vis_generate.py 文件源码

def vis_generate(decoder, input_str, temperature=0.8):

    hidden = decoder.init_hidden(1)
    test_len = len(input_str)
    prime_input = torch.autograd.Variable(char_tensor(input_str[0]).unsqueeze(0))

    for p in range(len(prime_input) - 1):
        _, hidden = decoder(prime_input[:,p], hidden)
    hidden_matrix = np.copy(hidden.unsqueeze(0).data.numpy())
    hidden_matrix = hidden_matrix.reshape((1,hidden_matrix.size))
    inp = prime_input[:,-1]
    for p in range(1,test_len):
        output, hidden = decoder(inp, hidden)
        hidden_matrix = np.vstack((hidden_matrix, hidden[0,0,:].data.numpy()))
        # print hidden[0,0,:].data.numpy()
        # Sample from the network as a multinomial distribution
        # output_dist = output.data.view(-1).div(temperature).exp()
        # top_i = torch.multinomial(output_dist, 1)[0]

        # predicted_char = chars[top_i]
        inp = torch.autograd.Variable(char_tensor(input_str[p]).unsqueeze(0))
    hidden_matrix = np.delete(hidden_matrix, 0, 0)
    df = pd.DataFrame(hidden_matrix)
    df.to_csv('paran-data-df.csv')
    np.savetxt("paren-data.csv", hidden_matrix, delimiter=",")
    np.savetxt("paren-data.tsv", hidden_matrix, delimiter="\t")

# Run as standalone script