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'):
""" This function initialises a neural network
:param n_in: Dimensionality of input features
:param hidden_layer_size: The layer size for each hidden layer
:param n_out: Dimensionality of output features
:param hidden_layer_type: the activation types of each hidden layers, e.g., TANH, LSTM, GRU, BLSTM
:param output_type: the activation type of the output layer, by default is 'LINEAR', linear regression.
:param dropout_rate: probability of dropout, a float number between 0 and 1.
:type n_in: Integer
:type hidden_layer_size: A list of integers
:type n_out: Integrer
"""
self.n_in = int(n_in)
self.n_out = int(n_out)
self.n_layers = len(hidden_layer_size)
self.hidden_layer_size = hidden_layer_size
self.hidden_layer_type = hidden_layer_type
assert len(self.hidden_layer_size) == len(self.hidden_layer_type)
self.output_type = output_type
self.dropout_rate = dropout_rate
self.loss_function = loss_function
self.optimizer = optimizer
# create model
self.model = Sequential()
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