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Updates #56

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Updates #56

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12 changes: 6 additions & 6 deletions models/dual_encoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@ def dual_encoder_model(
# Build the RNN
with tf.variable_scope("rnn") as vs:
# We use an LSTM Cell
cell = tf.nn.rnn_cell.LSTMCell(
cell = tf.contrib.rnn.LSTMCell(
hparams.rnn_dim,
forget_bias=2.0,
use_peepholes=True,
Expand All @@ -51,10 +51,10 @@ def dual_encoder_model(
# Run the utterance and context through the RNN
rnn_outputs, rnn_states = tf.nn.dynamic_rnn(
cell,
tf.concat(0, [context_embedded, utterance_embedded]),
sequence_length=tf.concat(0, [context_len, utterance_len]),
tf.concat( [context_embedded, utterance_embedded],0),
sequence_length=tf.concat([context_len, utterance_len],0),
dtype=tf.float32)
encoding_context, encoding_utterance = tf.split(0, 2, rnn_states.h)
encoding_context, encoding_utterance = tf.split(rnn_states.h, 2, 0)

with tf.variable_scope("prediction") as vs:
M = tf.get_variable("M",
Expand All @@ -68,7 +68,7 @@ def dual_encoder_model(

# Dot product between generated response and actual response
# (c * M) * r
logits = tf.batch_matmul(generated_response, encoding_utterance, True)
logits = tf.matmul(generated_response, encoding_utterance, True)
logits = tf.squeeze(logits, [2])

# Apply sigmoid to convert logits to probabilities
Expand All @@ -78,7 +78,7 @@ def dual_encoder_model(
return probs, None

# Calculate the binary cross-entropy loss
losses = tf.nn.sigmoid_cross_entropy_with_logits(logits, tf.to_float(targets))
losses = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=tf.to_float(targets))

# Mean loss across the batch of examples
mean_loss = tf.reduce_mean(losses, name="mean_loss")
Expand Down