{ "cells": [ { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MorseEncoder started\n" ] }, { "data": { "image/png": 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EgRcCksVNT3M2Bc2ZEKj7PXpGuh1xBHDqqeEfd5/iKy62b3j0889Afn6gq0M5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\n3/nq1botffaZ/r8txcV6hnL5cvtAfex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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "MorseEncoder finished. 1950 datapoints created\n" ] } ], "source": [ "import MorseEncoder\n", "print ('MorseEncoder started')\n", "%matplotlib inline\n", "from matplotlib.pyplot import plot,show,figure,legend, title\n", "from numpy.random import normal\n", "WPM= 40\n", "Tq = .8 # QSB cycle time in seconds (typically 5..10 secs)\n", "sigma = 0.01 # add some Gaussian noise\n", "P = MorseEncoder.signal('QUICK',WPM,Tq,sigma)\n", "figure(figsize=(12,3))\n", "lb1,=plot(P.t,P.sig,'b',label=\"sig\")\n", "show()\n", "print ('MorseEncoder finished. %d datapoints created' % len(P.sig)) " ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = P[[\"sig\",\"dah\",\"dit\",\"ele\",\"chr\",\"wrd\"]]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'\\nimport pandas as pd \\nfrom random import random\\n\\nflow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000 \\npdata = pd.DataFrame({\"a\":flow, \"b\":flow}) \\npdata.b = pdata.b.shift(9) \\ndata = pdata.iloc[10:] * random() # some noise \\ndata.shape\\n'" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''\n", "import pandas as pd \n", "from random import random\n", "\n", "flow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000 \n", "pdata = pd.DataFrame({\"a\":flow, \"b\":flow}) \n", "pdata.b = pdata.b.shift(9) \n", "data = pdata.iloc[10:] * random() # some noise \n", "data.shape\n", "'''" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "import numpy as np\n", "\n", "def _load_data(data, n_prev = 100): \n", " \"\"\"\n", " data should be pd.DataFrame()\n", " \"\"\"\n", "\n", " docX, docY = [], []\n", " for i in range(len(data)-n_prev):\n", " docX.append(data.iloc[i:i+n_prev].as_matrix())\n", " docY.append(data.iloc[i+n_prev].as_matrix())\n", " alsX = np.array(docX)\n", " alsY = np.array(docY)\n", "\n", " return alsX, alsY\n", "\n", "def train_test_split(df, test_size=0.5): \n", " \"\"\"\n", " This just splits data to training and testing parts\n", " \"\"\"\n", " ntrn = round(len(df) * (1 - test_size))\n", "\n", " X_train, y_train = _load_data(df.iloc[0:ntrn])\n", " X_test, y_test = _load_data(df.iloc[ntrn:])\n", "\n", " return (X_train, y_train), (X_test, y_test)\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "from keras.models import Sequential \n", "from keras.layers.core import Dense, Activation \n", "from keras.layers.recurrent import LSTM\n", "\n", "in_neurons = 6 \n", "out_neurons = 6 \n", "hidden_neurons = 100\n", "\n", "model = Sequential() \n", "model.add(LSTM(output_dim=hidden_neurons, input_dim=in_neurons, return_sequences=False)) \n", "model.add(Dense(output_dim=out_neurons, input_dim=hidden_neurons))\n", "model.add(Activation(\"linear\")) \n", "model.compile(loss=\"mean_squared_error\", optimizer=\"rmsprop\") \n", "\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "((875, 100, 6), (875, 6))\n" ] } ], "source": [ "(X_train, y_train), (X_test, y_test) = train_test_split(data) # retrieve data\n", "print (X_train.shape,y_train.shape)\n" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 831 samples, validate on 44 samples\n", "Epoch 1/10\n", "831/831 [==============================] - 24s - loss: 0.1325 - val_loss: 0.1279\n", "Epoch 2/10\n", "831/831 [==============================] - 24s - loss: 0.0736 - val_loss: 0.0982\n", "Epoch 3/10\n", "831/831 [==============================] - 24s - loss: 0.0548 - val_loss: 0.0839\n", "Epoch 4/10\n", "831/831 [==============================] - 23s - loss: 0.0448 - val_loss: 0.0732\n", "Epoch 5/10\n", "831/831 [==============================] - 24s - loss: 0.0382 - val_loss: 0.0652\n", "Epoch 6/10\n", "831/831 [==============================] - 23s - loss: 0.0335 - val_loss: 0.0622\n", "Epoch 7/10\n", "831/831 [==============================] - 23s - loss: 0.0309 - val_loss: 0.0598\n", "Epoch 8/10\n", "831/831 [==============================] - 24s - loss: 0.0289 - val_loss: 0.0571\n", "Epoch 9/10\n", "831/831 [==============================] - 23s - loss: 0.0270 - val_loss: 0.0545\n", "Epoch 10/10\n", "831/831 [==============================] - 23s - loss: 0.0257 - val_loss: 0.0530\n", "{'loss': [0.13252897972108518, 0.073648751894181658, 0.054770582999628094, 0.044789705260211282, 0.038179486947082883, 0.033490532395742206, 0.030936796153169611, 0.028887380172549798, 0.027028499418472247, 0.025724117228238891], 'val_loss': [0.12791160913568139, 0.098178429422459731, 0.083934664290291297, 0.073158245365286548, 0.065240221661190312, 0.062177903025774001, 0.059799854142075108, 0.057129044045861138, 0.054535108525005205, 0.05298025743265293], 'batch': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'size': [418, 418, 418, 418, 418, 418, 418, 418, 418, 418]}\n" ] } ], "source": [ "hist = model.fit(X_train, y_train, batch_size=450, nb_epoch=10, validation_split=0.05) \n", "print (hist.history)\n", "predicted = model.predict(X_test) \n", "rmse = np.sqrt(((predicted - y_test) ** 2).mean(axis=0))\n", "\n", "# and maybe plot it\n", "#pd.DataFrame(predicted[:100]).to_csv(\"predicted.csv\") \n", "#pd.DataFrame(y_test[:100]).to_csv(\"test_data.csv\") \n", "%matplotlib inline\n", "#from matplotlib.pyplot import