def __build_encoder_decoder_controller( self, input_neuron_count=20, hidden_neuron_count=20, weight_limit=0.5, dropout_rate=0.5, epochs=1000, batch_size=20, learning_rate=1e-05, attenuate_epoch=50, learning_attenuate_rate=0.1, seq_len=8, bptt_tau=8, test_size_rate=0.3, tol=1e-10, tld=100.0 ): encoder_graph = EncoderGraph() encoder_graph.observed_activating_function = LogisticFunction() encoder_graph.input_gate_activating_function = LogisticFunction() encoder_graph.forget_gate_activating_function = LogisticFunction() encoder_graph.output_gate_activating_function = LogisticFunction() encoder_graph.hidden_activating_function = LogisticFunction() encoder_graph.output_activating_function = LogisticFunction() encoder_graph.create_rnn_cells( input_neuron_count=input_neuron_count, hidden_neuron_count=hidden_neuron_count, output_neuron_count=1 ) encoder_opt_params = EncoderAdam() encoder_opt_params.weight_limit = weight_limit encoder_opt_params.dropout_rate = dropout_rate encoder = Encoder( graph=encoder_graph, epochs=100, batch_size=batch_size, learning_rate=learning_rate, learning_attenuate_rate=0.1, attenuate_epoch=50, bptt_tau=8, test_size_rate=0.3, computable_loss=MeanSquaredError(), opt_params=encoder_opt_params, verificatable_result=VerificateFunctionApproximation(), tol=tol, tld=tld ) decoder_graph = DecoderGraph() decoder_graph.observed_activating_function = LogisticFunction() decoder_graph.input_gate_activating_function = LogisticFunction() decoder_graph.forget_gate_activating_function = LogisticFunction() decoder_graph.output_gate_activating_function = LogisticFunction() decoder_graph.hidden_activating_function = LogisticFunction() decoder_graph.output_activating_function = SoftmaxFunction() decoder_graph.create_rnn_cells( input_neuron_count=hidden_neuron_count, hidden_neuron_count=hidden_neuron_count, output_neuron_count=input_neuron_count ) decoder_opt_params = DecoderAdam() decoder_opt_params.weight_limit = weight_limit decoder_opt_params.dropout_rate = dropout_rate decoder = Decoder( graph=decoder_graph, epochs=100, batch_size=batch_size, learning_rate=learning_rate, learning_attenuate_rate=0.1, attenuate_epoch=50, seq_len=seq_len, bptt_tau=bptt_tau, test_size_rate=0.3, computable_loss=MeanSquaredError(), opt_params=decoder_opt_params, verificatable_result=VerificateFunctionApproximation() ) encoder_decoder_controller = EncoderDecoderController( encoder=encoder, decoder=decoder, epochs=epochs, batch_size=batch_size, learning_rate=learning_rate, learning_attenuate_rate=learning_attenuate_rate, attenuate_epoch=attenuate_epoch, test_size_rate=test_size_rate, computable_loss=MeanSquaredError(), verificatable_result=VerificateFunctionApproximation(), tol=tol, tld=tld ) return encoder_decoder_controller
def learn(self, sentence_list, token_master_list, hidden_neuron_count=200, epochs=100, batch_size=100, learning_rate=1e-05, learning_attenuate_rate=0.1, attenuate_epoch=50, bptt_tau=8, weight_limit=0.5, dropout_rate=0.5, test_size_rate=0.3): ''' Init. Args: sentence_list: The `list` of sentences. token_master_list: Unique `list` of tokens. hidden_neuron_count: The number of units in hidden layer. epochs: Epochs of Mini-batch. bath_size: Batch size of Mini-batch. learning_rate: Learning rate. learning_attenuate_rate: Attenuate the `learning_rate` by a factor of this value every `attenuate_epoch`. attenuate_epoch: Attenuate the `learning_rate` by a factor of `learning_attenuate_rate` every `attenuate_epoch`. Additionally, in relation to regularization, this class constrains weight matrixes every `attenuate_epoch`. bptt_tau: Refereed maxinum step `t` in Backpropagation Through Time(BPTT). weight_limit: Regularization for weights matrix to repeat multiplying the weights matrix and `0.9` until $\sum_{j=0}^{n}w_{ji}^2 < weight\_limit$. dropout_rate: The probability of dropout. test_size_rate: Size of Test data set. If this value is `0`, the ''' observed_arr = self.__setup_dataset(sentence_list, token_master_list) self.__logger.debug("Shape of observed data points:") self.