ready for global opt

.ipynb_checkpoints/barplotting-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

.ipynb_checkpoints/hypoptim-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

.ipynb_checkpoints/mxmodel-checkpoint.ipynb

@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T11:54:23.398548Z",
+     "start_time": "2018-09-30T11:54:23.169238Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.75,  0.9 ,  0.85,  0.35, -0.8 , -0.6 ,  0.8 ,  0.8 ])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import h5py\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "#now load this dataset \n",
+    "h5f = h5py.File('./datasets/s8_sio2tio2_v2.h5','r')\n",
+    "X = h5f['sizes'][:]\n",
+    "Y = h5f['spectrum'][:]\n",
+    "\n",
+    "\n",
+    "#create a train - test split of the dataset\n",
+    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.4, random_state=42)\n",
+    "\n",
+    "# normalize inputs \n",
+    "x_test = (x_test - 50)/20 \n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T11:45:01.118397Z",
+     "start_time": "2018-09-30T11:44:59.855736Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import mxnet as mx\n",
+    "\n",
+    "# Step1: Load the model in MXNet\n",
+    "\n",
+    "# Use the same prefix and epoch parameters we used in save_mxnet_model API.\n",
+    "sym, arg_params, aux_params = mx.model.load_checkpoint(prefix='my_mod', epoch=0)\n",
+    "\n",
+    "# We use the data_names and data_shapes returned by save_mxnet_model API.\n",
+    "mod = mx.mod.Module(symbol=sym, \n",
+    "                    data_names=['/input_12'], \n",
+    "                    context=mx.gpu(1), \n",
+    "                    label_names=None)\n",
+    "mod.bind(for_training=False, \n",
+    "         data_shapes=[('/input_12', (1,8))], \n",
+    "         label_shapes=mod._label_shapes)\n",
+    "mod.set_params(arg_params, aux_params, allow_missing=True)                      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T11:47:00.407910Z",
+     "start_time": "2018-09-30T11:47:00.306416Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "gpu(1)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "size = np.random.randint(30, 71, (1,8))\n",
+    "#spec_ac = snlay.calc_spectrum(size, mats, lams)\n",
+    "size = (size - 50.0)/20.0\n",
+    "#size = np.expand_dims(size, axis = 0)\n",
+    "size.shape\n",
+    "#res1 = model.predict(size)\n",
+    "#data_iter = mx.io.NDArrayIter(size, None, 1)\n",
+    "for icv in np.arange(1000):\n",
+    "    res2 = mod.predict(size)\n",
+    "res2.context"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T11:42:00.611085Z",
+     "start_time": "2018-09-30T11:42:00.601881Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "[[0. 0. 0. 0.]\n",
+       " [0. 0. 0. 0.]\n",
+       " [0. 0. 0. 0.]]\n",
+       "<NDArray 3x4 @gpu(0)>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import mxnet as mx\n",
+    "from mxnet import nd\n",
+    "mx.random.seed(1)\n",
+    "x = nd.empty((3, 4), ctx=mx.gpu(0))\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

barplotting.ipynb

@@ -0,0 +1,84 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T16:27:30.263598Z",
+     "start_time": "2018-09-30T16:27:29.930319Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# The data\n",
+    "resnet = np.asarray([0.8375262254326821, 0.8678992675159352, 0.8527975528611832, 0.8523278104598541, 0.884647507843369, 0.9665709773157553, \n",
+    "              0.941322385257967, 0.9607122054596926, 1.0446496050070293, 1.5978083756003671])\n",
+    "fully = np.asarray([0.2938374312588796, 0.3047676507445782, 0.29713952786808656, 0.3112794673912947, 0.335618492063216, 0.3853395053207258, \n",
+    "         0.40619997414669395, 0.43130068820046324, 0.5361458921673319, 0.6306651849357078])\n",
+    "\n",
+    "resnet = np.flip(resnet)\n",
+    "fully = np.flip(fully)\n",
+    "indices = range(len(resnet))\n",
+    "names = ['1x','1x','2x','3x','4x', '5x', '6x', '7x', '8x', '9x', '10x']\n",
+    "# Calculate optimal width\n",
+    "width = np.min(np.diff(indices))/3.\n",
+    "\n",
+    "fig = plt.figure(figsize=(5,2.5))\n",
+    "ax = fig.add_subplot(111)\n",
+    "ax.bar(indices-width/2.,resnet,width,color='b',label='resnet (D)')\n",
+    "ax.bar(indices+width/2.,fully,width,color='r',label='densenet (A)')\n",
+    "#tiks = ax.get_xticks().tolist()\n",
+    "#ax.axes.set_xticklabels(names)\n",
+    "plt.xticks(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), \n",
+    "               ('1', '2', '3', '4', '5',  '6', '7', '8', '9', '10'))\n",
+    "ax.set_xlabel('Training Data Size (multiples of 9K)')\n",
+    "ax.set_ylabel('Test MRE (%)')\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "ax.spines['right'].set_visible(False)\n",
+    "ax.legend(loc=\"best\")\n",
+    "fig.savefig(\"datasize.pdf\", bbox_inches='tight')\n",
+    "#ax.spines['bottom'].set_visible(False)\n",
+    "#ax.spines['left'].set_visible(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

