LSTM條件

Question

我正在用tensorflow學習CNN的LSTM。 我想將一些標量標籤放入LSTM網絡中作爲條件。 有誰知道我的意思是哪個LSTM？ 如果有，請讓我知道那個的用法

謝謝。

Answer 1

繼承人申請CNN和LSTM一個序列的輸出概率，像你這樣的例子問：

def build_model(inputs): 

    BATCH_SIZE = 4 
    NUM_CLASSES = 2 
    NUM_UNITS = 128 
    H = 224 
    W = 224 
    C = 3 
    TIME_STEPS = 4 
    # inputs is assumed to be of shape (BATCH_SIZE, TIME_STEPS, H, W, C) 
    # reshape your input such that you can apply the CNN for all images 
    input_cnn_reshaped = tf.reshape(inputs, (-1, H, W, C)) 

    # define CNN, for instance vgg 16 
    cnn_logits_output, _ = vgg_16(input_cnn_reshaped, num_classes=NUM_CLASSES) 
    cnn_probabilities_output = tf.nn.softmax(cnn_logits_output) 

    # reshape back to time series convention 
    cnn_probabilities_output = tf.reshape(cnn_probabilities_output, (BATCH_SIZE, TIME_STEPS, NUM_CLASSES)) 

    # perform LSTM over the probabilities per image 
    cell = tf.contrib.rnn.LSTMCell(NUM_UNITS) 
    _, state = tf.nn.dynamic_rnn(cell, cnn_probabilities_output) 

    # employ FC layer over the last state 
    logits = tf.layers.dense(state, NUM_UNITS) 

    # logits is of shape (BATCH_SIZE, NUM_CLASSES) 
    return logits 

順便說一句，一個更好的辦法是聘請LSTM過去隱藏層，即使用CNN作爲特徵提取器並對特徵序列進行預測。