Add test

python/mxnet/gluon/block.py

@@ -775,7 +775,8 @@ def _get_graph(self, *args):
             grouped_inputs = _regroup(inputs, self._in_format)[0]
 
             params = {i: j.var() for i, j in self._reg_params.items()}
-            out = self.hybrid_forward(symbol, *grouped_inputs, **params)  # pylint: disable=no-value-for-parameter
+            with self.name_scope():
+                out = self.hybrid_forward(symbol, *grouped_inputs, **params)  # pylint: disable=no-value-for-parameter
             out, self._out_format = _flatten(out, "output")
 
             self._cached_graph = inputs, symbol.Group(out, _check_same_symbol_type(out))
@@ -959,7 +960,8 @@ def forward(self, x, *args):
             "HybridBlock requires the first argument to forward be either " \
             "Symbol or NDArray, but got %s"%type(x)
         params = {i: j.var() for i, j in self._reg_params.items()}
-        return self.hybrid_forward(symbol, x, *args, **params)
+        with self.name_scope():
+            return self.hybrid_forward(symbol, x, *args, **params)
 
     def hybrid_forward(self, F, x, *args, **kwargs):
         """Overrides to construct symbolic graph for this `Block`.

python/mxnet/numpy/multiarray.py

@@ -782,7 +782,9 @@ def copy(self, order='C'):  # pylint: disable=arguments-differ
         return super(ndarray, self).copy().as_np_ndarray()
 
     def dot(self, b, out=None):
-        raise NotImplementedError
+        """Dot product of two arrays.
+        Refer to ``numpy.dot`` for full documentation."""
+        return _mx_np_op.dot(self, b, out=out)
 
     def reshape(self, *args, **kwargs):  # pylint: disable=arguments-differ
         """Returns an array containing the same data with a new shape.

python/mxnet/symbol/numpy/_symbol.py

@@ -218,7 +218,9 @@ def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ
         raise NotImplementedError
 
     def dot(self, b, out=None):
-        raise NotImplementedError
+        """Dot product of two arrays.
+        Refer to ``numpy.dot`` for full documentation."""
+        return _mx_np_op.dot(self, b, out=out)
 
     def reshape(self, *args, **kwargs):  # pylint: disable=arguments-differ
         """Returns an array containing the same data with a new shape.

tests/python/unittest/test_numpy_gluon.py

@@ -0,0 +1,113 @@
+# 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.
+
+# pylint: skip-file
+from __future__ import absolute_import
+from __future__ import division
+
+import mxnet as mx
+from mxnet import gluon, autograd, np
+from mxnet.test_utils import use_np
+
+
+def test_create_np_param():
+    M, K, N = 10, 9, 20
+
+    def check_block_params(x, TestBlock, hybridize, expected_type):
+        net = TestBlock()
+        net.initialize()
+        if hybridize:
+            net.hybridize()
+        net(x)
+        params = net.collect_params()
+        for k, v in params.items():
+            assert type(v.data()) is expected_type
+
+    class TestBlock1(gluon.HybridBlock):
+        def __init__(self):
+            super(TestBlock1, self).__init__()
+            with self.name_scope():
+                self.w = self.params.get('w', shape=(K, N), allow_deferred_init=True)
+
+        def hybrid_forward(self, F, x, w):
+            return F.dot(x, w)
+
+    @use_np
+    class TestBlock2(gluon.HybridBlock):
+        def __init__(self):
+            super(TestBlock2, self).__init__()
+            with self.name_scope():
+                self.w = self.params.get('w', shape=(K, N), allow_deferred_init=True)
+
+        def hybrid_forward(self, F, x, w):
+            return F.np.dot(x, w)
+
+    x = mx.nd.random.uniform(shape=(M, K))
+    check_block_params(x, TestBlock1, False, mx.nd.NDArray)
+    check_block_params(x, TestBlock1, True, mx.nd.NDArray)
+    check_block_params(x.as_np_ndarray(), TestBlock2, False, np.ndarray)
+    check_block_params(x.as_np_ndarray(), TestBlock2, True, np.ndarray)
+
+
+@use_np
+def test_optimizer_with_np_ndarrays():
+    class LinearRegression(gluon.HybridBlock):
+        def __init__(self, num_input_dim=0, num_hidden_dim=100, num_output_dim=10):
+            super(LinearRegression, self).__init__()
+            with self.name_scope():
+                self.w1 = self.params.get('w1', shape=(num_input_dim, num_hidden_dim),
+                                          allow_deferred_init=True)
+                self.w2 = self.params.get('w2', shape=(num_hidden_dim, num_output_dim),
+                                          allow_deferred_init=True)
+
+        def hybrid_forward(self, F, x, w1, w2):
+            h = x.dot(w1)  # equivalent to F.np.dot(x, w1)
+            h_relu = F.npx.relu(h)  # equivalent to F.relu(h) but generating np.ndarray
+            y_pred = h_relu.dot(w2)  # equivalent to F.np.dot(h_relu, w2)
+            return y_pred
+
+    class TotalLoss(gluon.HybridBlock):
+        def hybrid_forward(self, F, pred, label):
+            return ((pred - label) ** 2).sum()  # equivalent to F.np.sum(F.np.square(pred - label))
+
+    regressor = LinearRegression()
+    regressor.initialize(mx.init.Uniform())
+    regressor.hybridize()
+
+    # Create random input and output data
+    x = np.random.uniform(size=(64, 1000))  # x is of type mxnet.numpy.ndarray
+    regressor(x)
+    y = np.random.uniform(size=(64, 10))  # y is of type mxnet.numpy.ndarray
+
+    total_loss = TotalLoss()
+    total_loss.hybridize()
+
+    trainer = gluon.Trainer(regressor.collect_params(),
+                            'sgd',
+                            {'learning_rate': 1e-3, 'momentum': 0.9})
+
+    for t in range(2):
+        with autograd.record():
+            output = regressor(x)  # output is a type of np.ndarray because np.dot is the last op in the network
+            loss = total_loss(output, y)  # loss is a scalar np.ndarray
+        loss.backward()
+        trainer.step(1)
+
+
+if __name__ == '__main__':
+    import nose
+    nose.runmodule()