From ed161e7fb20a697ecba070ef7db4c231d700f245 Mon Sep 17 00:00:00 2001 From: Mitja Felicijan Date: Wed, 25 Mar 2020 05:12:40 +0100 Subject: Move to my own static generator --- src/files/dna-sequence/benchmarks.ods | Bin 21911 -> 0 bytes src/files/dna-sequence/chart-encoding-speed.png | Bin 14201 -> 0 bytes src/files/dna-sequence/chart-file-sizes.png | Bin 12391 -> 0 bytes src/files/dna-sequence/dna-basics.jpg | Bin 165883 -> 0 bytes src/files/dna-sequence/quote.png | Bin 1068 -> 0 bytes src/files/dna-sequence/sample-binary-file.png | Bin 66417 -> 0 bytes src/files/dna-sequence/sample.png | Bin 65930 -> 0 bytes src/files/do-fuse/copy-benchmarks.tsv | 101 -- src/files/do-fuse/fuse-droplets.png | Bin 42891 -> 0 bytes src/files/do-fuse/fuse-spaces.png | Bin 32450 -> 0 bytes src/files/do-fuse/sqlite-benchmarks.tsv | 1001 -------------------- src/files/go-profiling/golang-profiling-cpu.pdf | Bin 16518 -> 0 bytes src/files/go-profiling/golang-profiling-mem.pdf | Bin 19221 -> 0 bytes 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"cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/home/m/.local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.0.0-beta1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", - "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", - "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", - "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", - "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", - "/home/m/.local/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", - " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" - ] - } - ], - "source": [ - "import tensorflow as tf\n", - "from tensorflow import keras\n", - "\n", - "# Helper libraries\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "print(tf.__version__)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from numpy import genfromtxt\n", - "data = genfromtxt('data.csv', delimiter=',')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "data_input = data[:,0:3]\n", - "data_labels = data[:,3]\n", - "\n", - "#data_input = np.transpose(data_input)\n", - "#data_labels = np.transpose(data_labels)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(600, 3)\n", - "[1.e-01 1.e+00 3.e+02]\n" - ] - } - ], - "source": [ - "print(np.shape(data_input))\n", - "print(data_input[2])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "print(len(data_input))\n", - "print(len(data_labels))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(500, 3)\n", - "(100, 3)\n", - "(500,)\n", - "(100,)\n" - ] - } - ], - "source": [ - "data_input_train = data_input[0:500,:]\n", - "data_input_test = data_input[500:,:]\n", - "\n", - "data_labels_train = data_labels[0:500]\n", - "data_labels_test = data_labels[500:]\n", - "\n", - "print(np.shape(data_input_train))\n", - "print(np.shape(data_input_test))\n", - "\n", - "print(np.shape(data_labels_train))\n", - "print(np.shape(data_labels_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "model = keras.Sequential([\n", - " keras.layers.Dense(128, activation='relu', input_shape=[3]),\n", - " keras.layers.Dense(512, activation='relu'),\n", - " keras.layers.Dense(512, activation='relu'),\n", - " keras.layers.Dense(512, activation='relu'),\n", - " keras.layers.Dense(128, activation='relu'),\n", - " keras.layers.Dense(1)\n", - "])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "optimizer = tf.keras.optimizers.RMSprop(0.001)\n", - "model.compile(loss='mse',\n", - " optimizer=optimizer,\n", - " metrics=['accuracy'])" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Train on 500 samples\n", - "Epoch 1/100\n", - "500/500 [==============================] - 0s 399us/sample - loss: 247.2794 - accuracy: 0.0040\n", - "Epoch 2/100\n", - "500/500 [==============================] - 0s 121us/sample - loss: 4.2495 - accuracy: 0.0060\n", - "Epoch 