使用Keras搭建cnn+rnn， BRNN，DRNN等模型

Keras api 提前知道：

• BatchNormalization, 用来加快每次迭代中的训练速度

Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.

• TimeDistributed, 总的来说TimeDistributed层在每个时间步上均操作了Dense，比单一dense操作更能发现数据集中比较复杂的模式
  
  简单的理解：

1. keras中TimeDistributed的用法
   
   更进一步的理解：
2. How to Use the TimeDistributed Layer for Long Short-Term Memory Networks in Python
3. 1对应的翻译

• Bidrectional, keras封装了的双向包装函数。

Keras 相关导入

from keras import backend as K
from keras.models import Model
from keras.layers import (BatchNormalization, Conv1D, Conv2D, Dense, Input, Dropout,
    TimeDistributed, Activation, Bidirectional, SimpleRNN, GRU, LSTM, MaxPooling1D, Flatten, MaxPooling2D)

RNN

def simple_rnn_model(input_dim, output_dim=29):
    """ Build a recurrent network for speech
    """
    # Main acoustic input
    input_data = Input(name='the_input', shape=(None, input_dim))
    # Add recurrent layer
    simp_rnn = GRU(output_dim, return_sequences=True,
                 implementation=2, name='rnn')(input_data)
    # Add softmax activation layer
    y_pred = Activation('softmax', name='softmax')(simp_rnn)
    # Specify the model
    model = Model(inputs=input_data, outputs=y_pred)
    model.output_length = lambda x: x
    print(model.summary())
    return model

或者直接使用Keras SimpleRNN

rnn + timedistribute

def rnn_model(input_dim, units, activation, output_dim=29):
    """ Build a recurrent network for speech
    """
    # Main acoustic input
    input_data = Input(name='the_input', shape=(None, input_dim))
    # Add recurrent layer
    simp_rnn = LSTM(units, activation=activation,
        return_sequences=True, implementation=2, name='rnn')(input_data)
    # TODO: Add batch normalization
    bn_rnn = BatchNormalization()(simp_rnn)
    # TODO: Add a TimeDistributed(Dense(output_dim)) layer
    time_dense = TimeDistributed(Dense(output_dim))(bn_rnn)
    # Add softmax activation layer
    y_pred = Activation('softmax', name='softmax', )(time_dense)
    # Specify the model
    model = Model(inputs=input_data, outputs=y_pred)
    model.output_length = lambda x: x
    print(model.summary())
    return model

cnn+rnn+timedistribute

def cnn_output_length(input_length, filter_size, border_mode, stride,
                       dilation=1):
    """ Compute the length of the output sequence after 1D convolution along
        time. Note that this function is in line with the function used in
        Convolution1D class from Keras.
    Params:
        input_length (int): Length of the input sequence.
        filter_size (int): Width of the convolution kernel.
        border_mode (str): Only support `same` or `valid`.
        stride (int): Stride size used in 1D convolution.
        dilation (int)
    """
    if input_length is None:
        return None
    assert border_mode in {'same', 'valid', 'causal', 'full'}
    dilated_filter_size = filter_size + (filter_size - 1) * (dilation - 1)
    if border_mode == 'same':
        output_length = input_length
    elif border_mode == 'valid':
        output_length = input_length - dilated_filter_size + 1
    elif border_mode == 'causal':
        output_length = input_length
    elif border_mode == 'full':
        output_length = input_length + dilated_filter_size - 1
    return (output_length + stride - 1) // stride

def cnn_rnn_model(input_dim, filters, kernel_size, conv_stride,
    conv_border_mode, units, output_dim=29):
    """ Build a recurrent + convolutional network for speech
    """
    # Main acoustic input
    input_data = Input(name='the_input', shape=(None, input_dim))
    # Add convolutional layer
    conv_1d = Conv1D(filters, kernel_size,
                     strides=conv_stride,
                     padding=conv_border_mode,
                     activation='relu',
                     name='conv1d')(input_data)
    # Add batch normalization
    bn_cnn = BatchNormalization(name='bn_conv_1d')(conv_1d)
    # Add a recurrent layer
    simp_rnn = SimpleRNN(units, activation='relu',
        return_sequences=True, implementation=2, name='rnn')(bn_cnn)
    # TODO: Add batch normalization
    bn_rnn = BatchNormalization()(simp_rnn)
    # TODO: Add a TimeDistributed(Dense(output_dim)) layer
    time_dense = TimeDistributed(Dense(output_dim))(bn_rnn)
    # Add softmax activation layer
    y_pred = Activation('softmax', name='softmax')(time_dense)
    # Specify the model
    model = Model(inputs=input_data, outputs=y_pred)
    model.output_length = lambda x: cnn_output_length(
        x, kernel_size, conv_border_mode, conv_stride)
    print(model.summary())
    return model

deep rnn + timedistribute

def deep_rnn_model(input_dim, units, recur_layers, output_dim=29):
    """ Build a deep recurrent network for speech
    """
    # Main acoustic input
    input_data = Input(name='the_input', shape=(None, input_dim))
    # TODO: Add recurrent layers, each with batch normalization
    # Add a recurrent layer
    for i in range(recur_layers):
        if i:
            simp_rnn = GRU(units, return_sequences=True,
                           implementation=2)(simp_rnn)
        else:
            simp_rnn = GRU(units, return_sequences=True,
                           implementation=2)(input_data)

    # TODO: Add batch normalization
    bn_rnn = BatchNormalization()(simp_rnn)
    # TODO: Add a TimeDistributed(Dense(output_dim)) layer
    time_dense = TimeDistributed(Dense(output_dim))(bn_rnn)
    # Add softmax activation layer
    y_pred = Activation('softmax', name='softmax')(time_dense)
    # Specify the model
    model = Model(inputs=input_data, outputs=y_pred)
    model.output_length = lambda x: x
    print(model.summary())
    return model

bidirection rnn + timedistribute

def bidirectional_rnn_model(input_dim, units, output_dim=29):
    """ Build a bidirectional recurrent network for speech
    """
    # Main acoustic input
    input_data = Input(name='the_input', shape=(None, input_dim))
    # TODO: Add bidirectional recurrent layer
    bidir_rnn = Bidirectional(GRU(units, return_sequences=True))(input_data)
    bidir_rnn = BatchNormalization()(bidir_rnn)
    # TODO: Add a TimeDistributed(Dense(output_dim)) layer
    time_dense = TimeDistributed(Dense(output_dim))(bidir_rnn)
    # Add softmax activation layer
    y_pred = Activation('softmax', name='softmax')(time_dense)
    # Specify the model
    model = Model(inputs=input_data, outputs=y_pred)
    model.output_length = lambda x: x
    print(model.summary())
    return model

其他：

使用Keras进行深度学习：（五）RNN和双向RNN讲解及实践