def __init__(self, params=None):
if params == None:
print "Please provide input params"
else:
self._params = params
self.model_options = self._params
Preprocess.__init__(self, self.model_options)
def __init__(self, Object=None, params=None):
default_params = {
"num_hidden_layers":2,
"dim_proj":128,
"patience":10,
"max_epochs":5000,
"dispFreq":10,
"decay_c":0.,
"lrate":0.0001,
"n_words":10000,
"optimizer":"adadelta",
"encoder":"lstm",
"saveto":"lstm_model.npz",
"validFreq":370,
"saveFreq":1110,
"maxlen":100,
"batch_size":16,
"valid_batch_size":64,
"valid_portion":0.05,
"dataset":"imdb",
"noise_std":0.,
"use_dropout":True,
"reload_model":"",
"text_col":0,
"dedupe":True,
"label_col":5,
"train_max":0.5,
"train_size":1524,
"test_size":1533,
"data_directory":"../../../data/",
"data_file":"Annotated_Comments_for_Always_Discreet_1.csv",
#"data_file":"Annotated_Comments_for_Crest White Strips 1.csv",
"raw_rows":None,
"class_type":"Sentiment",
"correct_spelling":False,
"unk_threshold":0.8
}
# self._del_keys = ['_layers','f_grad_shared','f_grad']
self._del_keys = ['_layers','f_grad_shared','f_grad','train_set','test_set','_test_xx','_trXX','_teXX']
if params!=None:
#print params
for key,value in params.iteritems():
try:
default_params[key] = value
except:
print "Could not add: " + str(key) +" --> "+ str(value)
self._params = default_params
self._model_options = self._params # Originally in Load_LSTM_Params
### THIS OBJECT THING NEEDS TO BE SIMPLIFIED NOW THAT EVERYTHING IS INHERITED.
if Object==None:
# self._params = params
Preprocess.__init__(self, self._model_options)
# Load_LSTM_Params.__init__(self, self._params)
self._layers = {'lstm': (self.param_init_lstm, self.lstm_layer)}
elif Object!=None: # Copy the LSTM Object
# Params & Data variables
self._params = copy.deepcopy(Object._params)
self.train_set = copy.deepcopy(Object.train_set)
self.valid_set = copy.deepcopy(Object.valid_set)
self.test_set = copy.deepcopy(Object.test_set)
self._DICTIONARY = copy.deepcopy(Object._DICTIONARY)
try:
print "Assuming object is LSTM, copying..."
# LSTM variables
self._layers = copy.deepcopy(Object._layers)
self._model_options = copy.deepcopy(Object._model_options)
self._params = copy.deepcopy(Object._params)
self._tparams = copy.deepcopy(Object._tparams) # I don't know if this will work, do Theano variables need to be recompiled?
self._model_options = copy.deepcopy(Object._model_options)
self.optimizer = self._model_options['optimizer']
except:
print "Couldn't copy LSTM Object, initializing a new object."
self._layers = {'lstm': (self.param_init_lstm, self.lstm_layer)}
self._model_options = Object._model_options
self._params = self._init_params(self._model_options)
self._tparams = self._init_tparams(self._params)
self.optimizer = self._model_options['optimizer']
