1. Conv1D 层
1.1 语法
keras.layers.convolutional.Conv1D(filters, kernel_size, strides=1, padding='valid', dilation_rate=1, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
一维卷积层（即时域卷积），用以在一维输入信号上进行邻域滤波。当使用该层作为首层时，需要提供关键字参数input_shape。例如(10,128)代表一个长为10的序列，序列中每个信号为128向量。而(None, 128)代表变长的128维向量序列。
该层生成将输入信号与卷积核按照单一的空域（或时域）方向进行卷积。如果use_bias=True，则还会加上一个偏置项，若activation不为None，则输出为经过激活函数的输出。
1.2 参数
-
filters：卷积核的数目（即输出的维度）
-
kernel_size：整数或由单个整数构成的list/tuple，卷积核的空域或时域窗长度
-
strides：整数或由单个整数构成的list/tuple，为卷积的步长。任何不为1的strides均与任何不为1的dilation_rate均不兼容
-
padding：补0策略，为“valid”, “same” 或“causal”，“causal”将产生因果（膨胀的）卷积，即output[t]不依赖于input[t+1：]。当对不能违反时间顺序的时序信号建模时有用。参考WaveNet: A Generative Model for Raw Audio, section 2.1.。“valid”代表只进行有效的卷积，即对边界数据不处理。“same”代表保留边界处的卷积结果，通常会导致输出shape与输入shape相同。
-
activation：激活函数，为预定义的激活函数名（参考激活函数），或逐元素（element-wise）的Theano函数。如果不指定该参数，将不会使用任何激活函数（即使用线性激活函数：a(x)=x）
-
dilation_rate：整数或由单个整数构成的list/tuple，指定dilated convolution中的膨胀比例。任何不为1的dilation_rate均与任何不为1的strides均不兼容。
-
use_bias:布尔值，是否使用偏置项
-
kernel_initializer：权值初始化方法，为预定义初始化方法名的字符串，或用于初始化权重的初始化器。参考initializers
-
bias_initializer：权值初始化方法，为预定义初始化方法名的字符串，或用于初始化权重的初始化器。参考initializers
-
kernel_regularizer：施加在权重上的正则项，为Regularizer对象
-
bias_regularizer：施加在偏置向量上的正则项，为Regularizer对象
-
activity_regularizer：施加在输出上的正则项，为Regularizer对象
-
kernel_constraints：施加在权重上的约束项，为Constraints对象
-
bias_constraints：施加在偏置上的约束项，为Constraints对象
1.3 输入 shape
形如（samples，steps，input_dim）的3D张量
1.4 输出 shape
形如（samples，new_steps，nb_filter）的3D张量，因为有向量填充的原因，steps的值会改变
【Tips】可以将Convolution1D看作Convolution2D的快捷版，对例子中（10，32）的信号进行1D卷积相当于对其进行卷积核为（filter_length, 32）的2D卷积。【@3rduncle】
2. MaxPooling1D
2.1 语法
keras.layers.pooling.MaxPooling1D(pool_size=2, strides=None, padding='valid')
2.2 参数
-
pool_size：整数，池化窗口大小
-
strides：整数或None，下采样因子，例如设2将会使得输出shape为输入的一半，若为None则默认值为pool_size。
-
padding：‘valid’或者‘same’
2.3 输入 shape
- 形如（samples，steps，features）的3D张量
2.4 输出 shape
- 形如（samples，downsampled_steps，features）的3D张量
3. 举例
3.1 数据预处理
from keras.datasets import imdb
from keras.preprocessing import sequence
max_features = 10000 # number of words to consider as features
max_len = 500 # cut texts after this number of words (among top max_features most common words)
print('Loading data...')
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
print(len(x_train), 'train sequences')
print(len(x_test), 'test sequences')
print('Pad sequences (samples x time)')
x_train = sequence.pad_sequences(x_train, maxlen=max_len)
x_test = sequence.pad_sequences(x_test, maxlen=max_len)
print('x_train shape:', x_train.shape)
print('x_test shape:', x_test.shape)
outputs：
Loading data... 25000 train sequences 25000 test sequences Pad sequences (samples x time) x_train shape: (25000, 500) x_test shape: (25000, 500)
3.2 数据训练
from keras.models import Sequential
from keras import layers
from keras.optimizers import RMSprop
model = Sequential()
model.add(layers.Embedding(max_features, 128, input_length=max_len))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.MaxPooling1D(5))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(1))
model.summary()
model.compile(optimizer=RMSprop(lr=1e-4),
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit(x_train, y_train,
epochs=10,
batch_size=128,
validation_split=0.2)
outputs：
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding_1 (Embedding) (None, 500, 128) 1280000 _________________________________________________________________ conv1d_1 (Conv1D) (None, 494, 32) 28704 _________________________________________________________________ max_pooling1d_1 (MaxPooling1 (None, 98, 32) 0 _________________________________________________________________ conv1d_2 (Conv1D) (None, 92, 32) 7200 _________________________________________________________________ global_max_pooling1d_1 (Glob (None, 32) 0 _________________________________________________________________ dense_1 (Dense) (None, 1) 33 ================================================================= Total params: 1,315,937 Trainable params: 1,315,937 Non-trainable params: 0 _________________________________________________________________ Train on 20000 samples, validate on 5000 samples Epoch 1/10 20000/20000 [==============================] - 71s 4ms/step - loss: 0.8337 - acc: 0.5092 - val_loss: 0.6874 - val_acc: 0.5654 Epoch 2/10 20000/20000 [==============================] - 73s 4ms/step - loss: 0.6699 - acc: 0.6383 - val_loss: 0.6641 - val_acc: 0.6590 Epoch 3/10 20000/20000 [==============================] - 86s 4ms/step - loss: 0.6234 - acc: 0.7532 - val_loss: 0.6079 - val_acc: 0.7436 Epoch 4/10 20000/20000 [==============================] - 88s 4ms/step - loss: 0.5256 - acc: 0.8075 - val_loss: 0.4843 - val_acc: 0.8060 Epoch 5/10 20000/20000 [==============================] - 80s 4ms/step - loss: 0.4125 - acc: 0.8478 - val_loss: 0.4287 - val_acc: 0.8318 Epoch 6/10 20000/20000 [==============================] - 88s 4ms/step - loss: 0.3495 - acc: 0.8659 - val_loss: 0.4164 - val_acc: 0.8344 Epoch 7/10 20000/20000 [==============================] - 86s 4ms/step - loss: 0.3128 - acc: 0.8601 - val_loss: 0.4437 - val_acc: 0.8184 Epoch 8/10 20000/20000 [==============================] - 82s 4ms/step - loss: 0.2819 - acc: 0.8454 - val_loss: 0.4282 - val_acc: 0.8024 Epoch 9/10 20000/20000 [==============================] - 73s 4ms/step - loss: 0.2574 - acc: 0.8302 - val_loss: 0.4700 - val_acc: 0.7780 Epoch 10/10 20000/20000 [==============================] - 71s 4ms/step - loss: 0.2345 - acc: 0.8064 - val_loss: 0.5111 - val_acc: 0.7470