tf.nn.rnn_cell貌似要弃用了。 将所有tf.nn.rnn_cell更改为tf.contrib.rnn 一个最简单的LSTM单元。先说一下用法。 __init__( numunits, # int类型,LSTM内部的单元数,即每个time_step的输出大小 forget_bias=1.0,…
For Tensorflow 1.2 and Keras 2.0, the linetf.contrib.rnn.core_rnn_cell.BasicLSTMCellshould be replaced bytf.contrib.rnn.BasicLSTMCell.
@tf_export("nn.rnn_cell.BasicLSTMCell") classBasicLSTMCell(LayerRNNCell): """Basic LSTM recurrent network cell. The implementation is based on: http://arxiv.org/abs/1409.2329. We add forget_bias (default: 1) to the biases of the forget gate in order to reduce the scale of forgetting...
BasicRNNCell是最基本的RNN cell单元。 输入参数: num_units:RNN层神经元的个数 input_size(该参数已被弃用) activation: 内部状态之间的激活函数 reuse: Python布尔值, 描述是否重用现有作用域中的变量 tf.contrib.rnn.BasicLSTMCell BasicLSTMCell类是最基本的LSTM循环神经网络单元。 输入参数: num_units: LSTM ...
tf.contrib.rnn.BasicLSTMCell(rnn_unit)函数的解读 函数功能解读 函数代码实现 1.2. @tf_export("nn.rnn_cell.BasicLSTMCell")3. class BasicLSTMCell(LayerRNNCell):4. """Basic LSTM recurrent network cell.5.6. The implementation is based on: http://arxiv.org/abs/1409.2329.7.8. We add forget_...
@tf_export("nn.rnn_cell.BasicLSTMCell")classBasicLSTMCell(LayerRNNCell):"""Basic LSTM recurrent network cell. The implementation is based on: http://arxiv.org/abs/1409.2329. We add forget_bias (default: 1) to the biases of the forget gate in order to ...
@tf_export("nn.rnn_cell.BasicLSTMCell") class BasicLSTMCell(LayerRNNCell): """Basic LSTM recurrent network cell. The implementation is based on: http:///abs/1409.2329. We add forget_bias (default: 1) to the biases of the forget gate in order to ...
从经过cudnnlstm训练的检查点恢复时,必须使用“CudnnCompatibleLSTMCell”。 ”“” 函数代码实现 @tf_export("nn.rnn_cell.BasicLSTMCell") class BasicLSTMCell(LayerRNNCell): """Basic LSTM recurrent network cell. The implementation is based on: http://arxiv.org/abs/1409.2329. ...
首先tf.nn.dynamic_rnn()的time_major是默认的false,故输入X应该是一个[batch_size,step,input_size]=[4,2,3] 的tensor,注意我们这里调用的是BasicRNNCell,只有一层循环网络,outputs是最后一层每个step的输出,它的结构是[batch_size,step,n_neurons]=[4,2,5] ,states是每一层的最后那个step的输出,由于本...
basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=n_neurons) seq_length = tf.placeholder(tf.int32, [None]) outputs, states = tf.nn.dynamic_rnn(basic_cell, X, dtype=tf.float32, sequence_length=seq_length) init = tf.global_variables_initializer() ...