def build_model(self, metadata_path=None, embedding_weights=None):
self.embedding_weights, self.config = ops.embedding_layer(
metadata_path, embedding_weights)
self.embedded_text = tf.nn.embedding_lookup(self.embedding_weights,
self.input)
self.embedded_sentences, _ = ops.embed_sentences(
self.sentences, self.embedding_weights)
with tf.name_scope("CNN_LSTM"):
self.cnn_out = ops.multi_filter_conv_block(
self.embedded_text,
self.args["n_filters"],
dropout_keep_prob=self.args["dropout"])
self.lstm_out = ops.lstm_block(
self.cnn_out,
self.args["hidden_units"],
dropout=self.args["dropout"],
layers=self.args["rnn_layers"],
dynamic=False,
bidirectional=self.args["bidirectional"])
self.concat_features_sent_words = tf.concat(
[self.lstm_out, self.embedded_sentences], axis=-1)
self.out = tf.squeeze(
fully_connected(self.concat_features_sent_words,
1,
activation='sigmoid'))
with tf.name_scope("loss"):
self.loss = losses.mean_squared_error(self.sentiment, self.out)
if self.args["l2_reg_beta"] > 0.0:
self.regularizer = ops.get_regularizer(
self.args["l2_reg_beta"])
self.loss = tf.reduce_mean(self.loss + self.regularizer)
#### Evaluation Measures.
with tf.name_scope("Pearson_correlation"):
self.pco, self.pco_update = tf.contrib.metrics.streaming_pearson_correlation(
self.out, self.sentiment, name="pearson")
with tf.name_scope("MSE"):
self.mse, self.mse_update = tf.metrics.mean_squared_error(
self.sentiment, self.out, name="mse")
def build_model(self, metadata_path=None, embedding_weights=None):
self.embedding_weights, self.config = ops.embedding_layer(
metadata_path, embedding_weights)
self.embedded = tf.nn.embedding_lookup(self.embedding_weights,
self.input)
self.lstm_out = ops.lstm_block(
self.embedded,
self.args["hidden_units"],
dropout=self.args["dropout"],
layers=self.args["rnn_layers"],
dynamic=False,
bidirectional=self.args["bidirectional"])
self.dense1 = fully_connected(self.lstm_out, 128)
dropped_out = dropout(self.dense1, keep_prob=0.8)
self.dense2 = fully_connected(dropped_out, 128)
dropped_out = dropout(self.dense2, keep_prob=0.8)
self.out = tf.squeeze(fully_connected(dropped_out, 1))
with tf.name_scope("loss"):
#self.loss = self.cost()
self.loss = losses.mean_squared_error(self.input_sim, self.out)
if self.args["l2_reg_beta"] > 0.0:
self.regularizer = ops.get_regularizer(
self.args["l2_reg_beta"])
self.loss = tf.reduce_mean(self.loss + self.regularizer)
# Compute some Evaluation Measures to keep track of the training process
with tf.name_scope("Pearson_correlation"):
self.pco, self.pco_update = tf.contrib.metrics.streaming_pearson_correlation(
self.out, self.input_sim, name="pearson")
# Compute some Evaluation Measures to keep track of the training process
with tf.name_scope("MSE"):
self.mse, self.mse_update = tf.metrics.mean_squared_error(
self.input_sim, self.out, name="mse")
def build_model(self, metadata_path=None, embedding_weights=None):
with tf.name_scope("embedding"):
self.embedding_weights, self.config = ops.embedding_layer(
metadata_path, embedding_weights)
self.embedded_text = tf.nn.embedding_lookup(
self.embedding_weights, self.sentence)
with tf.name_scope("CNN_LSTM"):
self.cnn_out = ops.multi_filter_conv_block(
self.embedded_text,
self.args["n_filters"],
dropout_keep_prob=self.args["dropout"])
self.lstm_out = ops.lstm_block(
self.cnn_out,
self.args["hidden_units"],
dropout=self.args["dropout"],
layers=self.args["rnn_layers"],
dynamic=False,
bidirectional=self.args["bidirectional"])
self.out = fully_connected(self.lstm_out, 5)
with tf.name_scope("loss"):
self.loss = losses.categorical_cross_entropy(
self.sentiment, self.out)
if self.args["l2_reg_beta"] > 0.0:
self.regularizer = ops.get_regularizer(
self.args["l2_reg_beta"])
self.loss = tf.reduce_mean(self.loss + self.regularizer)
