Create tf_module.py

tf_module.py

@@ -0,0 +1,42 @@
+import tensorflow as tf
+from tensorflow.contrib import rnn, layers
+
+
+def length_seq(seq):
+    idx = tf.sign(tf.reduce_max(tf.abs(seq), reduction_indices=2))
+    seq_len = tf.cast(tf.reduce_sum(idx, reduction_indices=1), tf.int32)
+    return seq_len
+
+
+def BiGRU(x, hidden_size, dropout_prob=None, n_layer=None):
+    x_len = length_seq(x)
+    if n_layer: # multilayer GRU
+        GRU_forwards = [rnn.GRUCell(hidden_size) for _ in range(n_layer)]
+        GRU_backwards = [rnn.GRUCell(hidden_size) for _ in range(n_layer)]
+        if dropout_prob:
+            GRU_forwards = [rnn.DropoutWrapper(i, input_keep_prob=dropout_prob) for i in GRU_forwards]
+            GRU_backwards = [rnn.DropoutWrapper(i, input_keep_prob=dropout_prob) for i in GRU_backwards]
+        out, _, _ = rnn.stack_bidirectional_dynamic_rnn(cells_fw=GRU_forwards, cells_bw=GRU_backwards, inputs=x, sequence_length=x_len, dtype=tf.float32)
+    else:
+        GRU_forward = rnn.GRUCell(hidden_size)
+        GRU_backword = rnn.GRUCell(hidden_size)
+        if dropout_prob:
+            GRU_forward = rnn.DropoutWrapper(GRU_forward, input_keep_prob=dropout_prob)
+            GRU_backword = rnn.DropoutWrapper(GRU_backword, input_keep_prob=dropout_prob)
+        (forward_out, backward_out), _ = tf.nn.bidirectional_dynamic_rnn(cell_fw=GRU_forward, cell_bw=GRU_backword, inputs=x, sequence_length=x_len, dtype=tf.float32)
+        out = tf.concat((forward_out, backward_out), axis=2)
+    return out
+
+
+def Attention_(x, hidden_size):
+    contexts = tf.Variable(tf.truncated_normal(shape=[hidden_size]))
+    h = layers.fully_connected(x, hidden_size, activation_fn=tf.nn.tanh)
+    alpha = tf.nn.softmax(tf.reduce_sum(tf.multiply(h, contexts), axis=2, keepdims=True), axis=1)
+    out = tf.reduce_sum(tf.multiply(x, alpha), axis=1)
+    return out
+
+
+def fc_(x, hidden_size):
+    return layers.fully_connected(inputs=x,
+                                  num_outputs=hidden_size,
+                                  activation_fn=None)