Machine learning 基于错误批处理的不同长度序列分类

我在TensorFlow后端使用Keras。我刚刚弄明白了如何在没有掩蔽的情况下训练和分类不同长度的序列，因为我无法让掩蔽起作用。在我正在使用的玩具示例中，我试图训练一个LSTM来检测任意长度的序列是否以

1

开头

from keras.models import Sequential
from keras.layers import LSTM, Dense
import numpy as np


def gen_sig(num_samples, seq_len):
    one_indices = np.random.choice(a=num_samples, size=num_samples // 2, replace=False)

    x_val = np.zeros((num_samples, seq_len), dtype=np.bool)
    x_val[one_indices, 0] = 1

    y_val = np.zeros(num_samples, dtype=np.bool)
    y_val[one_indices] = 1

    return x_val, y_val


N_train = 100
N_test = 10
recall_len = 20

X_train, y_train = gen_sig(N_train, recall_len)

X_test, y_test = gen_sig(N_train, recall_len)

print('Build STATEFUL model...')
model = Sequential()
model.add(LSTM(10, batch_input_shape=(1, 1, 1), return_sequences=False, stateful=True))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

print('Train...')
for epoch in range(15):
    mean_tr_acc = []
    mean_tr_loss = []

    for seq_idx in range(X_train.shape[0]):
        start_val = X_train[seq_idx, 0]
        assert y_train[seq_idx] == start_val
        assert tuple(np.nonzero(X_train[seq_idx, :]))[0].shape[0] == start_val

        y_in = np.array([y_train[seq_idx]], dtype=np.bool)

        for j in range(np.random.choice(a=np.arange(5, recall_len+1))):
            x_in = np.array([[[X_train[seq_idx][j]]]])
            tr_loss, tr_acc = model.train_on_batch(x_in, y_in)

            mean_tr_acc.append(tr_acc)
            mean_tr_loss.append(tr_loss)

            model.reset_states()

    print('accuracy training = {}'.format(np.mean(mean_tr_acc)))
    print('loss training = {}'.format(np.mean(mean_tr_loss)))
    print('___________________________________')

    mean_te_acc = []
    mean_te_loss = []
    for seq_idx in range(X_test.shape[0]):
        start_val = X_test[seq_idx, 0]
        assert y_test[seq_idx] == start_val
        assert tuple(np.nonzero(X_test[seq_idx, :]))[0].shape[0] == start_val

        y_in = np.array([y_test[seq_idx]], dtype=np.bool)

        for j in range(np.random.choice(a=np.arange(5, recall_len+1))):
            te_loss, te_acc = model.test_on_batch(np.array([[[X_test[seq_idx][j]]]], dtype=np.bool), y_in)
            mean_te_acc.append(te_acc)
            mean_te_loss.append(te_loss)
        model.reset_states()

    print('accuracy testing = {}'.format(np.mean(mean_te_acc)))
    print('loss testing = {}'.format(np.mean(mean_te_loss)))
    print('___________________________________')

如代码中所示，我的错误在每个时间步都被批处理。这是不好的，原因有很多。如何分两步培训网络？例如：

通过网络运行一组值以累积错误

根据此累积误差调整网络的权重

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要完成原始问题中描述的操作，最简单的方法是使用掩蔽对原始网络进行训练，然后使用有状态网络进行测试，以便对任何长度输入进行分类：

import numpy as np
np.random.seed(1)

import tensorflow as tf
tf.set_random_seed(1)

from keras import models
from keras.layers import Dense, Masking, LSTM

import matplotlib.pyplot as plt


def stateful_model():
    hidden_units = 256

    model = models.Sequential()
    model.add(LSTM(hidden_units, batch_input_shape=(1, 1, 1), return_sequences=False, stateful=True))
    model.add(Dense(1, activation='relu', name='output'))

    model.compile(loss='binary_crossentropy', optimizer='rmsprop')

    return model


def train_rnn(x_train, y_train, max_len, mask):
    epochs = 10
    batch_size = 200

    vec_dims = 1
    hidden_units = 256
    in_shape = (max_len, vec_dims)

    model = models.Sequential()

    model.add(Masking(mask, name="in_layer", input_shape=in_shape,))
    model.add(LSTM(hidden_units, return_sequences=False))
    model.add(Dense(1, activation='relu', name='output'))

    model.compile(loss='binary_crossentropy', optimizer='rmsprop')

    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs,
              validation_split=0.05)

    return model


def gen_train_sig_cls_pair(t_stops, num_examples, mask):
    x = []
    y = []
    max_t = int(np.max(t_stops))

    for t_stop in t_stops:
        one_indices = np.random.choice(a=num_examples, size=num_examples // 2, replace=False)

        sig = np.zeros((num_examples, max_t), dtype=np.int8)
        sig[one_indices, 0] = 1
        sig[:, t_stop:] = mask
        x.append(sig)

        cls = np.zeros(num_examples, dtype=np.bool)
        cls[one_indices] = 1
        y.append(cls)

    return np.concatenate(x, axis=0), np.concatenate(y, axis=0)


def gen_test_sig_cls_pair(t_stops, num_examples):
    x = []
    y = []

    for t_stop in t_stops:
        one_indices = np.random.choice(a=num_examples, size=num_examples // 2, replace=False)

        sig = np.zeros((num_examples, t_stop), dtype=np.bool)
        sig[one_indices, 0] = 1
        x.extend(list(sig))

        cls = np.zeros((num_examples, t_stop), dtype=np.bool)
        cls[one_indices] = 1
        y.extend(list(cls))

    return x, y


if __name__ == '__main__':
    noise_mag = 0.01
    mask_val = -10
    signal_lengths = (10, 15, 20)

    x_in, y_in = gen_train_sig_cls_pair(signal_lengths, 10, mask_val)

    mod = train_rnn(x_in[:, :, None], y_in, int(np.max(signal_lengths)), mask_val)

    testing_dat, expected = gen_test_sig_cls_pair(signal_lengths, 3)

    state_mod = stateful_model()
    state_mod.set_weights(mod.get_weights())

    res = []
    for s_i in range(len(testing_dat)):
        seq_in = list(testing_dat[s_i])
        seq_len = len(seq_in)

        for t_i in range(seq_len):
            res.extend(state_mod.predict(np.array([[[seq_in[t_i]]]])))

        state_mod.reset_states()

    fig, axes = plt.subplots(2)
    axes[0].plot(np.concatenate(testing_dat), label="input")

    axes[1].plot(res, "ro", label="result", alpha=0.2)
    axes[1].plot(np.concatenate(expected, axis=0), "bo", label="expected", alpha=0.2)
    axes[1].legend(bbox_to_anchor=(1.1, 1))

    plt.show()