
None