Merge pull request #1089 from zmeihui/23-9-7-ms-model

Create the ms_model_mlp folder for dynamic model creation

examples/model_selection_psql/ms_model_mlp/model.py

@@ -0,0 +1,98 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+#
+
+from singa import layer
+from singa import model
+from singa import tensor
+from singa import opt
+from singa import device
+from singa.autograd import Operator
+from singa.layer import Layer
+from singa import singa_wrap as singa
+import argparse
+import numpy as np
+
+np_dtype = {"float16": np.float16, "float32": np.float32}
+
+singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
+
+#### self-defined loss begin
+
+### from autograd.py
+class SumError(Operator):
+
+    def __init__(self):
+        super(SumError, self).__init__()
+        # self.t = t.data
+
+    def forward(self, x):
+        # self.err = singa.__sub__(x, self.t)
+        self.data_x = x
+        # sqr = singa.Square(self.err)
+        # loss = singa.SumAll(sqr)
+        loss = singa.SumAll(x)
+        # self.n = 1
+        # for s in x.shape():
+        #     self.n *= s
+        # loss /= self.n
+        return loss
+
+    def backward(self, dy=1.0):
+        # dx = self.err
+        dev = device.get_default_device()
+        dx = tensor.Tensor(self.data_x.shape, dev, singa_dtype['float32'])
+        dx.copy_from_numpy(np.ones(self.data_x.shape))
+        # dx *= float(2 / self.n)
+        dx *= dy
+        return dx
+
+def se_loss(x):
+    # assert x.shape == t.shape, "input and target shape different: %s, %s" % (
+    #     x.shape, t.shape)
+    return SumError()(x)[0]
+
+### from layer.py
+class SumErrorLayer(Layer):
+    """
+    Generate a MeanSquareError operator
+    """
+
+    def __init__(self):
+        super(SumErrorLayer, self).__init__()
+
+    def forward(self, x):
+        return se_loss(x)
+
+#### self-defined loss end
+
+class MSMLP(model.Model):
+
+    def __init__(self, data_size=10, perceptron_size=100, num_classes=10, layer_hidden_list=[10,10,10,10]):
+        super(MSMLP, self).__init__()
+        self.num_classes = num_classes
+        self.dimension = 2
+
+        self.relu = layer.ReLU()
+        self.linear1 = layer.Linear(layer_hidden_list[0])
+        self.linear2 = layer.Linear(layer_hidden_list[1])
+        self.linear3 = layer.Linear(layer_hidden_list[2])
+        self.linear4 = layer.Linear(layer_hidden_list[3])
+        self.linear5 = layer.Linear(num_classes)
+        self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
+        self.sum_error = SumErrorLayer()