[incubator-mxnet] 17/42: Fix (#15188)
This is an automated email from the ASF dual-hosted git repository.
haoj pushed a commit to branch numpy
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
commit ab38acbdc38b12a05061411e7b1b8d7fbb913ea1
Author: reminisce
AuthorDate: Sun Jun 9 08:56:16 2019 -0700
Fix (#15188)
---
example/numpy/numpy_semantics.ipynb| 308 +
python/mxnet/gluon/data/dataloader.py | 10 +-
python/mxnet/gluon/data/vision/datasets.py | 5 +-
python/mxnet/numpy/multiarray.py | 19 +-
python/mxnet/numpy_extension/__init__.py | 7 +-
python/mxnet/util.py | 47 +++--
6 files changed, 369 insertions(+), 27 deletions(-)
diff --git a/example/numpy/numpy_semantics.ipynb
b/example/numpy/numpy_semantics.ipynb
new file mode 100644
index 000..1cec51f
--- /dev/null
+++ b/example/numpy/numpy_semantics.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+"# How to Use NumPy Semantics in MXNet with `mxnet.numpy` Module\n",
+"\n",
+"## NumPy Shape Semantics\n",
+"\n",
+"### Example \n",
+"\n",
+"| Shape Example | MXNet (before) | MXNet/NumPy |\n",
+"|:---:|:---:|:---:|\n",
+"| `()` | unknown | Scalar tensor |\n",
+"| `(2, 0, 1)` | Second dimension unknown | Zero-size tensor |\n",
+"| `None`(Python) | N/A | Unknown |\n",
+"| `(2, -1, 0)`(C++) | N/A | Second dim uknown|\n",
+"\n",
+"### Affected modules\n",
+"- Shape inference: imperative, symbolic, Gluon\n",
+"- Legacy operators (not recommended to use)\n",
+"- MXNet/NumPy operators\n",
+"\n",
+"## NumPy Array Semantics\n",
+"**Definition:** The type of created ndarrays is
`mxnet.numpy.ndarray`/`mxnet.symbol.numpy._Symbol`, instead of
`mxnet.ndarray.NDArray`/`mxnet.symbol.Symbol` (only affects Gluon modules).\n",
+"- Block/HybridBlock\n",
+"- Parameter creation and initialization.\n",
+"- Inputs/outputs (symbol/ndarray) of
`__call__`/`forward`/`hybrid_forward`.\n",
+"- Computational graph construction.\n",
+"- Dataloader\n",
+"\n",
+"## Dependency of Two Types of Semantics\n",
+"- It is required to keep NumPy shape semantics active while activating
NumPy array semantics.\n",
+"- Deactivating NumPy shape semantics while NumPy array semantics is still
active is not allowed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"import logging\n",
+"import mxnet as mx\n",
+"from mxnet import np, npx, gluon\n",
+"\n",
+"logging.basicConfig(level=logging.INFO)\n",
+"\n",
+"try:\n",
+"npx.set_np(shape=False, array=True)\n",
+"except ValueError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+"## How to Enable NumPy Shape semantics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"a = mx.nd.random.uniform(shape=())\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"b = mx.nd.random.uniform(shape=(2, 0, 1))\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"c = np.random.uniform()\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"d = np.random.uniform(size=(2, 0, 1))\n",
+"except mx.MXNetError as e:\n",
+"print(e) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"npx.set_np(shape=True, array=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"a = mx.nd.random.uniform(shape=())\n",
+"b = mx.nd.random.uniform(shape=(2, 0, 1))\n",
+"c = np.random.uniform()\n",
+"d = np.random.uniform(size=(2, 0, 1))\n",
+"\n",
+"print('type(a) =', type(a))\n",
+"print('a.shape = ', a.shape)\n",
+"print('a.size = ', a.size)\n",
+"\n",
+"print('type(b) =', type(b))\n",
+"print('b.shape = ', b.shape)\n",
+"print('b.size = ', b.size)\n",
+"\n",
+"print('type(c) =', type(c))\n",
+"print('c.shape = ', c.shape)\n",
+"print('c.size = ', c.size)\n",
+"\n",
+"print('type(d) =', type(d))\n",
+"print('d.shape = ', d.shape)\n",
+"print('d.size = ', d.size)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+
[incubator-mxnet] 17/42: Fix (#15188)
This is an automated email from the ASF dual-hosted git repository.