#### Evaluation Measures.
with tf.name_scope("Graph_Accuracy"):
self.correct_preds = tf.equal(tf.argmax(self.out, 1),
tf.argmax(self.sentiment, 1))
self.accuracy = tf.reduce_mean(tf.cast(self.correct_preds,
tf.float32),
name="accuracy")
def build_model(self, metadata_path=None, embedding_weights=None):
"""
This method builds the computation graph by adding layers of
computations. It takes the metadata_path (of the dataset vocabulary)
and a preloaded word2vec matrix and input and uses them (if not None)
to initialize the Tensorflow variables. The metadata is used to
visualize the word embeddings that are being trained using Tensorflow
Projector. Additionally you can use any other tool to visualize them.
https://www.tensorflow.org/versions/r0.12/how_tos/embedding_viz/
:param metadata_path: Path to the metadata of the vocabulary. Refer
to the datasets API
https://github.com/mindgarage/Ovation/wiki/The-Datasets-API
:param embedding_weights: the preloaded w2v matrix that corresponds
to the vocabulary. Refer to https://github.com/mindgarage/Ovation/wiki/The-Datasets-API#what-does-a-dataset-object-have
:return:
"""
# Build the Embedding layer as the first layer of the model
self.embedding_weights, self.config = ops.embedding_layer(
metadata_path, embedding_weights)
self.embedded_s1 = tf.nn.embedding_lookup(self.embedding_weights,
self.input_s1)
self.embedded_s2 = tf.nn.embedding_lookup(self.embedding_weights,
self.input_s2)
self.s1_cnn_out = ops.multi_filter_conv_block(
self.embedded_s1,
self.args["n_filters"],
dropout_keep_prob=self.args["dropout"])
self.s1_lstm_out = ops.lstm_block(
self.s1_cnn_out,
self.args["hidden_units"],
dropout=self.args["dropout"],
layers=self.args["rnn_layers"],
dynamic=False,
bidirectional=self.args["bidirectional"])
## second Siamese arch part
self.s2_cnn_out = ops.multi_filter_conv_block(
self.embedded_s2,
self.args["n_filters"],
reuse=True,
dropout_keep_prob=self.args["dropout"])
self.s2_lstm_out = ops.lstm_block(
self.s2_cnn_out,
self.args["hidden_units"],
dropout=self.args["dropout"],
layers=self.args["rnn_layers"],
dynamic=False,
reuse=True,
bidirectional=self.args["bidirectional"])
self.distance = distances.exponential(self.s1_lstm_out,
self.s2_lstm_out)
# input sim : GT , distance: m
with tf.name_scope("loss"):
self.loss = losses.mean_squared_error(self.input_sim,
self.distance)
if self.args["l2_reg_beta"] > 0.0:
self.regularizer = ops.get_regularizer(
self.args["l2_reg_beta"])
self.loss = tf.reduce_mean(self.loss + self.regularizer)
# Compute some Evaluation Measures to keep track of the training process
with tf.name_scope("Pearson_correlation"):
self.pco, self.pco_update = tf.contrib.metrics.streaming_pearson_correlation(
self.distance, self.input_sim, name="pearson")
# Compute some Evaluation Measures to keep track of the training process
with tf.name_scope("MSE"):
self.mse, self.mse_update = tf.metrics.mean_squared_error(
self.input_sim, self.distance, name="mse")
def build_model(self, metadata_path=None, embedding_weights=None):
self.embedding_weights, self.config = ops.embedding_layer(
metadata_path, embedding_weights)
self.embedded = tf.nn.embedding_lookup(self.embedding_weights,
self.input)
self.embedded = tf.concat((self.embedded, self.augmented), axis=2)
self.facts = ops.lstm_block(self.embedded,
self.args["hidden_units"],
dropout=self.args["dropout"],
layers=self.args["rnn_layers"],
dynamic=False,
return_seq=True,
return_state=False,
bidirectional=self.args["bidirectional"])
self.facts = tf.transpose(self.facts, perm=[1, 0, 2])
self.attention_weights = tf.get_variable(
"W",
shape=[self.args['batch_size'], 2 * self.args['hidden_units']])
# self.attention_weights = tf.parallel_stack([self.attention_weights] *
# self.args['batch_size'])
self.attentions = []
self.sentiment = self.attention_weights
self.sentiment_memories = [self.sentiment]
# memory module
with tf.variable_scope(
"memory", initializer=tf.contrib.layers.xavier_initializer()):
print('==> build episodic memory')
