AttributeError: module 'tensorflow' has no attribute 'log'

[neginjv]

Hi, I used ZhuSuan library to build bayesian lstm cell. I used the code that was in paper of you:ZhuSuan: A Library for Bayesian Deep python. But I got an error:

AttributeError: module 'tensorflow' has no attribute 'log' Could someone help me to solve this problem? class BayesianLSTMCell(object): def init(self, num_units, forget_bias=1.0): self._forget_bias = forget_bias w_mean = tf.zeros([2 * num_units + 1, 4 * num_units]) self._w = zs.Normal('w', w_mean, std=1., group_ndims=2) def call(self, state, inputs): c, h = state batch_size = tf.shape(inputs)[0] linear_in = tf.concat([inputs, h, tf.ones([batch_size, 1])], axis=1) linear_out = tf.matmul(linear_in, self._w) # i = input_gate, j = new_input, f = forget_gate, o = output_gate i, j, f, o = tf.split(value=linear_out, num_or_size_splits=4, axis=1) new_c = (c * tf.sigmoid(f + self._forget_bias) + tf.sigmoid(i) * tf.tanh(j)) new_h = tf.tanh(new_c) * tf.sigmoid(o) return new_c, new_h def bayesian_rnn(cell, inputs, seq_len): batch_size = tf.shape(inputs)[0] initializer = (tf.zeros([batch_size, 128]), tf.zeros([batch_size, 128])) c_list, h_list = tf.scan(cell, inputs, initializer=initializer) relevant_outputs = tf.gather_nd( h_list, tf.stack([seq_len - 1, tf.range(batch_size)], axis=1)) logits = tf.squeeze(tf.layers.dense(relevant_outputs, 1), -1) return logits seq_len=5 with zs.BayesianNet() as model: cell = BayesianLSTMCell(128, forget_bias=0.) logits = bayesian_rnn(cell, b, seq_len) _ = zs.Bernoulli(Y, logits, dtype=tf.float32