Clear outputs

Author: Patrick Huembeli

Notebooks/pytorch-qiskit-0.1-u3.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,18 +33,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/phuembeli/Qiskit_develop/qiskit-terra/qiskit/pulse/channels/pulse_channels.py:25: DeprecationWarning: Channels have been migrated. Please use `from qiskit.pulse.channels import X` rather than `from qiskit.pulse.channels.pulse_channels import X`.\n",
-      "  DeprecationWarning)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from qiskit import QuantumRegister,QuantumCircuit,ClassicalRegister,execute\n",
     "from qiskit.circuit import Parameter\n",
@@ -54,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2019-10-01T16:09:30.598730Z",
@@ -201,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,17 +237,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tensor([[0.0206, 0.0210, 0.0104]])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# x = torch.tensor([np.pi/4, np.pi/4, np.pi/4], requires_grad=True)\n",
     "x = torch.tensor([[0.0, 0.0, 0.0]], requires_grad=True)\n",
@@ -284,39 +267,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 100/100 [00:09<00:00, 10.37it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1277a0b70>]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "qc = TorchCircuit.apply\n",
     "\n",
@@ -358,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -380,117 +333,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "0it [00:00, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "9920512it [00:09, 1033422.94it/s]                             \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "0it [00:00, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "32768it [00:00, 91809.04it/s]                            \n",
-      "0it [00:00, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "1654784it [00:02, 567437.39it/s]                             \n",
-      "0it [00:00, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "8192it [00:00, 29906.12it/s]            "
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
-      "Processing...\n",
-      "Done!\n",
-      "Dataset MNIST\n",
-      "    Number of datapoints: 40\n",
-      "    Root location: ./data\n",
-      "    Split: Train\n",
-      "    StandardTransform\n",
-      "Transform: Compose(\n",
-      "               ToTensor()\n",
-      "           )\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "import torchvision\n",
@@ -533,7 +378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -563,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -589,26 +434,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-0.7789\n",
-      "-0.8216\n",
-      "-0.8955\n",
-      "-0.99\n",
-      "-0.8736\n",
-      "-0.921\n",
-      "-0.9768\n",
-      "-0.926\n",
-      "-0.8887\n",
-      "-0.8338\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "epochs = 10\n",
     "for epoch in range(epochs):\n",
@@ -638,17 +466,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy is: 1.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "accuracy = 0\n",
     "number = 0\n",