cnn+rnn+attention

来源：互联网发布：网络支付有哪些编辑：程序博客网时间：2024/05/29 02:21
下面是单层rnn+attention的代码，若考虑多层rnn请参考博主的：tf.contrib.rnn.static_rnn与tf.nn.dynamic_rnn区别
def attention(inputs, attention_size, time_major=False):    if isinstance(inputs, tuple):        # In case of Bi-RNN, concatenate the forward and the backward RNN outputs.        inputs = tf.concat(inputs, 2)    if time_major:        # (T,B,D) => (B,T,D)        inputs = tf.transpose(inputs, [1, 0, 2])    inputs_shape = inputs.shape    sequence_length = inputs_shape[1].value  # the length of sequences processed in the antecedent RNN layer    hidden_size = inputs_shape[2].value  # hidden size of the RNN layer    # Attention mechanism    W_omega = tf.Variable(tf.random_normal([hidden_size, attention_size], stddev=0.1))    b_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))    u_omega = tf.Variable(tf.random_normal([attention_size], stddev=0.1))    v = tf.tanh(tf.matmul(tf.reshape(inputs, [-1, hidden_size]), W_omega) + tf.reshape(b_omega, [1, -1]))    vu = tf.matmul(v, tf.reshape(u_omega, [-1, 1]))    exps = tf.reshape(tf.exp(vu), [-1, sequence_length])    alphas = exps / tf.reshape(tf.reduce_sum(exps, 1), [-1, 1])    # Output of Bi-RNN is reduced with attention vector    output = tf.reduce_sum(inputs * tf.reshape(alphas, [-1, sequence_length, 1]), 1)    return output# cnn的输出为B*4*8*128chunk_size = 128  chunk_n = 32  rnn_size = 256  attention_size = 50n_output_layer = MAX_CAPTCHA*CHAR_SET_LEN   # 输出层  # 定义待训练的神经网络  def recurrent_neural_network():     data = crack_captcha_cnn()           data = tf.reshape(data, [-1, chunk_n, chunk_size])      data = tf.transpose(data, [1,0,2])      data = tf.reshape(data, [-1, chunk_size])      data = tf.split(data,chunk_n)        # 只用RNN    #layer = {'w_':tf.Variable(tf.random_normal([rnn_size, n_output_layer])), 'b_':tf.Variable(tf.random_normal([n_output_layer]))}     #lstm_cell = tf.contrib.rnn.BasicLSTMCell(rnn_size)     #outputs, status = tf.contrib.rnn.static_rnn(lstm_cell, data, dtype=tf.float32)      #ouput = tf.add(tf.matmul(outputs[-1], layer['w_']), layer['b_'])          # RNN + Attention        lstm_cell = tf.contrib.rnn.BasicLSTMCell(rnn_size)     outputs, status = tf.contrib.rnn.static_rnn(lstm_cell, data, dtype=tf.float32)         attention_output = attention(outputs, attention_size, True)        # output    drop = tf.nn.dropout(attention_output, keep_prob)    # Fully connected layer    W = tf.Variable(tf.truncated_normal([rnn_size, n_output_layer], stddev=0.1), name="W")    b = tf.Variable(tf.constant(0., shape=[n_output_layer]), name="b")        ouput = tf.nn.xw_plus_b(drop, W, b, name="scores")    return ouput
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