Added dropout support in ConvLSTMCell. (#20089)

Implemented dropout and recurrent dropout in `ConvLSTMCell` with the same approach as in Keras 2.

Fixes #20063

keras/src/layers/rnn/conv_lstm.py

@@ -149,6 +149,7 @@ def __init__(
 
         self.dropout = min(1.0, max(0.0, dropout))
         self.recurrent_dropout = min(1.0, max(0.0, recurrent_dropout))
+        self.dropout_mask_count = 4
         self.input_spec = InputSpec(ndim=rank + 2)
         self.state_size = -1  # Custom, defined in methods
 
@@ -233,23 +234,29 @@ def call(self, inputs, states, training=False):
         h_tm1 = states[0]  # previous memory state
         c_tm1 = states[1]  # previous carry state
 
-        # dp_mask = self.get_dropout_mask(inputs)
-        # rec_dp_mask = self.get_recurrent_dropout_mask(h_tm1)
-
-        # if training and 0.0 < self.dropout < 1.0:
-        #     inputs *= dp_mask
-        # if training and 0.0 < self.recurrent_dropout < 1.0:
-        #     h_tm1 *= rec_dp_mask
-
-        inputs_i = inputs
-        inputs_f = inputs
-        inputs_c = inputs
-        inputs_o = inputs
-
-        h_tm1_i = h_tm1
-        h_tm1_f = h_tm1
-        h_tm1_c = h_tm1
-        h_tm1_o = h_tm1
+        if training and 0.0 < self.dropout < 1.0:
+            dp_mask = self.get_dropout_mask(inputs)
+            inputs_i = inputs * dp_mask[0]
+            inputs_f = inputs * dp_mask[1]
+            inputs_c = inputs * dp_mask[2]
+            inputs_o = inputs * dp_mask[3]
+        else:
+            inputs_i = inputs
+            inputs_f = inputs
+            inputs_c = inputs
+            inputs_o = inputs
+
+        if training and 0.0 < self.recurrent_dropout < 1.0:
+            rec_dp_mask = self.get_recurrent_dropout_mask(h_tm1)
+            h_tm1_i = h_tm1 * rec_dp_mask[0]
+            h_tm1_f = h_tm1 * rec_dp_mask[1]
+            h_tm1_c = h_tm1 * rec_dp_mask[2]
+            h_tm1_o = h_tm1 * rec_dp_mask[3]
+        else:
+            h_tm1_i = h_tm1
+            h_tm1_f = h_tm1
+            h_tm1_c = h_tm1
+            h_tm1_o = h_tm1
 
         (kernel_i, kernel_f, kernel_c, kernel_o) = ops.split(
             self.kernel, 4, axis=self.rank + 1

keras/src/layers/rnn/dropout_rnn_cell.py

@@ -19,23 +19,36 @@ class DropoutRNNCell:
     all incoming steps, so that the same mask is used for every step.
     """
 
+    def _create_dropout_mask(self, step_input, dropout_rate):
+        count = getattr(self, "dropout_mask_count", None)
+        ones = ops.ones_like(step_input)
+        if count is None:
+            return backend.random.dropout(
+                ones, rate=dropout_rate, seed=self.seed_generator
+            )
+        else:
+            return [
+                backend.random.dropout(
+                    ones, rate=dropout_rate, seed=self.seed_generator
+                )
+                for _ in range(count)
+            ]
+
     def get_dropout_mask(self, step_input):
         if not hasattr(self, "_dropout_mask"):
             self._dropout_mask = None
         if self._dropout_mask is None and self.dropout > 0:
-            ones = ops.ones_like(step_input)
-            self._dropout_mask = backend.random.dropout(
-                ones, rate=self.dropout, seed=self.seed_generator
+            self._dropout_mask = self._create_dropout_mask(
+                step_input, self.dropout
             )
         return self._dropout_mask
 
     def get_recurrent_dropout_mask(self, step_input):
         if not hasattr(self, "_recurrent_dropout_mask"):
             self._recurrent_dropout_mask = None
         if self._recurrent_dropout_mask is None and self.recurrent_dropout > 0:
-            ones = ops.ones_like(step_input)
-            self._recurrent_dropout_mask = backend.random.dropout(
-                ones, rate=self.recurrent_dropout, seed=self.seed_generator
+            self._recurrent_dropout_mask = self._create_dropout_mask(
+                step_input, self.recurrent_dropout
             )
         return self._recurrent_dropout_mask