3/100\n", - "500/500 [==============================] - 0s 131us/sample - loss: 1.8787 - accuracy: 0.0040\n", - "Epoch 4/100\n", - "500/500 [==============================] - 0s 121us/sample - loss: 0.4284 - accuracy: 0.0060\n", - "Epoch 5/100\n", - "500/500 [==============================] - 0s 107us/sample - loss: 4.7904 - accuracy: 0.0080\n", - "Epoch 6/100\n", - "500/500 [==============================] - 0s 113us/sample - loss: 0.0819 - accuracy: 0.0040\n", - "Epoch 7/100\n", - "500/500 [==============================] - 0s 108us/sample - loss: 1.6904 - accuracy: 0.0040\n", - "Epoch 8/100\n", - "500/500 [==============================] - 0s 116us/sample - loss: 0.1761 - accuracy: 0.0040\n", - "Epoch 9/100\n", - "500/500 [==============================] - 0s 142us/sample - loss: 0.1135 - accuracy: 0.0040\n", - "Epoch 10/100\n", - "500/500 [==============================] - 0s 124us/sample - loss: 0.4387 - accuracy: 0.0040\n", - "Epoch 11/100\n", - "500/500 [==============================] - 0s 112us/sample - loss: 0.0815 - accuracy: 0.0040\n", - "Epoch 12/100\n", - "500/500 [==============================] - 0s 117us/sample - loss: 0.1725 - accuracy: 0.0040\n", - "Epoch 13/100\n", - "500/500 [==============================] - 0s 119us/sample - loss: 0.1487 - accuracy: 0.0040\n", - "Epoch 14/100\n", - "500/500 [==============================] - 0s 111us/sample - loss: 0.0720 - accuracy: 0.0040\n", - "Epoch 15/100\n", - "500/500 [==============================] - 0s 111us/sample - loss: 0.3110 - accuracy: 0.0040\n", - "Epoch 16/100\n", - "500/500 [==============================] - 0s 128us/sample - loss: 0.0947 - accuracy: 0.0040\n", - "Epoch 17/100\n", - "500/500 [==============================] - 0s 133us/sample - loss: 0.0739 - accuracy: 0.0040\n", - "Epoch 18/100\n", - "500/500 [==============================] - 0s 131us/sample - loss: 0.1353 - accuracy: 0.0060\n", - "Epoch 19/100\n", - "500/500 [==============================] - 0s 135us/sample - loss: 0.0837 - accuracy: 0.0040\n", - "Epoch 20/100\n", - "500/500 [==============================] - 0s 130us/sample - loss: 0.0754 - accuracy: 0.0040\n", - "Epoch 21/100\n", - "500/500 [==============================] - 0s 118us/sample - loss: 0.0840 - accuracy: 0.0040\n", - "Epoch 22/100\n", - "500/500 [==============================] - 0s 115us/sample - loss: 0.1105 - accuracy: 0.0040\n", - "Epoch 23/100\n", - "500/500 [==============================] - 0s 116us/sample - loss: 0.0651 - accuracy: 0.0040\n", - "Epoch 24/100\n", - "500/500 [==============================] - 0s 109us/sample - loss: 0.0615 - accuracy: 0.0040\n", - "Epoch 25/100\n", - "500/500 [==============================] - 0s 118us/sample - loss: 0.0656 - accuracy: 0.0040\n", - "Epoch 26/100\n", - "500/500 [==============================] - 0s 113us/sample - loss: 0.0695 - accuracy: 0.0040\n", - "Epoch 27/100\n", - "500/500 [==============================] - 0s 116us/sample - loss: 0.0585 - accuracy: 0.0040\n", - "Epoch 28/100\n", - "500/500 [==============================] - 0s 118us/sample - loss: 0.1300 - accuracy: 0.0040\n", - "Epoch 29/100\n", - "500/500 [==============================] - 0s 112us/sample - loss: 0.0567 - accuracy: 0.0040\n", - "Epoch 30/100\n", - "500/500 [==============================] - 0s 137us/sample - loss: 0.0647 - accuracy: 0.0040\n", - "Epoch 31/100\n", - "500/500 [==============================] - 0s 130us/sample - loss: 0.0559 - accuracy: 0.0040\n", - "Epoch 32/100\n", - "500/500 [==============================] - 0s 130us/sample - loss: 0.0576 - accuracy: 0.0040\n", - "Epoch 33/100\n", - "500/500 [==============================] - 0s 128us/sample - loss: 0.0578 - accuracy: 0.0040\n", - "Epoch 34/100\n", - "500/500 [==============================] - 0s 130us/sample - loss: 0.0512 - accuracy: 0.0040\n", - "Epoch 