haoj pushed a commit to branch numpy
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
commit ddc8b58e1d85a1297e18b8f9d1c55bbbc6aad467
Author: reminisce
AuthorDate: Sun Jun 9 08:56:16 2019 -0700
Fix (#15188)
---
example/numpy/numpy_semantics.ipynb| 308 +
python/mxnet/gluon/data/dataloader.py | 10 +-
python/mxnet/gluon/data/vision/datasets.py | 5 +-
python/mxnet/numpy/multiarray.py | 19 +-
python/mxnet/numpy_extension/__init__.py | 7 +-
python/mxnet/util.py | 47 +++--
6 files changed, 369 insertions(+), 27 deletions(-)
diff --git a/example/numpy/numpy_semantics.ipynb
b/example/numpy/numpy_semantics.ipynb
new file mode 100644
index 000..1cec51f
--- /dev/null
+++ b/example/numpy/numpy_semantics.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+"# How to Use NumPy Semantics in MXNet with `mxnet.numpy` Module\n",
+"\n",
+"## NumPy Shape Semantics\n",
+"\n",
+"### Example \n",
+"\n",
+"| Shape Example | MXNet (before) | MXNet/NumPy |\n",
+"|:---:|:---:|:---:|\n",
+"| `()` | unknown | Scalar tensor |\n",
+"| `(2, 0, 1)` | Second dimension unknown | Zero-size tensor |\n",
+"| `None`(Python) | N/A | Unknown |\n",
+"| `(2, -1, 0)`(C++) | N/A | Second dim uknown|\n",
+"\n",
+"### Affected modules\n",
+"- Shape inference: imperative, symbolic, Gluon\n",
+"- Legacy operators (not recommended to use)\n",
+"- MXNet/NumPy operators\n",
+"\n",
+"## NumPy Array Semantics\n",
+"**Definition:** The type of created ndarrays is
`mxnet.numpy.ndarray`/`mxnet.symbol.numpy._Symbol`, instead of
`mxnet.ndarray.NDArray`/`mxnet.symbol.Symbol` (only affects Gluon modules).\n",
+"- Block/HybridBlock\n",
+"- Parameter creation and initialization.\n",
+"- Inputs/outputs (symbol/ndarray) of
`__call__`/`forward`/`hybrid_forward`.\n",
+"- Computational graph construction.\n",
+"- Dataloader\n",
+"\n",
+"## Dependency of Two Types of Semantics\n",
+"- It is required to keep NumPy shape semantics active while activating
NumPy array semantics.\n",
+"- Deactivating NumPy shape semantics while NumPy array semantics is still
active is not allowed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"import logging\n",
+"import mxnet as mx\n",
+"from mxnet import np, npx, gluon\n",
+"\n",
+"logging.basicConfig(level=logging.INFO)\n",
+"\n",
+"try:\n",
+"npx.set_np(shape=False, array=True)\n",
+"except ValueError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+"## How to Enable NumPy Shape semantics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"a = mx.nd.random.uniform(shape=())\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"b = mx.nd.random.uniform(shape=(2, 0, 1))\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"c = np.random.uniform()\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"d = np.random.uniform(size=(2, 0, 1))\n",
+"except mx.MXNetError as e:\n",
+"print(e) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"npx.set_np(shape=True, array=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"a = mx.nd.random.uniform(shape=())\n",
+"b = mx.nd.random.uniform(shape=(2, 0, 1))\n",
+"c = np.random.uniform()\n",
+"d = np.random.uniform(size=(2, 0, 1))\n",
+"\n",
+"print('type(a) =', type(a))\n",
+"print('a.shape = ', a.shape)\n",
+"print('a.size = ', a.size)\n",
+"\n",
+"print('type(b) =', type(b))\n",
+"print('b.shape = ', b.shape)\n",
+"print('b.size = ', b.size)\n",
+"\n",
+"print('type(c) =', type(c))\n",
+"print('c.shape = ', c.shape)\n",
+"print('c.size = ', c.size)\n",
+"\n",
+"print('type(d) =', type(d))\n",
+"print('d.shape = ', d.shape)\n",
+"print('d.size = ', d.size)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+
[incubator-mxnet] 17/42: Fix (#15188)
This is an automated email from the ASF dual-hosted git repository.