__logger.debug(observed_arr.shape) # Init. encoder_graph = EncoderGraph() # Activation function in LSTM. encoder_graph.observed_activating_function = LogisticFunction() encoder_graph.input_gate_activating_function = LogisticFunction() encoder_graph.forget_gate_activating_function = LogisticFunction() encoder_graph.output_gate_activating_function = LogisticFunction() encoder_graph.hidden_activating_function = LogisticFunction() encoder_graph.output_activating_function = LogisticFunction() # Initialization strategy. # This method initialize each weight matrices and biases in Gaussian distribution: `np.random.normal(size=hoge) * 0.01`. encoder_graph.create_rnn_cells( input_neuron_count=observed_arr.shape[-1], hidden_neuron_count=hidden_neuron_count, output_neuron_count=observed_arr.shape[-1]) # Init. decoder_graph = DecoderGraph() # Activation function in LSTM. decoder_graph.observed_activating_function = LogisticFunction() decoder_graph.input_gate_activating_function = LogisticFunction() decoder_graph.forget_gate_activating_function = LogisticFunction() decoder_graph.output_gate_activating_function = LogisticFunction() decoder_graph.hidden_activating_function = LogisticFunction() decoder_graph.output_activating_function = LogisticFunction() # Initialization strategy. # This method initialize each weight matrices and biases in Gaussian distribution: `np.random.normal(size=hoge) * 0.01`. decoder_graph.create_rnn_cells( input_neuron_count=hidden_neuron_count, hidden_neuron_count=observed_arr.shape[-1], output_neuron_count=hidden_neuron_count) encoder_opt_params = EncoderAdam() encoder_opt_params.weight_limit = weight_limit encoder_opt_params.dropout_rate = dropout_rate encoder = Encoder( # Delegate `graph` to `LSTMModel`. graph=encoder_graph, # The number of epochs in mini-batch training. epochs=epochs, # The batch size. batch_size=batch_size, # Learning rate. learning_rate=learning_rate, # Attenuate the `learning_rate` by a factor of this value every `attenuate_epoch`. learning_attenuate_rate=learning_attenuate_rate, # Attenuate the `learning_rate` by a factor of `learning_attenuate_rate` every `attenuate_epoch`. attenuate_epoch=attenuate_epoch, # Refereed maxinum step `t` in BPTT. If `0`, this class referes all past data in BPTT. bptt_tau=bptt_tau, # Size of Test data set. If this value is `0`, the validation will not be executed. test_size_rate=test_size_rate, # Loss function. computable_loss=MeanSquaredError(), # Optimizer. opt_params=encoder_opt_params, # Verification function. verificatable_result=VerificateFunctionApproximation(), tol=0.0) decoder_opt_params = DecoderAdam() decoder_opt_params.weight_limit = weight_limit decoder_opt_params.dropout_rate = dropout_rate decoder = Decoder( # Delegate `graph` to `LSTMModel`. graph=decoder_graph, # The number of epochs in mini-batch training. epochs=epochs, # The batch size. batch_size=batch_size, # Learning rate. learning_rate=learning_rate, # Attenuate the `learning_rate` by a factor of this value every `attenuate_epoch`. learning_attenuate_rate=learning_attenuate_rate, # Attenuate the `learning_rate` by a factor of `learning_attenuate_rate` every `attenuate_epoch`. attenuate_epoch=attenuate_epoch, # Refereed maxinum step `t` in BPTT. If `0`, this class referes all past data in BPTT. bptt_tau=bptt_tau, # Size of Test data set. If this value is `0`, the validation will not be executed. test_size_rate=test_size_rate, # Loss function. computable_loss=MeanSquaredError(), # Optimizer. opt_params=decoder_opt_params, # Verification function. verificatable_result=VerificateFunctionApproximation(), tol=0.0) encoder_decoder_controller = EncoderDecoderController( encoder=encoder, decoder=decoder, epochs=epochs, batch_size=batch_size, learning_rate=learning_rate, learning_attenuate_rate=learning_attenuate_rate, attenuate_epoch=attenuate_epoch, test_size_rate=test_size_rate, computable_loss=MeanSquaredError(), verificatable_result=VerificateFunctionApproximation(), tol=0.0) # Learning. encoder_decoder_controller.learn(observed_arr, observed_arr) self.__controller = encoder_decoder_controller self.__token_master_list = token_master_list