mxmodel.ipynb

@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T16:20:36.613702Z",
+     "start_time": "2018-09-30T16:20:35.532595Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([67., 31., 30., 37., 56., 37., 61., 49.])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import h5py\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "#now load this dataset \n",
+    "h5f = h5py.File('./datasets/s8_sio2tio2_v2.h5','r')\n",
+    "X = h5f['sizes'][:]\n",
+    "Y = h5f['spectrum'][:]\n",
+    "\n",
+    "\n",
+    "#create a train - test split of the dataset\n",
+    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.4, random_state=42)\n",
+    "\n",
+    "\n",
+    "\n",
+    "x_test[9]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T16:20:49.933501Z",
+     "start_time": "2018-09-30T16:20:45.837033Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import mxnet as mx\n",
+    "\n",
+    "# Step1: Load the model in MXNet\n",
+    "\n",
+    "# Use the same prefix and epoch parameters we used in save_mxnet_model API.\n",
+    "sym, arg_params, aux_params = mx.model.load_checkpoint(prefix='my_mod', epoch=0)\n",
+    "\n",
+    "# We use the data_names and data_shapes returned by save_mxnet_model API.\n",
+    "mod = mx.mod.Module(symbol=sym, \n",
+    "                    data_names=['/input_12'], \n",
+    "                    context=mx.gpu(), \n",
+    "                    label_names=None)\n",
+    "mod.bind(for_training=False, \n",
+    "         data_shapes=[('/input_12', (1,8))], \n",
+    "         label_shapes=mod._label_shapes)\n",
+    "mod.set_params(arg_params, aux_params, allow_missing=True)                      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T16:21:01.661357Z",
+     "start_time": "2018-09-30T16:21:01.485033Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.16714763641357422\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scnets as scn\n",
+    "import mxnet as mx\n",
+    "from mxnet import nd\n",
+    "import time\n",
+    "\n",
+    "size = np.random.randint(30, 71, (1,8))\n",
+    "#spec_ac = snlay.calc_spectrum(size, mats, lams)\n",
+    "size = (size - 50.0)/20.0\n",
+    "#size = np.expand_dims(size, axis = 0)\n",
+    "size.shape\n",
+    "\n",
+    "y_true = nd.array(y_test[9], ctx=mx.gpu())\n",
+    "\n",
+    "reps = 1000\n",
+    "start = time.time()\n",
+    "#res1 = model.predict(size)\n",
+    "#data_iter = mx.io.NDArrayIter(size, None, 1)\n",
+    "for icv in np.arange(reps):\n",
+    "    size = np.random.randint(30, 71, (1,8))\n",
+    "    size = (size - 50.0)/20.0\n",
+    "    res2 = mod.predict(size)\n",
+    "    err = nd.abs(y_true - res2)/y_true\n",
+    "    finerr = nd.sum(err)\n",
+    "    \n",
+    "end = time.time()\n",
+    "print(1000*(end - start)/reps)   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T16:21:08.815235Z",
+     "start_time": "2018-09-30T16:21:08.809272Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import scnets as scn\n",
+    "import mxnet as mx\n",
+    "from mxnet import nd\n",
+    "def give_loss(x_in, y_true, mxmod):\n",
+    "    y_t = nd.array(y_true, ctx=mx.gpu())\n",
+    "    res2 = mxmod.predict(x_in)\n",
+    "    err = nd.abs(y_t - res2)/y_t\n",
+    "    err2 = nd.sum(err).asscalar()\n",
+    "    #ez2 = err2.copyto(mx.cpu())\n",
+    "    return err2\n",
+    "\n",
+    "#     err2cp = nd.zeros_like(err2, ctx=mx.cpu())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T16:49:04.385979Z",
+     "start_time": "2018-09-30T16:49:02.703408Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.672537088394165\n"
+     ]
+    }
+   ],
+   "source": [
+    "size = np.random.randint(30, 71, (1,8))\n",
+    "#spec_ac = snlay.calc_spectrum(size, mats, lams)\n",
+    "size = (size - 50.0)/20.0\n",
+    "\n",
+    "ans = 1.00\n",
+    "cd = give_loss(x_in=size, y_true=y_test[6], mxmod=mod)\n",
+    "\n",
+    "\n",
+    "reps = 1000\n",
+    "start = time.time()\n",
+    "for icv in np.arange(reps):\n",
+    "    cc = give_loss(x_in=size, y_true=y_test[6], mxmod=mod)\n",
+    "    #ans = cc.asscalar()\n",
+    "\n",
+    "end = time.time()\n",
+    "print(1000*(end - start)/reps)   # "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T16:21:45.903809Z",
+     "start_time": "2018-09-30T16:21:45.898205Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "248.17133"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2018-09-30T15:58:44.524220Z",
+     "start_time": "2018-09-30T15:58:44.518399Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.__getitem__(0).asscalar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