haoj pushed a commit to branch numpy
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
commit 49feb63c1b21d9665ae1d53264b4ae0d6f869958
Author: reminisce
AuthorDate: Sun Jun 9 08:56:16 2019 -0700
Fix (#15188)
---
example/numpy/numpy_semantics.ipynb| 308 +
python/mxnet/gluon/data/dataloader.py | 10 +-
python/mxnet/gluon/data/vision/datasets.py | 5 +-
python/mxnet/numpy/multiarray.py | 19 +-
python/mxnet/numpy_extension/__init__.py | 7 +-
python/mxnet/util.py | 47 +++--
6 files changed, 369 insertions(+), 27 deletions(-)
diff --git a/example/numpy/numpy_semantics.ipynb
b/example/numpy/numpy_semantics.ipynb
new file mode 100644
index 000..1cec51f
--- /dev/null
+++ b/example/numpy/numpy_semantics.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+"# How to Use NumPy Semantics in MXNet with `mxnet.numpy` Module\n",
+"\n",
+"## NumPy Shape Semantics\n",
+"\n",
+"### Example \n",
+"\n",
+"| Shape Example | MXNet (before) | MXNet/NumPy |\n",
+"|:---:|:---:|:---:|\n",
+"| `()` | unknown | Scalar tensor |\n",
+"| `(2, 0, 1)` | Second dimension unknown | Zero-size tensor |\n",
+"| `None`(Python) | N/A | Unknown |\n",
+"| `(2, -1, 0)`(C++) | N/A | Second dim uknown|\n",
+"\n",
+"### Affected modules\n",
+"- Shape inference: imperative, symbolic, Gluon\n",
+"- Legacy operators (not recommended to use)\n",
+"- MXNet/NumPy operators\n",
+"\n",
+"## NumPy Array Semantics\n",
+"**Definition:** The type of created ndarrays is
`mxnet.numpy.ndarray`/`mxnet.symbol.numpy._Symbol`, instead of
`mxnet.ndarray.NDArray`/`mxnet.symbol.Symbol` (only affects Gluon modules).\n",
+"- Block/HybridBlock\n",
+"- Parameter creation and initialization.\n",
+"- Inputs/outputs (symbol/ndarray) of
`__call__`/`forward`/`hybrid_forward`.\n",
+"- Computational graph construction.\n",
+"- Dataloader\n",
+"\n",
+"## Dependency of Two Types of Semantics\n",
+"- It is required to keep NumPy shape semantics active while activating
NumPy array semantics.\n",
+"- Deactivating NumPy shape semantics while NumPy array semantics is still
active is not allowed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"import logging\n",
+"import mxnet as mx\n",
+"from mxnet import np, npx, gluon\n",
+"\n",
+"logging.basicConfig(level=logging.INFO)\n",
+"\n",
+"try:\n",
+"npx.set_np(shape=False, array=True)\n",
+"except ValueError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+"## How to Enable NumPy Shape semantics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"a = mx.nd.random.uniform(shape=())\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"b = mx.nd.random.uniform(shape=(2, 0, 1))\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"c = np.random.uniform()\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"d = np.random.uniform(size=(2, 0, 1))\n",
+"except mx.MXNetError as e:\n",
+"print(e) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"npx.set_np(shape=True, array=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"a = mx.nd.random.uniform(shape=())\n",
+"b = mx.nd.random.uniform(shape=(2, 0, 1))\n",
+"c = np.random.uniform()\n",
+"d = np.random.uniform(size=(2, 0, 1))\n",
+"\n",
+"print('type(a) =', type(a))\n",
+"print('a.shape = ', a.shape)\n",
+"print('a.size = ', a.size)\n",
+"\n",
+"print('type(b) =', type(b))\n",
+"print('b.shape = ', b.shape)\n",
+"print('b.size = ', b.size)\n",
+"\n",
+"print('type(c) =', type(c))\n",
+"print('c.shape = ', c.shape)\n",
+"print('c.size = ', c.size)\n",
+"\n",
+"print('type(d) =', type(d))\n",
+"print('d.shape = ', d.shape)\n",
+"print('d.size = ', d.size)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+
[incubator-mxnet] 17/42: Fix (#15188)
This is an automated email from the ASF dual-hosted git repository.
