[GitHub] [singa] nudles commented on a change in pull request #626: [WIP] SINGA-505 Computational graph with memory optimization

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nudles commented on a change in pull request #626: [WIP] SINGA-505 Computational graph with memory optimization
URL: https://github.com/apache/singa/pull/626#discussion_r393496835
 
 

 ##########
 File path: examples/autograd/mlp_buffer.py
 ##########
 @@ -0,0 +1,112 @@
+#
+# 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 tensor
+from singa.tensor import Tensor
+from singa import autograd
+from singa import optimizer
+from singa import device
+import numpy as np
+
+
+if __name__ == "__main__":
+    dev = device.get_default_device()
+
+    autograd.training = True
+    np.random.seed(0)
+
+    # prepare training data in numpy array
+
+    # generate the boundary
+    f = lambda x: (5 * x + 1)
+    bd_x = np.linspace(-1.0, 1, 200)
+    bd_y = f(bd_x)
+    # generate the training data
+    x = np.random.uniform(-1, 1, 400)
+    y = f(x) + 2 * np.random.randn(len(x))
+    # convert training data to 2d space
+    label = np.asarray([5 * a + 1 > b for (a, b) in zip(x, y)])
+    data = np.array([[a, b] for (a, b) in zip(x, y)], dtype=np.float32)
+
+    def to_categorical(y, num_classes):
+        """
+        Converts a class vector (integers) to binary class matrix.
+
+        Args
+            y: class vector to be converted into a matrix
+                (integers from 0 to num_classes).
+            num_classes: total number of classes.
+
+        Return
+            A binary matrix representation of the input.
+        """
+        y = np.array(y, dtype="int")
+        n = y.shape[0]
+        categorical = np.zeros((n, num_classes))
+        categorical[np.arange(n), y] = 1
+        return categorical
+
+    label = to_categorical(label, 2).astype(np.float32)
+    print("train_data_shape:", data.shape)
+    print("train_label_shape:", label.shape)
+
+    inputs = Tensor(data=data, device=dev)
+    target = Tensor(data=label, device=dev)
+
+    w0 = Tensor(shape=(2, 3), device=dev, requires_grad=True, stores_grad=True)
+    w0.gaussian(0.0, 0.1)
+    b0 = Tensor(shape=(1, 3), device=dev, requires_grad=True, stores_grad=True)
+    b0.set_value(0.0)
+
+    w1 = Tensor(shape=(3, 2), device=dev, requires_grad=True, stores_grad=True)
+    w1.gaussian(0.0, 0.1)
+    b1 = Tensor(shape=(1, 2), device=dev, requires_grad=True, stores_grad=True)
+    b1.set_value(0.0)
+
+    print("finished init inputs")
+    print("w0:\n", tensor.to_numpy(w0))
+    print("b0:\n", tensor.to_numpy(b0))
+    print("w1:\n", tensor.to_numpy(w1))
+    print("b1:\n", tensor.to_numpy(b1))
+
+    sgd = optimizer.SGD(0.05)
+
+    # training process
+    print("start training")
+
+    # Buffer the operations
+    dev.EnableGraph(True)
+    x = autograd.matmul(inputs, w0)
+    x = autograd.add_bias(x, b0)
+    x = autograd.relu(x)
+    x = autograd.matmul(x, w1)
+    x = autograd.add_bias(x, b1)
+    # x = autograd.softmax(x)
+    loss = autograd.softmax_cross_entropy(x, target)
+    print("start backward")
+    for p, gp in autograd.backward(loss):
+        sgd.apply(0, gp, p, "")
+    dev.EnableGraph(False)
+
+    # exec the buffered ops
+    print("start executing buffered functions")
+    for i in range(1001):
+        dev.RunGraph()
 
 Review comment:
   how is the data fed into the graph?

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