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desire2020
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,2 +1,4 @@ | ||
| __pycache__ | ||
| save/*.txt | ||
| save/*.txt | ||
| saved_model | ||
| .idea |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| import tensorflow as tf | ||
| from tensorflow.contrib.cudnn_rnn.python.layers.cudnn_rnn import CudnnLSTM, CUDNN_RNN_BIDIRECTION | ||
| from tensorflow.contrib import layers | ||
| class Critic(object): | ||
| def __call__(self, h): | ||
| # sequence -> [b, l, v] | ||
| _, l, v = h.get_shape().as_list() | ||
| h = tf.reshape(h, [-1, l, 1, v]) | ||
| with tf.variable_scope("textmover", reuse=tf.AUTO_REUSE): | ||
| h0 = layers.convolution2d( | ||
| h, v, [4, 1], [2, 1], | ||
| activation_fn=tf.nn.softplus | ||
| ) | ||
| h1 = layers.convolution2d( | ||
| h0, v, [4, 1], [1, 1], | ||
| activation_fn=tf.nn.softplus | ||
| ) | ||
| h2 = layers.convolution2d( | ||
| h1, v, [4, 1], [2, 1], | ||
| activation_fn=tf.nn.softplus | ||
| ) | ||
| h = layers.flatten(h2) | ||
| h = layers.fully_connected( | ||
| h, 1, activation_fn=tf.identity | ||
| ) | ||
| return h | ||
|
|
||
| class Mediator(object): | ||
| def __init__(self, num_emb, batch_size, emb_dim, hidden_dim, | ||
| sequence_length, start_token, | ||
| learning_rate=1e-3, reward_gamma=0.95, name="mediator", dropout_rate=0.5, with_professor_forcing=False): | ||
| self.num_emb = num_emb | ||
| # self.batch_size = batch_size | ||
| self.emb_dim = emb_dim | ||
| self.hidden_dim = hidden_dim | ||
| self.sequence_length = sequence_length | ||
| self.learning_rate = tf.Variable(float(learning_rate), trainable=False) | ||
| self.reward_gamma = reward_gamma | ||
| self.g_params = [] | ||
| self.d_params = [] | ||
| self.temperature = 1.0 | ||
| self.name = name | ||
| self.dropout_keep_rate = tf.Variable(float(1.0), trainable=False) | ||
| self.dropout_on = self.dropout_keep_rate.assign(dropout_rate) | ||
| self.dropout_off = self.dropout_keep_rate.assign(1.0) | ||
| self.expected_reward = tf.Variable(tf.zeros([self.sequence_length])) | ||
|
|
||
| self.x0 = tf.placeholder(tf.int32, shape=[None, self.sequence_length]) | ||
| self.x = self.x0 | ||
| self.x1 = tf.placeholder(tf.int32, shape=[None, self.sequence_length]) | ||
| input_x0 = tf.pad(self.x0, [[0, 0], [1, 0]])[:, 0:self.sequence_length] | ||
| input_x1 = tf.pad(self.x1, [[0, 0], [1, 0]])[:, 0:self.sequence_length] | ||
| output_x0 = tf.one_hot( | ||
| self.x0, self.num_emb, on_value=1.0, off_value=0.0 | ||
| ) | ||
| output_x1 = tf.one_hot( | ||
| self.x1, self.num_emb, on_value=1.0, off_value=0.0 | ||
| ) | ||
| with tf.variable_scope(name, reuse=tf.AUTO_REUSE): | ||
| embedding = tf.get_variable( | ||
| name="word_embeddings", | ||
| initializer=tf.random_normal(shape=[self.num_emb, self.emb_dim], stddev=0.1) | ||
| ) | ||
| Wo = tf.get_variable( | ||
| name="Weight_output", | ||
| initializer=tf.random_normal(shape=[self.hidden_dim, self.num_emb], stddev=0.1) | ||
| ) | ||
| bo = tf.get_variable( | ||
| name="bias_output", | ||
| initializer=tf.random_normal(shape=[self.num_emb], stddev=0.1) | ||
| ) | ||
| rnn = CudnnLSTM( | ||
| num_layers=1, | ||
| num_units=self.hidden_dim, | ||
| kernel_initializer=tf.orthogonal_initializer() | ||
| ) | ||
| def language_modeling(input_x): | ||
| with tf.variable_scope("language_model", reuse=tf.AUTO_REUSE): | ||
| emb_x = tf.nn.embedding_lookup( | ||
| embedding, input_x | ||
| ) | ||
| emb_x = tf.transpose(emb_x, [1, 0, 2]) | ||
| h, _ = rnn(emb_x) | ||
| h = tf.transpose(h, [1, 0, 2]) | ||
| h = tf.nn.dropout(h, self.dropout_keep_rate) | ||
| pred = tf.nn.log_softmax( | ||
| tf.reshape(h, [-1, self.hidden_dim]) @ Wo + bo, | ||
| axis=-1) | ||
| return h, tf.reshape(pred, [-1, self.sequence_length, self.num_emb]) | ||
| self.h0, self.log_predictions = language_modeling(input_x0) | ||
| self.h1, self.log_predictions_ = language_modeling(input_x1) | ||
| self.likelihood_loss = -tf.reduce_mean( | ||
| tf.reduce_sum( | ||
| self.log_predictions * output_x0 + | ||
| self.log_predictions_ * output_x1, axis=-1) | ||
| ) / 2.0 | ||
| self.m_opt = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.95) | ||
| if with_professor_forcing: | ||
| with tf.variable_scope("professor_forcing", reuse=tf.AUTO_REUSE): | ||
| critic = Critic() | ||
| myu = tf.random_uniform(shape=[tf.shape(self.x0)[0], self.sequence_length, 1], | ||
| minval=0.0, maxval=1.0) | ||
| hybrid = self.h0 * myu + self.h1 * (1.0 - myu) | ||
| gp = tf.reduce_mean(tf.nn.relu(tf.norm( | ||
| tf.reshape(tf.gradients(critic(hybrid), [hybrid])[0], [tf.shape(self.x0)[0], -1]), | ||
| axis=-1) - 1.0) ** 2) | ||
| self.d_loss = tf.reduce_mean(critic(self.h0) - critic(self.h1)) | ||
| self.d_opt = tf.train.AdamOptimizer(1e-4, beta1=0.5, beta2=0.9) | ||
| self.d_params = [v for v in tf.trainable_variables() if "professor_forcing" in v.name] | ||
| self.d_update = self.d_opt.minimize(self.d_loss + 5.0 * gp, var_list=self.d_params) | ||
| self.m_params = [v for v in tf.trainable_variables() if name in v.name] | ||
| if not with_professor_forcing: | ||
| self.likelihood_updates = self.m_opt.minimize(self.likelihood_loss, var_list=self.m_params) | ||
| else: | ||
| self.likelihood_updates = self.m_opt.minimize(self.likelihood_loss - self.d_loss, var_list=self.m_params) | ||
|
|
||
| def get_reward(self, sess, x): | ||
| output = sess.run(self.log_predictions, feed_dict={self.x0: x}) | ||
| return output |