haoj pushed a commit to branch numpy
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
commit 64abcf4dd13b39200e9f4af28d5e39a5f24c4b13
Author: reminisce
AuthorDate: Sun Jun 9 08:56:16 2019 -0700
Fix (#15188)
---
example/numpy/numpy_semantics.ipynb| 308 +
python/mxnet/gluon/data/dataloader.py | 10 +-
python/mxnet/gluon/data/vision/datasets.py | 5 +-
python/mxnet/numpy/multiarray.py | 19 +-
python/mxnet/numpy_extension/__init__.py | 7 +-
python/mxnet/util.py | 47 +++--
6 files changed, 369 insertions(+), 27 deletions(-)
diff --git a/example/numpy/numpy_semantics.ipynb
b/example/numpy/numpy_semantics.ipynb
new file mode 100644
index 000..1cec51f
--- /dev/null
+++ b/example/numpy/numpy_semantics.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+"# How to Use NumPy Semantics in MXNet with `mxnet.numpy` Module\n",
+"\n",
+"## NumPy Shape Semantics\n",
+"\n",
+"### Example \n",
+"\n",
+"| Shape Example | MXNet (before) | MXNet/NumPy |\n",
+"|:---:|:---:|:---:|\n",
+"| `()` | unknown | Scalar tensor |\n",
+"| `(2, 0, 1)` | Second dimension unknown | Zero-size tensor |\n",
+"| `None`(Python) | N/A | Unknown |\n",
+"| `(2, -1, 0)`(C++) | N/A | Second dim uknown|\n",
+"\n",
+"### Affected modules\n",
+"- Shape inference: imperative, symbolic, Gluon\n",
+"- Legacy operators (not recommended to use)\n",
+"- MXNet/NumPy operators\n",
+"\n",
+"## NumPy Array Semantics\n",
+"**Definition:** The type of created ndarrays is
`mxnet.numpy.ndarray`/`mxnet.symbol.numpy._Symbol`, instead of
`mxnet.ndarray.NDArray`/`mxnet.symbol.Symbol` (only affects Gluon modules).\n",
+"- Block/HybridBlock\n",
+"- Parameter creation and initialization.\n",
+"- Inputs/outputs (symbol/ndarray) of
`__call__`/`forward`/`hybrid_forward`.\n",
+"- Computational graph construction.\n",
+"- Dataloader\n",
+"\n",
+"## Dependency of Two Types of Semantics\n",
+"- It is required to keep NumPy shape semantics active while activating
NumPy array semantics.\n",
+"- Deactivating NumPy shape semantics while NumPy array semantics is still
active is not allowed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"import logging\n",
+"import mxnet as mx\n",
+"from mxnet import np, npx, gluon\n",
+"\n",
+"logging.basicConfig(level=logging.INFO)\n",
+"\n",
+"try:\n",
+"npx.set_np(shape=False, array=True)\n",
+"except ValueError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+"## How to Enable NumPy Shape semantics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"a = mx.nd.random.uniform(shape=())\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"b = mx.nd.random.uniform(shape=(2, 0, 1))\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"c = np.random.uniform()\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"d = np.random.uniform(size=(2, 0, 1))\n",
+"except mx.MXNetError as e:\n",
+"print(e) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"npx.set_np(shape=True, array=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"a = mx.nd.random.uniform(shape=())\n",
+"b = mx.nd.random.uniform(shape=(2, 0, 1))\n",
+"c = np.random.uniform()\n",
+"d = np.random.uniform(size=(2, 0, 1))\n",
+"\n",
+"print('type(a) =', type(a))\n",
+"print('a.shape = ', a.shape)\n",
+"print('a.size = ', a.size)\n",
+"\n",
+"print('type(b) =', type(b))\n",
+"print('b.shape = ', b.shape)\n",
+"print('b.size = ', b.size)\n",
+"\n",
+"print('type(c) =', type(c))\n",
+"print('c.shape = ', c.shape)\n",
+"print('c.size = ', c.size)\n",
+"\n",
+"print('type(d) =', type(d))\n",
+"print('d.shape = ', d.shape)\n",
+"print('d.size = ', d.size)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+
[incubator-mxnet] 17/42: Fix (#15188)
This is an automated email from the ASF dual-hosted git repository.
