Machine learning 使用子类化的深层Keras RNN
我正在做一个语音识别项目。我想构建一个具有可变数量RNN层的深层RNN 我成功地构建并测试了这个简单的模型 一项实验性培训工作产生了一条有希望的损失曲线（模型_1曲线） 因此，我决定使用子分类方法，以便将来更容易使用L层RNN构建此类模型 这个方法以前在一个更简单的问题上和我一起使用过，但我不确定为什么它不适用于前面提到的定义Machine learning 使用子类化的深层Keras RNN,machine-learning,keras,tensorflow2.0,subclass,recurrent-neural-network,Machine Learning,Keras,Tensorflow2.0,Subclass,Recurrent Neural Network,我正在做一个语音识别项目。我想构建一个具有可变数量RNN层的深层RNN 我成功地构建并测试了这个简单的模型 一项实验性培训工作产生了一条有希望的损失曲线（模型_1曲线） 因此，我决定使用子分类方法，以便将来更容易使用L层RNN构建此类模型 这个方法以前在一个更简单的问题上和我一起使用过，但我不确定为什么它不适用于前面提到的定义 class DeepRNN_Transcription(tf.keras.Model): def __init__(self, input_dim
class DeepRNN_Transcription(tf.keras.Model):
def __init__(self, input_dimension = 161, output_dimension = 29,
RNN_Mode = ['GRU'], HiddenNeurons = [200],
HiddenActivations = ['tanh']):
"""
Initialize fully customized deep neural network for Voice Recogintion.
The default numbers are for English Trasncription.
Input >> RNN1 >> Batch Normalizer 1 >> .... >>
RNN L >> Batch Normalizer L >> TimeDistriuted >> Softmax
Parameters
----------
input_dimension : int, optional
Total number of features for the given record.
The default is 161 extracted using Spectograms.
Another possibility could be N using MFCC (e.g., N = 13)
output_dimension : int, optional
Total number of target characters.
The default is 29 (26 char plus [space, ' and empty character for padding purposes ])
RNN_Mode : list, optional
Type of RNN Cell. The default is ['GRU']. Other options are 'LSTM' and 'SimpleRNN'
HiddenNeurons : list, optional
Number of neurons per layer. The default is [200].
HiddenActivations : TYPE, optional
Activation function per layer. The default is ['tanh'].
Returns
-------
None.
"""
super().__init__()
tf.keras.backend.clear_session()
self.inputlayer = tf.keras.layers.Input(name='the_input',
shape=(None, input_dimension))
i = 0
self.hidden_layers = dict()
for i, (d, a) in enumerate(zip(HiddenNeurons, HiddenActivations)):
self.hidden_layers['hidden_'+str(i)] = tf.keras.layers.GRU(
units = a, activation = a,
return_sequences=True,
implementation=2)
self.hidden_layers['Batch_'+str(i)] = tf.keras.layers.BatchNormalization()
self.TimeDistributed = tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(output_dimension))
self.outputlayer = tf.keras.layers.Dense(output_dimension, activation = 'softmax')
self.num_layers = len(HiddenNeurons) + 3
def call(self, inputtensor):
"""
Perform forward evaluation for the constructed Neural network given
input tensor. This method is called whenever you build the model.
Parameters
----------
inputtensor : tuple
Shape of the input tensor.
Returns
-------
x : tensor
The output generated from the forward evaluation.
"""
x = self.inputlayer(inputtensor)
for k, v in self.hidden_layers.items():
x = v(x)
x = self.outputlayer(x)
return x
def detailed_print(self):
"""
print Weights and Biases Details of the constucted model.
Returns
-------
None.
"""
for i, layer in enumerate(self.layers):
if len(layer.get_weights()) > 0:
w = layer.get_weights()[0]
b = layer.get_weights()[1]
print('\nLayer {}: {}\n'.format(i, layer.name))
print('\u2022 Weights:\n', w)
print('\n\u2022 Biases:\n', b)
print('\nThis layer has a total of {:,} weights and {:,} biases'.format(w.size, b.size))
print('\n------------------------')
else:
print('\nLayer {}: {}\n'.format(i, layer.name))
print('This layer has no weights or biases.')
print('\n------------------------')
Model1 = DeepRNN_Transcription()
Model1.build((None,161))
ValueError: You cannot build your model by calling `build` if your layers do not support float type inputs. Instead, in order to instantiate and build your model, `call` your model on real tensor data (of the correct dtype).
ValueError: You cannot build your model by calling `build` if your layers do not support float type inputs. Instead, in order to instantiate and build your model, `call` your model on real tensor data (of the correct dtype).