35/100\n", - "500/500 [==============================] - 0s 114us/sample - loss: 0.0601 - accuracy: 0.0040\n", - "Epoch 36/100\n", - "500/500 [==============================] - 0s 111us/sample - loss: 0.0531 - accuracy: 0.0040\n", - "Epoch 37/100\n", - "500/500 [==============================] - 0s 130us/sample - loss: 0.0532 - accuracy: 0.0040\n", - "Epoch 38/100\n", - "500/500 [==============================] - 0s 131us/sample - loss: 0.0480 - accuracy: 0.0040\n", - "Epoch 39/100\n", - "500/500 [==============================] - 0s 136us/sample - loss: 0.0503 - accuracy: 0.0040\n", - "Epoch 40/100\n", - "500/500 [==============================] - 0s 134us/sample - loss: 0.0468 - accuracy: 0.0040\n", - "Epoch 41/100\n", - "500/500 [==============================] - 0s 115us/sample - loss: 0.0509 - accuracy: 0.0040\n", - "Epoch 42/100\n", - "500/500 [==============================] - 0s 109us/sample - loss: 0.0453 - accuracy: 0.0040\n", - "Epoch 43/100\n", - "500/500 [==============================] - 0s 111us/sample - loss: 0.0484 - accuracy: 0.0040\n", - "Epoch 44/100\n", - "500/500 [==============================] - 0s 104us/sample - loss: 0.0458 - accuracy: 0.0040\n", - "Epoch 45/100\n", - "500/500 [==============================] - 0s 110us/sample - loss: 0.0481 - accuracy: 0.0040\n", - "Epoch 46/100\n", - "500/500 [==============================] - 0s 114us/sample - loss: 0.0468 - accuracy: 0.0060\n", - "Epoch 47/100\n", - "500/500 [==============================] - 0s 124us/sample - loss: 0.0473 - accuracy: 0.0060\n", - "Epoch 48/100\n", - "500/500 [==============================] - 0s 137us/sample - loss: 0.0455 - accuracy: 0.0040\n", - "Epoch 49/100\n", - "500/500 [==============================] - 0s 125us/sample - loss: 0.0431 - accuracy: 0.0060\n", - "Epoch 50/100\n", - "500/500 [==============================] - 0s 132us/sample - loss: 0.0432 - accuracy: 0.0060\n", - "Epoch 51/100\n", - "500/500 [==============================] - 0s 116us/sample - loss: 0.0484 - accuracy: 0.0060\n", - "Epoch 52/100\n", - "500/500 [==============================] - 0s 112us/sample - loss: 0.0482 - accuracy: 0.0040\n", - "Epoch 53/100\n", - "500/500 [==============================] - 0s 117us/sample - loss: 0.0444 - accuracy: 0.0060\n", - "Epoch 54/100\n", - "500/500 [==============================] - 0s 109us/sample - loss: 0.0469 - accuracy: 0.0060\n", - "Epoch 55/100\n", - "500/500 [==============================] - 0s 106us/sample - loss: 0.0427 - accuracy: 0.0040\n", - "Epoch 56/100\n", - "500/500 [==============================] - 0s 110us/sample - loss: 0.0433 - accuracy: 0.0040\n", - "Epoch 57/100\n", - "500/500 [==============================] - 0s 102us/sample - loss: 0.0437 - accuracy: 0.0060\n", - "Epoch 58/100\n", - "500/500 [==============================] - 0s 117us/sample - loss: 0.0425 - accuracy: 0.0040\n", - "Epoch 59/100\n", - "500/500 [==============================] - 0s 105us/sample - loss: 0.0418 - accuracy: 0.0040\n", - "Epoch 60/100\n", - "500/500 [==============================] - 0s 109us/sample - loss: 0.0397 - accuracy: 0.0040\n", - "Epoch 61/100\n", - "500/500 [==============================] - 0s 119us/sample - loss: 0.0507 - accuracy: 0.0040\n", - "Epoch 62/100\n", - "500/500 [==============================] - 0s 112us/sample - loss: 0.0402 - accuracy: 0.0060\n", - "Epoch 63/100\n", - "500/500 [==============================] - 0s 133us/sample - loss: 0.0397 - accuracy: 0.0040\n", - "Epoch 64/100\n", - "500/500 [==============================] - 0s 132us/sample - loss: 0.0427 - accuracy: 0.0060\n", - "Epoch 65/100\n", - "500/500 [==============================] - 0s 138us/sample - loss: 0.0398 - accuracy: 0.0040\n", - "Epoch 66/100\n", - "500/500 [==============================] - 0s 145us/sample - loss: 0.0375 - accuracy: 