haoj pushed a commit to branch numpy
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
commit 7bcddcf2c2e9a93a5b0058400fb6e7cc39476408
Author: reminisce
AuthorDate: Sun Jun 9 08:56:16 2019 -0700
Fix (#15188)
---
example/numpy/numpy_semantics.ipynb| 308 +
python/mxnet/gluon/data/dataloader.py | 10 +-
python/mxnet/gluon/data/vision/datasets.py | 5 +-
python/mxnet/numpy/multiarray.py | 19 +-
python/mxnet/numpy_extension/__init__.py | 7 +-
python/mxnet/util.py | 47 +++--
6 files changed, 369 insertions(+), 27 deletions(-)
diff --git a/example/numpy/numpy_semantics.ipynb
b/example/numpy/numpy_semantics.ipynb
new file mode 100644
index 000..1cec51f
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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+"# How to Use NumPy Semantics in MXNet with `mxnet.numpy` Module\n",
+"\n",
+"## NumPy Shape Semantics\n",
+"\n",
+"### Example \n",
+"\n",
+"| Shape Example | MXNet (before) | MXNet/NumPy |\n",
+"|:---:|:---:|:---:|\n",
+"| `()` | unknown | Scalar tensor |\n",
+"| `(2, 0, 1)` | Second dimension unknown | Zero-size tensor |\n",
+"| `None`(Python) | N/A | Unknown |\n",
+"| `(2, -1, 0)`(C++) | N/A | Second dim uknown|\n",
+"\n",
+"### Affected modules\n",
+"- Shape inference: imperative, symbolic, Gluon\n",
+"- Legacy operators (not recommended to use)\n",
+"- MXNet/NumPy operators\n",
+"\n",
+"## NumPy Array Semantics\n",
+"**Definition:** The type of created ndarrays is
`mxnet.numpy.ndarray`/`mxnet.symbol.numpy._Symbol`, instead of
`mxnet.ndarray.NDArray`/`mxnet.symbol.Symbol` (only affects Gluon modules).\n",
+"- Block/HybridBlock\n",
+"- Parameter creation and initialization.\n",
+"- Inputs/outputs (symbol/ndarray) of
`__call__`/`forward`/`hybrid_forward`.\n",
+"- Computational graph construction.\n",
+"- Dataloader\n",
+"\n",
+"## Dependency of Two Types of Semantics\n",
+"- It is required to keep NumPy shape semantics active while activating
NumPy array semantics.\n",
+"- Deactivating NumPy shape semantics while NumPy array semantics is still
active is not allowed."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"import logging\n",
+"import mxnet as mx\n",
+"from mxnet import np, npx, gluon\n",
+"\n",
+"logging.basicConfig(level=logging.INFO)\n",
+"\n",
+"try:\n",
+"npx.set_np(shape=False, array=True)\n",
+"except ValueError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+"## How to Enable NumPy Shape semantics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"a = mx.nd.random.uniform(shape=())\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"b = mx.nd.random.uniform(shape=(2, 0, 1))\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"c = np.random.uniform()\n",
+"except mx.MXNetError as e:\n",
+"print(e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"try:\n",
+"d = np.random.uniform(size=(2, 0, 1))\n",
+"except mx.MXNetError as e:\n",
+"print(e) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"npx.set_np(shape=True, array=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+"a = mx.nd.random.uniform(shape=())\n",
+"b = mx.nd.random.uniform(shape=(2, 0, 1))\n",
+"c = np.random.uniform()\n",
+"d = np.random.uniform(size=(2, 0, 1))\n",
+"\n",
+"print('type(a) =', type(a))\n",
+"print('a.shape = ', a.shape)\n",
+"print('a.size = ', a.size)\n",
+"\n",
+"print('type(b) =', type(b))\n",
+"print('b.shape = ', b.shape)\n",
+"print('b.size = ', b.size)\n",
+"\n",
+"print('type(c) =', type(c))\n",
+"print('c.shape = ', c.shape)\n",
+"print('c.size = ', c.size)\n",
+"\n",
+"print('type(d) =', type(d))\n",
+"print('d.shape = ', d.shape)\n",
+"print('d.size = ', d.size)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+
