Python letter_to_tensor示例

示例#1

文件： rnn.py 项目： aadhil96/one_shot_learning_research

        output = self.softmax(output)
        return output, hidden

    def init_hidden(self):
        return torch.zeros(1, self.hidden_size)


category_lines, all_categories = load_data()
n_categories = len(all_categories)

n_hidden = 128

rnn = RNN(N_LETTERS, n_hidden, n_categories)

#One step
input_tensor = letter_to_tensor('A')
hidden_tensor = rnn.init_hidden()

output, next_hidden = rnn(input_tensor, hidden_tensor)
#print(output.size())
#print(next_hidden.size())

# whole sequence/name
input_tensor = line_to_tensor('Albert')
hidden_tensor = rnn.init_hidden()

output, next_hidden = rnn(input_tensor[0], hidden_tensor)
#print(output.size())
#print(next_hidden.size())

示例#2

文件： rnn.py 项目： KonradDuraj/StoryGAN-for-Story-Visualization

        output = self.i2o(concatenated)
        output = self.softmax(output)
        return output, hidden

    def init_hidden(self):
        return torch.zeros(1, self.hidden_size)


category_lines, all_categories = load_data()
n_categories = len(all_categories)

n_hidden = 128
rnn = RNN(N_LETTERS, n_hidden, n_categories)

# One step
input_tensor = letter_to_tensor("A")
hidden_tensor = rnn.init_hidden()

output, next_hidden = rnn(input_tensor, hidden_tensor)
print(output.size())
print(next_hidden.size())

# Whole sequence name
input_tensor = line_to_tensor("Albert")
hidden_tensor = rnn.init_hidden()

output, next_hidden = rnn(input_tensor[0], hidden_tensor)
print(output.size())
print(next_hidden.size())