Loading the Model
embeddings = None
with open("./text/output.json", "r", encoding="utf-8") as file:
embeddings = json.loads(file.read())
map = {}
for item in embeddings:
map[tuple(item['embedding'])] = item
network = nn.Sequential(
nn.Linear(20, 20),
nn.Tanh(),
nn.Linear(20,len(embeddings)),
nn.Sigmoid()
)
# 2. Load the saved weights into the structure
network.load_state_dict(torch.load("./text/model.txt"))
Generating Text
word = random.sample(embeddings, 1)[0]
print(word)
def next(list):
float_list = list.data.tolist()
indices = range(len(float_list))
random_index = random.choices(indices, weights=float_list, k=1)[0]
return embeddings[random_index]
for i in range(10):
output = network(torch.tensor(word['embedding']))
word = next(output)
print(word['token'])
pass