From 71ddafc01efe593ce7737855b60b3e2d8cffcbcc Mon Sep 17 00:00:00 2001 From: Damien Bose Date: Wed, 6 Dec 2023 13:55:13 +0000 Subject: [PATCH] Clear outputs --- .../challenge-2/getting-started.ipynb | 359 ++---------------- 1 file changed, 31 insertions(+), 328 deletions(-) diff --git a/doxa-challenges/challenge-2/getting-started.ipynb b/doxa-challenges/challenge-2/getting-started.ipynb index 378b8a9..6f11394 100644 --- a/doxa-challenges/challenge-2/getting-started.ipynb +++ b/doxa-challenges/challenge-2/getting-started.ipynb @@ -49,131 +49,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: numpy in /Users/damienbose/opt/anaconda3/envs/doxa/lib/python3.10/site-packages (1.23.5)\n", - "Requirement already satisfied: pandas in /Users/damienbose/opt/anaconda3/envs/doxa/lib/python3.10/site-packages (1.5.2)\n", - "Requirement already satisfied: matplotlib in /Users/damienbose/opt/anaconda3/envs/doxa/lib/python3.10/site-packages (3.6.2)\n", - 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"execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -202,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -214,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -231,17 +109,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using cpu device\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "device = (\n", " \"cuda\"\n", @@ -267,22 +137,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 286k 100 286k 0 0 952k 0 --:--:-- --:--:-- --:--:-- 972k\n", - " % Total % Received % Xferd Average Speed Time Time Time Current\n", - " Dload Upload Total Spent Left Speed\n", - "100 67238 100 67238 0 0 186k 0 --:--:-- --:--:-- --:--:-- 190k\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Download the dataset if we don't already have it!\n", "if not os.path.exists(\"data\"):\n", @@ -294,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -311,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -330,146 +187,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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fixed_acidityvolatile_aciditycitric_acidresidual_sugarchloridesfree_sulfur_dioxidetotal_sulfur_dioxidedensitypHsulphatesalcohol
06.60.2400.357.70.03136.0135.00.993803.190.3710.5
18.30.2800.482.10.0936.012.00.994083.260.6212.4
27.70.7150.012.10.06431.043.00.993713.410.5711.8
35.20.3700.331.20.02813.081.00.990203.370.3811.7
46.60.2600.5615.40.05332.0141.00.998103.110.499.3
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" - ], - "text/plain": [ - " fixed_acidity volatile_acidity citric_acid residual_sugar chlorides \\\n", - "0 6.6 0.240 0.35 7.7 0.031 \n", - "1 8.3 0.280 0.48 2.1 0.093 \n", - "2 7.7 0.715 0.01 2.1 0.064 \n", - "3 5.2 0.370 0.33 1.2 0.028 \n", - "4 6.6 0.260 0.56 15.4 0.053 \n", - "\n", - " free_sulfur_dioxide total_sulfur_dioxide density pH sulphates \\\n", - "0 36.0 135.0 0.99380 3.19 0.37 \n", - "1 6.0 12.0 0.99408 3.26 0.62 \n", - "2 31.0 43.0 0.99371 3.41 0.57 \n", - "3 13.0 81.0 0.99020 3.37 0.38 \n", - "4 32.0 141.0 0.99810 3.11 0.49 \n", - "\n", - " alcohol \n", - "0 10.5 \n", - "1 12.4 \n", - "2 11.8 \n", - "3 11.7 \n", - "4 9.3 " - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# TODO: Print the first five rows of the data\n", "\n", @@ -478,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -489,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -514,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -530,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -553,17 +273,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sequential()\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "num_input_features, num_hidden_neurons = X_train.shape[1], 10\n", "model = nn.Sequential(\n", @@ -590,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -609,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -630,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -647,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -671,18 +383,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "incomplete input (3340681339.py, line 3)", - "output_type": "error", - "traceback": [ - "\u001b[0;36m Cell \u001b[0;32mIn[21], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m \u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m incomplete input\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "for _ in range(num_epochs):\n", " # TODO: add the code to train your model. (hint: use plotlosses to see the live loss plot)\n",