def __init__(self, layers: [base_layer.AbstractLayer]):
"""
:parameter layers: a list of tuple for init layers
"""
self.layers = layers
self.weighted_layers = self.get_weighted_layers()
self.rng = np.random.RandomState(1234)
self.depth = len(self.weighted_layers)
# set pickle file path
self.file_name = os.path.splitext(os.path.basename(sys.argv[0]))[0]
self.pickle_file = conf.DATA_PATH + self.file_name + '.pkl'
self.connect()
# self.reset_params()
self.init_weights_baises()
self.weights, self.biases = self._get_weights_biases()
# l1 and l2 regularization
self.l1, self.l2 = self.init_regularization()
# init my_theano functions
self.x = T.matrix('x')
self.y = T.ivector('y')
self.index = T.iscalar('index')
self.train_model = None
self.valid_model = None
self.test_model = None
# set early stopping patience
self.patience = 20
self.patience_inc_coef = -0.1
self.lest_valid_error = np.inf
def __init__(self, layers, ceptron):
"""
:parameter layers: a list of layers, by the type of base_layer
"""
assert type(layers) == list
self.layers = layers
self.weights = self.init_weights()
self.biases = self.init_biases()
self.ceptron = ceptron
self.depth = len(layers) - 1
# init my_theano functions
self.x = T.matrix('x')
self.y = T.ivector('y')
self.index = T.iscalar('index')
self.p_y = self.forward(self.x)
self.train_model = None
# self.set_weights_biases = my_theano.function(self._make_updates(self.weights, self.biases))
# set early stopping patience
self.patience = 5
self.patience_inc_coef = -0.1
self.lest_valid_error = np.inf
# set pickle file path
self.file_name = os.path.splitext(os.path.basename(sys.argv[0]))[0]
self.pickle_file = conf.DATA_PATH + self.file_name + '.pkl'
def __init__(self, layers: [base_layer.AbstractLayer]):
"""
:parameter layers: a list of tuple for init layers
"""
self.layers = layers
self.rng = np.random.RandomState(1234)
self.depth = len(self.layers) - 1
self.weights = self.init_weights()
self.biases = self.init_biases()
# l1 and l2 regularization
self.l1, self.l2 = self.init_regularization()
# init my_theano functions
self.x = T.matrix('x')
self.y = T.ivector('y')
self.index = T.iscalar('index')
self.p_y = self.forward(self.x)
self.train_model = None
self.valid_model = None
self.test_model = None
# self.set_weights_biases = my_theano.function(self._make_updates(self.weights, self.biases))
# set early stopping patience
self.patience = 20
self.patience_inc_coef = -0.1
self.lest_valid_error = np.inf
# set pickle file path
self.file_name = os.path.splitext(os.path.basename(sys.argv[0]))[0]
self.pickle_file = conf.DATA_PATH + self.file_name + '.pkl'
