From a76f3d7cd7a4b979d709847a91ca46a7a42290ed Mon Sep 17 00:00:00 2001
From: liuchangshiye <liuchangshiye@gmail.com>
Date: Fri, 25 Aug 2023 23:01:11 +0800
Subject: [PATCH] add mlp model for malaria detection

---
 examples/malaria_cnn/model/mlp.py | 68 +++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)
 create mode 100644 examples/malaria_cnn/model/mlp.py

diff --git a/examples/malaria_cnn/model/mlp.py b/examples/malaria_cnn/model/mlp.py
new file mode 100644
index 000000000..502fad512
--- /dev/null
+++ b/examples/malaria_cnn/model/mlp.py
@@ -0,0 +1,68 @@
+
+from singa import layer
+from singa import model
+from singa import tensor
+from singa import opt
+from singa import device
+import argparse
+import numpy as np
+
+np_dtype = {"float16": np.float16, "float32": np.float32}
+
+singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
+
+
+class MLP(model.Model):
+
+    def __init__(self, perceptron_size=100, num_classes=10):
+        super(MLP, self).__init__()
+        self.num_classes = num_classes
+        self.dimension = 2
+
+        self.relu = layer.ReLU()
+        self.linear1 = layer.Linear(perceptron_size)
+        self.linear2 = layer.Linear(num_classes)
+        self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
+
+    def forward(self, inputs):
+        y = self.linear1(inputs)
+        y = self.relu(y)
+        y = self.linear2(y)
+        return y
+
+    def train_one_batch(self, x, y, dist_option, spars):
+        out = self.forward(x)
+        loss = self.softmax_cross_entropy(out, y)
+
+        if dist_option == 'plain':
+            self.optimizer(loss)
+        elif dist_option == 'half':
+            self.optimizer.backward_and_update_half(loss)
+        elif dist_option == 'partialUpdate':
+            self.optimizer.backward_and_partial_update(loss)
+        elif dist_option == 'sparseTopK':
+            self.optimizer.backward_and_sparse_update(loss,
+                                                      topK=True,
+                                                      spars=spars)
+        elif dist_option == 'sparseThreshold':
+            self.optimizer.backward_and_sparse_update(loss,
+                                                      topK=False,
+                                                      spars=spars)
+        return out, loss
+
+    def set_optimizer(self, optimizer):
+        self.optimizer = optimizer
+
+
+def create_model(**kwargs):
+    """Constructs a CNN model.
+
+    Returns:
+        The created CNN model.
+    """
+    model = MLP(**kwargs)
+
+    return model
+
+
+__all__ = ['MLP', 'create_model']
\ No newline at end of file