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
