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sample_models.py
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sample_models.py
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from keras import backend as K
from keras.models import Model
from keras.layers import (BatchNormalization, Conv1D, Dense, Input,
TimeDistributed, Activation, Bidirectional, SimpleRNN, GRU, LSTM, MaxPooling1D)
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
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 = GRU(units, activation=activation,
return_sequences=True, implementation=2, name='rnn')(input_data)
# TODO: Add batch normalization
bn_rnn = BatchNormalization(name='simp_rnn')(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
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(name='simp_rnn')(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
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'}
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
return (output_length + stride - 1) // stride
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
prev_layer = input_data
for layer in range(recur_layers):
prev_layer = GRU(units, activation='relu',
return_sequences=True, implementation=2, name=f'rnn_{layer+1}')(prev_layer)
# TODO: Add batch normalization
prev_layer = BatchNormalization()(prev_layer)
# TODO: Add a TimeDistributed(Dense(output_dim)) layer
time_dense = TimeDistributed(Dense(output_dim))(prev_layer)
# 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
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, activation='relu',
return_sequences=True, implementation=2, name='bi-rnn'))(input_data)
# 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
def final_model(input_dim, filters, kernel_size, conv_stride,
conv_border_mode, units, recur_layers, output_dim=29):
""" Build a deep network for speech
"""
# Main acoustic input
input_data = Input(name='the_input', shape=(None, input_dim))
# TODO: Specify the layers in your network
# Add convolutional layer
conv_1d = Conv1D(filters, kernel_size,
strides=conv_stride,
padding=conv_border_mode,
activation='relu',
name='conv1d')(input_data)
pooled_1d = MaxPooling1D(pool_size=2, strides=1)(conv_1d)
# Add batch normalization
bn_cnn = BatchNormalization(name='bn_conv_1d')(pooled_1d)
prev_layer = bn_cnn
for layer in range(recur_layers):
prev_layer = Bidirectional(GRU(units, activation='relu',
return_sequences=True, implementation=2, name=f'gru_{layer+1}'))(prev_layer)
# TODO: Add batch normalization
prev_layer = BatchNormalization()(prev_layer)
# TODO: Add a TimeDistributed(Dense(output_dim)) layer
time_dense = TimeDistributed(Dense(output_dim))(prev_layer)
# TODO: Add softmax activation layer
y_pred = Activation('softmax', name='softmax')(time_dense)
# Specify the model
model = Model(inputs=input_data, outputs=y_pred)
# TODO: Specify model.output_length
model.output_length = lambda x: cnn_output_length(
x, kernel_size, conv_border_mode, conv_stride)/2
print(model.summary())
return model