0.0060\n", - "Epoch 67/100\n", - "500/500 [==============================] - 0s 138us/sample - loss: 0.0402 - accuracy: 0.0060\n", - "Epoch 68/100\n", - "500/500 [==============================] - 0s 132us/sample - loss: 0.0388 - accuracy: 0.0080\n", - "Epoch 69/100\n", - "500/500 [==============================] - 0s 115us/sample - loss: 0.0375 - accuracy: 0.0080\n", - "Epoch 70/100\n", - "500/500 [==============================] - 0s 113us/sample - loss: 0.0384 - accuracy: 0.0040\n", - "Epoch 71/100\n", - "500/500 [==============================] - 0s 109us/sample - loss: 0.0360 - accuracy: 0.0080\n", - "Epoch 72/100\n", - "500/500 [==============================] - 0s 111us/sample - loss: 0.0350 - accuracy: 0.0080\n", - "Epoch 73/100\n", - "500/500 [==============================] - 0s 118us/sample - loss: 0.0370 - accuracy: 0.0060\n", - "Epoch 74/100\n", - "500/500 [==============================] - 0s 95us/sample - loss: 0.0354 - accuracy: 0.0080\n", - "Epoch 75/100\n", - "500/500 [==============================] - 0s 102us/sample - loss: 0.0376 - accuracy: 0.0060\n", - "Epoch 76/100\n", - "500/500 [==============================] - 0s 106us/sample - loss: 0.0371 - accuracy: 0.0080\n", - "Epoch 77/100\n", - "500/500 [==============================] - 0s 100us/sample - loss: 0.0369 - accuracy: 0.0060\n", - "Epoch 78/100\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "500/500 [==============================] - 0s 98us/sample - loss: 0.0315 - accuracy: 0.0060\n", - "Epoch 79/100\n", - "500/500 [==============================] - 0s 97us/sample - loss: 0.0355 - accuracy: 0.0060\n", - "Epoch 80/100\n", - "500/500 [==============================] - 0s 100us/sample - loss: 0.0278 - accuracy: 0.0080\n", - "Epoch 81/100\n", - "500/500 [==============================] - 0s 99us/sample - loss: 0.0320 - accuracy: 0.0080\n", - "Epoch 82/100\n", - "500/500 [==============================] - 0s 99us/sample - loss: 0.0321 - accuracy: 0.0080\n", - "Epoch 83/100\n", - "500/500 [==============================] - 0s 94us/sample - loss: 0.0332 - accuracy: 0.0060\n", - "Epoch 84/100\n", - "500/500 [==============================] - 0s 106us/sample - loss: 0.0317 - accuracy: 0.0060\n", - "Epoch 85/100\n", - "500/500 [==============================] - 0s 103us/sample - loss: 0.0293 - accuracy: 0.0080\n", - "Epoch 86/100\n", - "500/500 [==============================] - 0s 107us/sample - loss: 0.0304 - accuracy: 0.0060\n", - "Epoch 87/100\n", - "500/500 [==============================] - 0s 101us/sample - loss: 0.0327 - accuracy: 0.0040\n", - "Epoch 88/100\n", - "500/500 [==============================] - 0s 100us/sample - loss: 0.0290 - accuracy: 0.0080\n", - "Epoch 89/100\n", - "500/500 [==============================] - 0s 123us/sample - loss: 0.0293 - accuracy: 0.0060\n", - "Epoch 90/100\n", - "500/500 [==============================] - 0s 104us/sample - loss: 0.0246 - accuracy: 0.0060\n", - "Epoch 91/100\n", - "500/500 [==============================] - 0s 124us/sample - loss: 0.0303 - accuracy: 0.0060\n", - "Epoch 92/100\n", - "500/500 [==============================] - 0s 129us/sample - loss: 0.0376 - accuracy: 0.0080\n", - "Epoch 93/100\n", - "500/500 [==============================] - 0s 122us/sample - loss: 0.0264 - accuracy: 0.0080\n", - "Epoch 94/100\n", - "500/500 [==============================] - 0s 102us/sample - loss: 0.0265 - accuracy: 0.0080\n", - "Epoch 95/100\n", - "500/500 [==============================] - 0s 108us/sample - loss: 0.0291 - accuracy: 0.0080\n", - "Epoch 96/100\n", - "500/500 [==============================] - 0s 101us/sample - loss: 0.0314 - accuracy: 0.0080\n", - "Epoch 97/100\n", - "500/500 [==============================] - 0s 95us/sample - loss: 0.0257 - accuracy: 0.0060\n", - "Epoch 98/100\n", - "500/500 [==============================] - 