utils.py
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from __future__ import print_function
import codecs
import sys
from keras.models import Model
from keras.layers import Input, Dense, Dropout, Activation, BatchNormalization
def eprint(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs)
def initialize_neural_model(number_of_features, path_to_model):
inputs = Input(shape=(number_of_features,))
output_from_1st_layer = Dense(1000, activation='relu')(inputs)
output_from_1st_layer = Dropout(0.2)(output_from_1st_layer)
output_from_1st_layer = BatchNormalization()(output_from_1st_layer)
output_from_2nd_layer = Dense(500, activation='relu')(output_from_1st_layer)
output_from_2nd_layer = Dropout(0.2)(output_from_2nd_layer)
output_from_2nd_layer = BatchNormalization()(output_from_2nd_layer)
output_from_3rd_layer = Dense(300, activation='relu')(output_from_2nd_layer)
output_from_3rd_layer = Dropout(0.2)(output_from_3rd_layer)
output_from_3rd_layer = BatchNormalization()(output_from_3rd_layer)
output = Dense(1, activation='sigmoid')(output_from_3rd_layer)
model = Model(inputs, output)
model.compile(optimizer='Adam', loss='binary_crossentropy', metrics=['accuracy'])
model.load_weights(path_to_model)
return model
def load_freq_list(freq_path):
freq_list = {}
with codecs.open(freq_path, 'r', 'utf-8') as freq_file:
lines = freq_file.readlines()
for line in lines:
line_parts = line.split()
freq = int(line_parts[0])
base = line_parts[1]
if base not in freq_list:
freq_list[base] = freq
return freq_list