def load_shit():
text_test = './../texts/melville.txt'
char_map_obj = Character_Map(text_test,'mapping.dat',overwrite=True, break_line=None)
unique_char = char_map_obj.unique_char
char_map_obj.k_map()
x, y, shared_x, shared_y = char_map_obj.gen_x_and_y(filename=None)
# print(shared_x, shared_y.get_value().shape[0])
nh = 100
nx = len(char_map_obj.unique_char)
ny = nx
trainer = RNN(nh,nx,ny)
trainer.load_param('param_6-10_17:52/param_epoch199.dat')
f = trainer.compile_gen_sentence()
for xi in x[100:150]:
y_guess = f(xi[0])
y_argmax = [np.argmax(y) for y in y_guess]
char_y = [unique_char[int(yi)] for yi in y_argmax]
print(char_y)
print(''.join(char_y))
return trainer, char_map_obj, x, y, shared_x, shared_y
def train_NN(mu, n_epoch, mini_batch): """ Train the neural net """ text_test = './../texts/melville.txt' char_map_obj = Character_Map(text_test,'mapping.dat',overwrite=True, break_line=None) char_map_obj.k_map() x, y, shared_x, shared_y = char_map_obj.gen_x_and_y(filename=None) nh = 100 nx = len(char_map_obj.unique_char) ny = nx trainer = RNNClass(nh,nx,ny) trainer.train_index((shared_x,shared_y),mu,n_epoch,mini_batch)
# """
# x = T.vector('x')
# y = self.gen_random_sentence(x)
# f = theano.function([x],y)
# return f
if __name__ == '__main__':
text_test = './../texts/melville.txt'
char_map_obj = Character_Map(text_test,'mapping.dat',overwrite=True, break_line=None)
char_map_obj.k_map()
x, y, shared_x, shared_y = char_map_obj.gen_x_and_y(filename=None)
# print(shared_x, shared_y.get_value().shape[0])
nh = 100
nx = len(char_map_obj.unique_char)
ny = nx
trainer = RNNClass(nh,nx,ny)
# jobs = []
# for i in xrange(2):
# p = multiprocessing.Process(target=trainer.train, args=((shared_x,shared_y),0.03,1000,10,))
# jobs.append(p)
# p.start()
# trainer.load_param('param_epoch95.dat')
trainer.train_index(training_data=(shared_x,shared_y),
learning_rate=0.01,
n_epochs=100,
