siemanko · April 21, 2020 11:19 · Mar 27, 2017 · Oct 4, 2016
diff --git a/my_lstm_cell.py b/my_lstm_cell.py
@@ -1,6 +1,7 @@
 class MyLSTMCell(tf.nn.rnn_cell.RNNCell):
     """Simplified Version rnn_cell.BasicLSTMCell"""
     def __init__(self, num_units):
+        super(MyLSTMCell, self).__init__()
         self._num_units = num_units
 
     def __call__(self, inputs, state, scope="LSTM"):

diff --git a/my_lstm_cell.py b/my_lstm_cell.py
@@ -0,0 +1,44 @@
+class MyLSTMCell(tf.nn.rnn_cell.RNNCell):
+    """Simplified Version rnn_cell.BasicLSTMCell"""
+    def __init__(self, num_units):
+        self._num_units = num_units
+
+    def __call__(self, inputs, state, scope="LSTM"):
+        with tf.variable_scope(scope):
+            c, h = state
+
+            gates  = layers.fully_connected(tf.concat(1, [inputs, h]),
+                                            num_outputs=4 * self._num_units,
+                                            activation_fn=None)
+            # i = input_gate, j = new_input, f = forget_gate, o = output_gate
+            i, j, f, o = tf.split(1, 4, gates)
+
+            forget_bias = 1.0
+            new_c = (c * tf.nn.sigmoid(f + forget_bias)
+                     + tf.nn.sigmoid(i) * tf.nn.tanh(j))
+            new_h =  tf.nn.tanh(new_c) * tf.nn.sigmoid(o)
+
+            return new_h, (new_c, new_h)
+
+    def zero_state(self, batch_size, dtype=tf.float32, learnable=False, scope="LSTM"):
+        if learnable:
+            c = tf.get_variable("c_init", (1, self._num_units),
+                    initializer=tf.random_normal_initializer(dtype=dtype))
+            h = tf.get_variable("h_init", (1, self._num_units),
+                    initializer=tf.random_normal_initializer(dtype=dtype))
+        else:
+            c = tf.zeros((1, self._num_units), dtype=dtype)
+            h = tf.zeros((1, self._num_units), dtype=dtype)
+        c = tf.tile(c, [batch_size, 1])
+        h = tf.tile(h, [batch_size, 1])
+        c.set_shape([None, self._num_units])
+        h.set_shape([None, self._num_units])
+        return (c, h)
+
+    @property
+    def state_size(self):
+        return (self._num_units, self._num_units)
+
+    @property
+    def output_size(self):
+        return self._num_units