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models/mlp.py

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+# Copyright 2023 DeepMind Technologies Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Implementation of a Multi-Layer Perceptron."""
+
+import copy
+from torch import nn
+import torch.nn.functional as F
+
+
+def clones(module, n):
+  return nn.ModuleList([copy.deepcopy(module) for _ in range(n)])
+
+
+class MLP(nn.Module):
+  """MLP class."""
+
+  def __init__(self, in_features, out_features, num_hidden, hidden_dim) -> None:
+    super().__init__()
+
+    self.layer0 = nn.Linear(in_features, hidden_dim)
+    self.layers = clones(nn.Linear(hidden_dim, hidden_dim), num_hidden)
+    self.out = nn.Linear(hidden_dim, out_features)
+
+  def forward(self, x):
+    x = F.relu(self.layer0(x))
+
+    for l in self.layers:
+      x = F.relu(l(x))
+
+    return self.out(x)