# generate n_hops episodes
prev_memory = self.sentiment
for i in range(self.args['num_hops']):
# get a new episode
print('==> generating episode', i)
episode, attn = ops.generate_episode(
prev_memory, self.sentiment, self.facts, i,
2 * self.args['hidden_units'], self.input_length,
self.args['embedding_dim'])
self.attentions.append(attn)
# untied weights for memory update
with tf.variable_scope("hop_%d" % i):
prev_memory = tf.layers.dense(
tf.concat([prev_memory, episode, self.sentiment], 1),
2 * self.args['hidden_units'],
activation=tf.nn.relu)
self.sentiment_memories.append(prev_memory)
self.output = prev_memory
self.output = tf.squeeze(
self.get_sentiment_score(self.output, self.sentiment))
with tf.name_scope("loss"):
self.loss = losses.mean_squared_error(self.input_sim, self.output)
if self.args["l2_reg_beta"] > 0.0:
self.regularizer = ops.get_regularizer(
self.args["l2_reg_beta"])
self.loss = tf.reduce_mean(self.loss + self.regularizer)
# Compute some Evaluation Measures to keep track of the training process
with tf.name_scope("Pearson_correlation"):
self.pco, self.pco_update = tf.contrib.metrics.streaming_pearson_correlation(
self.output, self.input_sim, name="pearson")
# Compute some Evaluation Measures to keep track of the training process
with tf.name_scope("MSE"):
self.mse, self.mse_update = tf.metrics.mean_squared_error(
self.input_sim, self.output, name="mse")
def build_model(self, metadata_path=None, embedding_weights=None):
# Transforms the `embedding_weights` data (that are just numpy variables) into a
# tf.Variable() object (or, if `embedding_weights` is None, just creates a new
# randomly tf.Variable()
self.embedding_weights, self.config = ops.embedding_layer(
metadata_path, embedding_weights)
# Transforms the `self.input` from a list of numbers into a list of word vectors
# Output is it Batch x Time x Word_Vector
self.embedded_input = tf.nn.embedding_lookup(self.embedding_weights,
self.input)
# Generate a random fixed vector
self.fixed_vec = tf.get_variable("fixed_vec", [128], trainable=False)
self.fixed_vec = tf.parallel_stack([self.fixed_vec] *
self.args.get("sequence_length"))
new_fixed_vec = self.fixed_vec
for i in range(1):
# Concatenate the fixed vector with each word vector
input_and_fixed = concatenate_matrices(new_fixed_vec,
self.embedded_input, 64)
# Apply a softmax in each sequence (i.e., in each element of the batch)
self.softmaxed_sequences = []
self.rescaled_sequences = []
for j, item in enumerate(input_and_fixed):
sequence = tf.stack(input_and_fixed[j])
# The Dense layer expects Batch x Input. I am fooling it into believing that
# it got a batch, and it will process each word separately, which is what I
# want.
fc_out = tf.layers.dense(sequence, 1)
softmaxed_seq = tf.nn.softmax(fc_out)
self.softmaxed_sequences.append(softmaxed_seq)
rescaled_seq = tf.multiply(sequence, softmaxed_seq)
self.rescaled_sequences.append(rescaled_seq)
self.rescaled_sequences = tf.stack(self.rescaled_sequences)
# For now, just hardcoding values here
self.lstm_out = ops.lstm_block(self.rescaled_sequences,
hidden_units=128,
dropout=0.5,
layers=1,
dynamic=False,
bidirectional=True)
self.loop_dense = tf.layers.dense(self.lstm_out,
128,
activation=tf.nn.sigmoid)
new_fixed_vec = self.loop_dense
self.final_dense = tf.layers.dense(self.loop_dense,
1,
activation=tf.nn.sigmoid)
self.out = tf.squeeze(self.final_dense, 1)
with tf.name_scope("loss"):
self.loss = losses.mean_squared_error(self.expected_output,
self.out)
if self.args["l2_reg_beta"] > 0.0:
self.regularizer = ops.get_regularizer(
self.args["l2_reg_beta"])
self.loss = tf.reduce_mean(self.loss + self.regularizer)
#### Evaluation Measures.
with tf.name_scope("Pearson_correlation"):
self.pco, self.pco_update = tf.contrib.metrics.streaming_pearson_correlation(
self.out, self.expected_output, name="pearson")
with tf.name_scope("MSE"):
self.mse, self.mse_update = tf.metrics.mean_squared_error(
self.expected_output, self.out, name="mse")