0s 100us/sample - loss: 0.0248 - accuracy: 0.0080\n", - "Epoch 99/100\n", - "500/500 [==============================] - 0s 94us/sample - loss: 0.0250 - accuracy: 0.0040\n", - "Epoch 100/100\n", - "500/500 [==============================] - 0s 106us/sample - loss: 0.0312 - accuracy: 0.0060\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#model.fit(data_input_train, data_labels_train, validation_data=(data_input_test, data_labels_test), epochs=100)\n", - "model.fit(data_input_train, data_labels_train, epochs=100)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100/100 - 0s - loss: 0.0470 - accuracy: 0.0100\n", - "\n", - "Test accuracy: 0.01\n" - ] - } - ], - "source": [ - "test_loss, test_acc = model.evaluate(data_input_test, data_labels_test, verbose=2)\n", - "\n", - "print('\\nTest accuracy:', test_acc)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.3141548]]\n" - ] - } - ], - "source": [ - "input = np.array([0.46,2,136])\n", - "input.shape = (1,3)\n", - "\n", - "prediction = model.predict(input)\n", - "print(prediction)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "predictions = model.predict(data_input_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib qt \n", - "plt.plot(predictions)\n", - "plt.plot(data_labels_test, 'r')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 204, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib qt\n", - "a = data_labels_test - predictions\n", - "plt.plot(a[0])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib qt\n", - "a = data_labels_test - predictions\n", - "plt.plot(predictions)\n", - "plt.plot(data_labels_test, 'r')\n", - "plt.plot(a[0], 'g')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 180, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "-0.08489150602276586" - ] - }, - "execution_count": 180, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.average(a[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 182, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_13\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_38 (Dense) (None, 128) 512 \n", - "_________________________________________________________________\n", - "dense_39 (Dense) (None, 512) 66048 \n", - "_________________________________________________________________\n", - "dense_40 (Dense) (None, 512) 262656 \n", - "_________________________________________________________________\n", - "dense_41 (Dense) (None, 512) 262656 \n", - "_________________________________________________________________\n", - "dense_42 (Dense) (None, 128) 65664 \n", - "_________________________________________________________________\n", - "dense_43 (Dense) (None, 1) 129 \n", - "=================================================================\n", - "Total params: 657,665\n", - "Trainable params: 657,665\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 183, - "metadata": {}, - "outputs": [], - "source": [ - "model.save('my_model.h5')" - ] - }, - { - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/src/files/sentiment-analysis/.ipynb_checkpoints/sentiment-analysis-checkpoint.ipynb b/src/files/sentiment-analysis/.ipynb_checkpoints/sentiment-analysis-checkpoint.ipynb deleted file mode 100644 index 2c0934c..0000000 --- a/src/files/sentiment-analysis/.ipynb_checkpoints/sentiment-analysis-checkpoint.ipynb +++ /dev/null @@ -1,170 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sentiment analysis of Guardian World News articles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get articles from a website" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install rss parser dependency" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install feedparser" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Parsing RSS feed for world news" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import feedparser\n", - "feed_url = \"https://www.theguardian.com/world/rss\"\n", - "feed = feedparser.parse(feed_url)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "for item in feed.entries:\n", - " # sanitize html\n", - " item.description = re.sub('<[^<]+?