def __build_encoder_decoder_controller(self, input_neuron_count=20, hidden_neuron_count=20, weight_limit=0.5, dropout_rate=0.5, epochs=1000, batch_size=20, learning_rate=1e-05, attenuate_epoch=50, learning_attenuate_rate=0.1, seq_len=8, bptt_tau=8, test_size_rate=0.3, tol=1e-10, tld=100.0): # Init. encoder_graph = EncoderGraph() # Activation function in LSTM. encoder_graph.observed_activating_function = LogisticFunction() encoder_graph.input_gate_activating_function = LogisticFunction() encoder_graph.forget_gate_activating_function = LogisticFunction() encoder_graph.output_gate_activating_function = LogisticFunction() encoder_graph.hidden_activating_function = LogisticFunction() encoder_graph.output_activating_function = LogisticFunction() # Initialization strategy. # This method initialize each weight matrices and biases in Gaussian distribution: `np.random.normal(size=hoge) * 0.01`. encoder_graph.create_rnn_cells(input_neuron_count=input_neuron_count, hidden_neuron_count=hidden_neuron_count, output_neuron_count=1) encoder_opt_params = EncoderAdam() encoder_opt_params.weight_limit = weight_limit encoder_opt_params.dropout_rate = dropout_rate encoder = Encoder( # Delegate `graph` to `LSTMModel`. graph=encoder_graph, # The number of epochs in mini-batch training. epochs=100, # The batch size. batch_size=batch_size, # Learning rate. learning_rate=learning_rate, # Attenuate the `learning_rate` by a factor of this value every `attenuate_epoch`. learning_attenuate_rate=0.1, # Attenuate the `learning_rate` by a factor of `learning_attenuate_rate` every `attenuate_epoch`. attenuate_epoch=50, # Refereed maxinum step `t` in BPTT. If `0`, this class referes all past data in BPTT. bptt_tau=8, # Size of Test data set. If this value is `0`, the validation will not be executed. test_size_rate=0.3, # Loss function. computable_loss=MeanSquaredError(), # Optimizer. opt_params=encoder_opt_params, # Verification function. verificatable_result=VerificateFunctionApproximation()) # Init. decoder_graph = DecoderGraph() # Activation function in LSTM. decoder_graph.observed_activating_function = LogisticFunction() decoder_graph.input_gate_activating_function = LogisticFunction() decoder_graph.forget_gate_activating_function = LogisticFunction() decoder_graph.output_gate_activating_function = LogisticFunction() decoder_graph.hidden_activating_function = LogisticFunction() decoder_graph.output_activating_function = SoftmaxFunction() # Initialization strategy. # This method initialize each weight matrices and biases in Gaussian distribution: `np.random.normal(size=hoge) * 0.01`. decoder_graph.create_rnn_cells(input_neuron_count=hidden_neuron_count, hidden_neuron_count=hidden_neuron_count, output_neuron_count=input_neuron_count) decoder_opt_params = DecoderAdam() decoder_opt_params.weight_limit = weight_limit decoder_opt_params.dropout_rate = dropout_rate decoder = Decoder( # Delegate `graph` to `LSTMModel`. graph=decoder_graph, # The number of epochs in mini-batch training. epochs=100, # The batch size. batch_size=batch_size, # Learning rate. learning_rate=learning_rate, # Attenuate the `learning_rate` by a factor of this value every `attenuate_epoch`. learning_attenuate_rate=0.1, # Attenuate the `learning_rate` by a factor of `learning_attenuate_rate` every `attenuate_epoch`. attenuate_epoch=50, # The length of sequences. seq_len=seq_len, # Refereed maxinum step `t` in BPTT. If `0`, this class referes all past data in BPTT. bptt_tau=bptt_tau, # Size of Test data set. If this value is `0`, the validation will not be executed. test_size_rate=0.3, # Loss function. computable_loss=MeanSquaredError(), # Optimizer. opt_params=decoder_opt_params, # Verification function. verificatable_result=VerificateFunctionApproximation()) encoder_decoder_controller = EncoderDecoderController( encoder=encoder, decoder=decoder, epochs=epochs, batch_size=batch_size, learning_rate=learning_rate, learning_attenuate_rate=learning_attenuate_rate, attenuate_epoch=attenuate_epoch, test_size_rate=test_size_rate, computable_loss=MeanSquaredError(), verificatable_result=VerificateFunctionApproximation(), tol=tol, tld=tld) return encoder_decoder_controller