Merge pull request #956 from wannature/singa_v14
add cnn implementations for large dataset example
diff --git a/examples/largedataset_cnn/model/cnn.py b/examples/largedataset_cnn/model/cnn.py
new file mode 100644
index 0000000..3877e83
--- /dev/null
+++ b/examples/largedataset_cnn/model/cnn.py
@@ -0,0 +1,90 @@
+#
+# 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
+
+
+class CNN(model.Model):
+
+ def __init__(self, num_classes=10, num_channels=1):
+ super(CNN, self).__init__()
+ self.num_classes = num_classes
+ self.input_size = 28
+ self.dimension = 4
+ self.conv1 = layer.Conv2d(num_channels, 20, 5, padding=0, activation="RELU")
+ self.conv2 = layer.Conv2d(20, 50, 5, padding=0, activation="RELU")
+ self.linear1 = layer.Linear(500)
+ self.linear2 = layer.Linear(num_classes)
+ self.pooling1 = layer.MaxPool2d(2, 2, padding=0)
+ self.pooling2 = layer.MaxPool2d(2, 2, padding=0)
+ self.relu = layer.ReLU()
+ self.flatten = layer.Flatten()
+ self.softmax_cross_entropy = layer.SoftMaxCrossEntropy()
+
+ def forward(self, x):
+ y = self.conv1(x)
+ y = self.pooling1(y)
+ y = self.conv2(y)
+ y = self.pooling2(y)
+ y = self.flatten(y)
+ y = self.linear1(y)
+ 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(pretrained=False, **kwargs):
+ """Constructs a CNN model.
+
+ Args:
+ pretrained (bool): If True, returns a pre-trained model.
+
+ Returns:
+ The created CNN model.
+ """
+ model = CNN(**kwargs)
+
+ return model
+
+
+__all__ = ['CNN', 'create_model']
