chapter 04 05 06代码接口更新

chapter_05_deep-learning-computation/0_model-construction.ipynb

@@ -15,46 +15,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append('..')"
-   ],
+   "execution_count": 1,
    "metadata": {
     "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
     "pycharm": {
      "name": "#%%\n"
     }
-   }
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('..')"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "origin_pos": 2,
     "tab": [
      "pytorch"
     ]
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[WARNING] CORE(326787,ffff9641e640,python):2025-12-14-22:36:28.672.832 [mindspore/core/utils/ms_context.cc:533] GetJitLevel] Set jit level to O2 for rank table startup method.\n",
+      "/usr/local/python3.10.14/lib/python3.10/site-packages/numpy/core/getlimits.py:500: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
+      "  setattr(self, word, getattr(machar, word).flat[0])\n",
+      "/usr/local/python3.10.14/lib/python3.10/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
+      "  return self._float_to_str(self.smallest_subnormal)\n",
+      "/usr/local/python3.10.14/lib/python3.10/site-packages/numpy/core/getlimits.py:500: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
+      "  setattr(self, word, getattr(machar, word).flat[0])\n",
+      "/usr/local/python3.10.14/lib/python3.10/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
+      "  return self._float_to_str(self.smallest_subnormal)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "Tensor(shape=[2, 10], dtype=Float32, value=\n",
-       "[[-6.79369224e-03,  7.97413464e-04,  3.23574618e-03 ...  7.16019771e-04, -1.44797785e-04,  1.57120079e-03],\n",
-       " [-6.07040105e-03,  5.09693520e-03,  4.14576288e-03 ...  1.12101820e-03, -4.35058493e-04,  4.35355678e-03]])"
+       "[[-6.29626662e-02,  1.17263123e-01,  2.36853719e-01 ... -1.18644319e-01, -6.00607768e-02, -3.54341976e-02],\n",
+       " [ 2.40386669e-02,  1.30358398e-01,  1.87603414e-01 ... -5.95855154e-03, -1.92452669e-01,  9.18698683e-03]])"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "from d2l import mindspore as d2l\n",
     "import mindspore\n",
-    "from mindspore import nn, ops, Tensor\n",
+    "from mindspore import nn, mint, Tensor #ops->mint\n",
     "import numpy as np\n",
     "\n",
     "net = nn.SequentialCell([nn.Dense(20, 256), nn.ReLU(), nn.Dense(256, 10)])\n",
@@ -87,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "origin_pos": 12,
     "tab": [
@@ -123,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "origin_pos": 16,
     "tab": [
@@ -135,11 +153,11 @@
      "data": {
       "text/plain": [
        "Tensor(shape=[2, 10], dtype=Float32, value=\n",
-       "[[ 1.00408215e-06, -7.51196174e-04, -1.47786422e-03 ...  9.07916750e-04,  3.31065315e-03,  3.96206928e-03],\n",
-       " [-1.93189026e-03, -1.93725619e-03, -2.41200835e-03 ...  5.95276617e-03,  4.78746044e-03,  5.16231870e-03]])"
+       "[[-2.16613740e-01,  1.00081787e-02, -7.52887726e-02 ... -3.07381839e-01, -2.11595483e-02, -8.64412785e-02],\n",
+       " [-2.62258470e-01,  2.33387649e-02,  1.90157443e-04 ... -2.12538928e-01, -8.94006062e-03, -1.43828928e-01]])"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -162,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "origin_pos": 26,
     "tab": [
@@ -174,11 +192,11 @@
      "data": {
       "text/plain": [
        "Tensor(shape=[2, 10], dtype=Float32, value=\n",
-       "[[ 1.56846642e-03, -4.25875094e-03,  5.70291071e-04 ... -1.61940954e-03,  2.20699748e-03, -5.04909083e-03],\n",
-       " [-6.69776287e-04, -4.14503831e-03,  6.94219721e-04 ... -3.46967345e-03,  5.57024789e-04, -5.33907907e-03]])"
+       "[[ 1.53975673e-02,  1.21631876e-01, -8.63668323e-02 ... -1.22261629e-01,  2.93108106e-01, -3.11329216e-01],\n",
+       " [-2.56069917e-02,  1.63210899e-01, -8.27370808e-02 ... -1.32705793e-01,  3.17860305e-01, -3.00570577e-01]])"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -213,25 +231,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "origin_pos": 34,
     "tab": [
      "pytorch"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Tensor(shape=[], dtype=Float32, value= 0.542837)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "class FixedHiddenMLP(nn.Cell):\n",
     "    def __init__(self):\n",
@@ -254,18 +261,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "net = FixedHiddenMLP()\n",
-    "net(X)"
-   ],
+   "execution_count": 7,
    "metadata": {
     "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
     "pycharm": {
      "name": "#%%\n"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Tensor(shape=[], dtype=Float32, value= 0.0628106)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "net = FixedHiddenMLP()\n",
+    "net(X)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -280,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {
     "origin_pos": 37,
     "tab": [
@@ -291,10 +312,10 @@
     {
      "data": {
       "text/plain": [
-       "Tensor(shape=[], dtype=Float32, value= -0.0956512)"
+       "Tensor(shape=[], dtype=Float32, value= -0.418528)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -318,9 +339,9 @@
  "metadata": {
   "celltoolbar": "Slideshow",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.10",
    "language": "python",
-   "name": "python3"
+   "name": "py310"
   },
   "language_info": {
    "codemirror_mode": {
@@ -332,7 +353,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.10.14"
   },
   "rise": {
    "autolaunch": true,
@@ -343,4 +364,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}