# The "pooler" converts the encoded sequence tensor of shape # [batch_size, seq_length, hidden_size] to a tensor of shape # [batch_size, hidden_size]. This is necessary for segment-level # (or segment-pair-level) classification tasks where we need a fixed # dimensional representation of the segment. with tf.variable_scope("pooler"): # We "pool" the model by simply taking the hidden state corresponding # to the first token. We assume that this has been pre-trained first_token_tensor = tf.squeeze(self.sequence_output[:, 0:1, :], axis=1) self.pooled_output = tf.layers.dense( first_token_tensor, config.hidden_size, activation=tf.tanh, kernel_initializer=create_initializer(config.initializer_range))

python3 -m venv ./venv source ./venv/bin/activate pip install --upgrade pip pip install tensorflow==1.15rc2 pip install -r ../requirements.txt

x = json.load(open("tmp/output")) In [8]: x["features"][2]["token"] Out[8]: '正しい' In [10]: x["features"][2]["layers"][0] Out[10]: {'index': -1, 'values': [-0.433769, ...]}