def __init__(self, n_in, hidden_layer_size, n_out, hidden_layer_type, output_type='linear', dropout_rate=0.0, loss_function='mse', optimizer='adam', rnn_params=None):
kerasModels.__init__(self, n_in, hidden_layer_size, n_out, hidden_layer_type, output_type, dropout_rate, loss_function, optimizer)
#### TODO: Find a good way to pass below params ####
self.merge_size = rnn_params['merge_size']
self.seq_length = rnn_params['seq_length']
self.bucket_range = rnn_params['bucket_range']
self.stateful = rnn_params['stateful']
pass;
def __init__(self, n_in, hidden_layer_size, n_out, hidden_layer_type, output_type='linear', dropout_rate=0.0, loss_function='mse', optimizer='adam'):
kerasModels.__init__(self, n_in, hidden_layer_size, n_out, hidden_layer_type, output_type, dropout_rate, loss_function, optimizer)
#### TODO: Find a good way to pass below params ####
self.merge_size = 1
self.seq_length = 200
self.bucket_range = 50
self.stateful = False
pass;
def __init__(self, n_in, hidden_layer_size, n_out, hidden_layer_type, output_type='linear', dropout_rate=0.0, loss_function='mse', optimizer='adam'):
kerasModels.__init__(self, n_in, hidden_layer_size, n_out, hidden_layer_type, output_type, dropout_rate, loss_function, optimizer)
#### TODO: Find a good way to pass below params ####
self.merge_size = 4400
self.seq_length = 200
self.bucket_range = 100
self.stateful = False
pass;
def __init__(self, model_params, rnn_params, training_params):
# Subclass Keras model, feed in model parameters
kerasModels.__init__(self, model_params)
# Rnn parameters
self.merge_size = rnn_params['merge_size']
self.seq_length = rnn_params['seq_length']
self.bucket_range = rnn_params['bucket_range']
self.stateful = rnn_params['stateful']
# Training parameters
self.batch_size = training_params['batch_size']
self.num_of_epochs = training_params['num_of_epochs']
self.shuffle_data = training_params['shuffle_data']
self.tensorboard_dir = training_params['tensorboard_dir']
self.stopping_patience = training_params['stopping_patience']
self.restore_best_weights = training_params['restore_best_weights']
def __init__(self,
n_in,
hidden_layer_size,
n_out,
hidden_layer_type,
output_type='linear',
dropout_rate=0.0,
loss_function='mse',
optimizer='adam',
rnn_params=None):
kerasModels.__init__(self, n_in, hidden_layer_size, n_out,
hidden_layer_type, output_type, dropout_rate,
loss_function, optimizer)
#### TODO: Find a good way to pass below params ####
self.merge_size = rnn_params['merge_size']
self.seq_length = rnn_params['seq_length']
self.bucket_range = rnn_params['bucket_range']
self.stateful = rnn_params['stateful']
pass