>', '', item.description)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install Vader Sentiment library and perform sentiment analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install vaderSentiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n", - "analyser = SentimentIntensityAnalyzer()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sentiment_results = []\n", - "for item in feed.entries:\n", - " sentiment_title = analyser.polarity_scores(item.title)\n", - " sentiment_description = analyser.polarity_scores(item.description)\n", - " sentiment_results.append([sentiment_title['compound'], sentiment_description['compound']])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install Matplotlib and visualize compound score" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "plt.rcParams['figure.figsize'] = (15, 3)\n", - "plt.plot(sentiment_results, drawstyle='steps')\n", - "plt.title('Sentiment analysis relationship between title and description (Guardian World News)')\n", - "plt.legend(['title', 'description'])\n", - "plt.show()" - ] - }, - { - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/files/sentiment-analysis/guardian-sa-title-desc-relationship.png b/src/files/sentiment-analysis/guardian-sa-title-desc-relationship.png deleted file mode 100644 index 7195bbf..0000000 Binary files a/src/files/sentiment-analysis/guardian-sa-title-desc-relationship.png and /dev/null differ diff --git a/src/files/sentiment-analysis/sentiment-analysis.ipynb b/src/files/sentiment-analysis/sentiment-analysis.ipynb deleted file mode 100644 index 2c0934c..0000000 --- a/src/files/sentiment-analysis/sentiment-analysis.ipynb +++ /dev/null @@ -1,170 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sentiment analysis of Guardian World News articles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Get articles from a website" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install rss parser dependency" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install feedparser" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Parsing RSS feed for world news" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import feedparser\n", - "feed_url = \"https://www.theguardian.com/world/rss\"\n", - "feed = feedparser.parse(feed_url)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import re\n", - "for item in feed.entries:\n", - " # sanitize html\n", - " item.description = re.sub('<[^<]+?>', '', item.description)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install Vader Sentiment library and perform sentiment analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install vaderSentiment" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n", - "analyser = SentimentIntensityAnalyzer()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sentiment_results = []\n", - "for item in feed.entries:\n", - " sentiment_title = analyser.polarity_scores(item.title)\n", - " sentiment_description = analyser.polarity_scores(item.description)\n", - " sentiment_results.append([sentiment_title['compound'], sentiment_description['compound']])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install Matplotlib and visualize compound score" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "plt.rcParams['figure.figsize'] = (15, 3)\n", - "plt.plot(sentiment_results, drawstyle='steps')\n", - "plt.title('Sentiment analysis relationship between title and description (Guardian World News)')\n", - "plt.legend(['title', 'description'])\n", - "plt.show()" - ] - }, - { - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} -- cgit v1.2.3