我正在使用带有TensorFlow后端的Keras。我刚刚弄清楚了如何在没有掩码的情况下训练和分类不同长度的序列，因为我无法使掩码工作。在我正在处理的玩具示例中，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 Sequentialfrom keras.layers import LSTM, Denseimport numpy as npdef 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_valN_train = 100N_test = 10recall_len = 20X_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('___________________________________')

如代码所示，我的错误是在每个时间步上批处理的。这有很多不利之处。我如何在两个步骤中训练网络？例如：

1. 通过网络运行一组值以累积错误
2. 根据累积的错误调整网络的权重

回答：

要实现原始问题中描述的内容，最简单的方法是使用掩码训练原始网络，然后用状态网络进行测试，这样就可以对任意长度的输入进行分类：

import numpy as npnp.random.seed(1)import tensorflow as tftf.set_random_seed(1)from keras import modelsfrom keras.layers import Dense, Masking, LSTMimport matplotlib.pyplot as pltdef 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 modeldef 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 modeldef 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, yif __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()