my_mod-symbol.json

@@ -0,0 +1,494 @@
+{
+  "nodes": [
+    {
+      "op": "null", 
+      "name": "/input_12", 
+      "attrs": {
+        "__dtype__": "0", 
+        "__shape__": "(0, 8)"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "null", 
+      "name": "dense_1/kernel2", 
+      "attrs": {
+        "__dtype__": "0", 
+        "__shape__": "(8, 256)"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "dot", 
+      "name": "dot3", 
+      "inputs": [[0, 0, 0], [1, 0, 0]]
+    }, 
+    {
+      "op": "null", 
+      "name": "dense_1/bias2", 
+      "attrs": {
+        "__dtype__": "0", 
+        "__shape__": "(256,)"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "broadcast_add", 
+      "name": "broadcast_add17", 
+      "inputs": [[2, 0, 0], [3, 0, 0]]
+    }, 
+    {
+      "op": "LeakyReLU", 
+      "name": "leakyrelu9", 
+      "attrs": {
+        "act_type": "leaky", 
+        "slope": "0.0"
+      }, 
+      "inputs": [[4, 0, 0]]
+    }, 
+    {
+      "op": "null", 
+      "name": "p_re_lu_1/alpha2", 
+      "attrs": {
+        "__dtype__": "0", 
+        "__shape__": "(256,)"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "_mul_scalar", 
+      "name": "_mulscalar9", 
+      "attrs": {"scalar": "-1.0"}, 
+      "inputs": [[6, 0, 0]]
+    }, 
+    {
+      "op": "_mul_scalar", 
+      "name": "_mulscalar11", 
+      "attrs": {"scalar": "-1.0"}, 
+      "inputs": [[4, 0, 0]]
+    }, 
+    {
+      "op": "LeakyReLU", 
+      "name": "leakyrelu11", 
+      "attrs": {
+        "act_type": "leaky", 
+        "slope": "0.0"
+      }, 
+      "inputs": [[8, 0, 0]]
+    }, 
+    {
+      "op": "broadcast_mul", 
+      "name": "broadcast_mul5", 
+      "inputs": [[7, 0, 0], [9, 0, 0]]
+    }, 
+    {
+      "op": "broadcast_add", 
+      "name": "broadcast_add19", 
+      "inputs": [[5, 0, 0], [10, 0, 0]]
+    }, 
+    {
+      "op": "Reshape", 
+      "name": "reshape13", 
+      "attrs": {"shape": "(0, 8, 32)"}, 
+      "inputs": [[11, 0, 0]]
+    }, 
+    {
+      "op": "expand_dims", 
+      "name": "expand_dims17", 
+      "attrs": {"axis": "2"}, 
+      "inputs": [[12, 0, 0]]
+    }, 
+    {
+      "op": "transpose", 
+      "name": "transpose27", 
+      "attrs": {"axes": "[0, 3, 1, 2]"}, 
+      "inputs": [[13, 0, 0]]
+    }, 
+    {
+      "op": "null", 
+      "name": "conv1d_1/kernel2", 
+      "attrs": {
+        "__dtype__": "0", 
+        "__shape__": "(3, 32, 32)"
+      }, 
+      "inputs": []
+    }, 
+    {
+      "op": "expand_dims", 
+      "name": "expand_dims19", 
+      "attrs": {"axis": "1"}, 
+      "inputs": [[15, 0, 0]]
+    }, 
+    {
+      "op": "transpose", 
+      "name": "transpose28", 
+      "attrs": {"axes": "(3, 2, 0, 1)"}, 
+      "inputs": [[16, 0, 0]]
+    }, 
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