Keras api 提前知道：

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, 總的來(lái)說(shuō)TimeDistributed層在每個(gè)時(shí)間步上均操作了Dense，比單一dense操作更能發(fā)現(xiàn)數(shù)據(jù)集中比較復(fù)雜的模式

簡(jiǎn)單的理解：

Bidrectional, keras封裝了的雙向包裝函數(shù)。

Keras 相關(guān)導(dǎo)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

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