plot\n", "#plot(predicted[:100])\n", "#plot(y_test[:100])" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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adgO1iBByJqaB66+3CL0kE3hM/gTugw/OVo6KJAsgaqKxACxbQSi22HfoOkKkqQkOPRRO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\ncijrKBzr9YNuB52IlqgsAMv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ARCKRaFCSAkgkEokGJSmARCKRaFCSAkgkEokGJSmARCKR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\nokH5/7KMhorCxJUNAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib.pyplot import plot\n", "predicted = model.predict(X_test)\n", "plot(y_test)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FEEQMIJZmZB32Dp8PeKuwBvRgihT19ZFdfzlQF1BGou9IrVVYY87lqArBFP4wnQDodQEF\nGgOIpgsoEAHwtDaCO1aLNSBLKFLEjAXgwQXkTqDnOdYmHIrBhfkEQKcLKNAYQLRdQCEXgABnppEi\nFgSgubOZtATfxSGxaAEoAVD4w3QCoNcFNFjqAPwJgEVY0NAC6osUCWJBANps3uMsLpQFoBiImE4A\ndGcBDZIYgD8BEEIELIaRINILogcSaPd3jiE4CyBaFqcqBFP4w3wCYCQLyOAN6XTK7ozJ3huNhpVw\nCACY2w1UV2f+ILCecxxniVMWgGLAYToB0J0FFEAMoK1NtuWN9ILwLsIlAHGWOFNmAmlabAiAv+6z\nIK+3WEwDVQKg8IXpBMBQFpDBGVk0b0YIowVg0kyglha5EE4kXSBms7KUACjMjPkEQG8WUAAxgNbW\n6PljwXwPp3AT6QAwmE9koy0AKgag8IXpBMBQFpDBh56yACJLpAPAYD6RVYVgCjNjOgEIZy8gMwiA\nEf+0pmm029v9Lo5jZgsgkv5/CFwAkuN8ZwbEqgWgBEDhC/MJQBh7AcWaC8jmtBFnicNq8d0e26wW\nQDQEINB20APRAlACoPCH6QQgnL2AYk0A9LgmoCtF0YQWwMGD0bEAzJIG6nTKzLNYqjxXDC5MJwA2\npy1sMYC2tujVAED4BMCsDeEaGiArK7LHDGcMwOg5bm+XFonV//pGYUEJgMIf5hOAMPYCGrACYFIX\n0IASAItxF1A0iw5BCYDCP6YTAD0LwkNgMYABKwAmDQIfPDiABEAYF9lYu94Ugw/TCYBeF1AgMYBY\nuyHb7e261qBVFkAPZrIAYu16Uww+zCcAYewF5GoFES0GmwUQDQEItBtocryfNNAALID29ti63hSD\nD9MJgF4XUCzGAIymKBrKAlIWAKAsAHeUACj8YToBMJQFNMB9skaCwGbNAsrOjuwxw9lwT1kAioGG\n+QQgjL2AYi0rQ7mAjGOmNNBYm3AoBh+mEwBDWUAD3CSP5TRQTYPGRsjMjOxxlQuoByUACn+YTgB0\nZwHFYAzA6A3ZYe/w2wcIzGkBNDfLcx0XF9njBtIKIlxpoMoFpDA75hOAMPYCijUB6HR06i6KM5sF\nEI0aADDeCsLmkP+QOItvpVIWgGIgYjoB6HTqzAIaBHUAdqddlzVkxl5A0fD/g7Q4HA7pgtJDu73d\nbwooKAtAMTAxnQDYHKoXkAu70+53Zgrm7AUULQEQwlgffD3ttkFZAIqBifkEwKm/F9BAdwG52kH7\nw4wuoGgJABg7z3rdbIGkgcba9aYYfJhOAHQXggUQ+Iy1G9LutIftXISbWBEAI+c4kG6gygWkMDOm\nEwC9LqBAZ2SxdEPqdgEpC6AXRtpBGLKyBviEQzH4CFoAhBCnCCE2CSEqhBC3e9mmVAixWgixXghR\n5mt/eh96cZa4AV+YYyQGoCyAHoxaALrPsQoCKwYYQWVpCyGswOPAAmA38I0QYrGmaeVu22QBfwFO\n1jRtlxDC5zLhDs0RNgGItUpgmyO2YwDDh0fn2IZdQDrrTlQMQDHQCNYCmANUapq2Q9M0G/AKcHaf\nbS4G/qNp2i4ATdPqfO1QWQA9GEkDNVsWULTqAMDYedYtsoMg5qQYfAQrAEVAtdvrXV3vuTMOyBFC\nfCKEWCmEuNTXDu1Ou99F0GHwCIByARknLEHgACyAaLuArFa5LrHTWLmMYhARbKG+nnKbeGAmcCKQ\nAnwthFimaVqF+0aLFi0CYMeqHaxNXctxI4/zuVOjWRmuB0K8/3s9bKggcGQw0g7CiMUZaxaAED3X\nXKL/UgdFDFBWVkZZWVnI9hesAOwGit1eFyOtAHeqgTpN09qANiHEZ8A0wKMALH16KXOOnuP3wEZv\nyGjfjBDGOgBlAfTCSDsII+c41mJOoARgoFFaWkppaWn363vvvTeo/QXrAloJjBNClAghEoALgMV9\ntnkTOEYIYRVCpABzgY3edmh32rGK0LuAYlEADNUBmNACiHQnUBdmCgJH0wUEKg6g8E1QFoCmaXYh\nxPXAe4AVeEbTtHIhxDVdnz+ladomIcS7wFrACTytaZpXAQhXFlCsCkC4ctTDTVNTbAjAQA4CgxIA\nhW+Cbtarado7wDt93nuqz+s/AX/Ss79wZQHF4s2o1z1htiUhNQ0OHYL09OgcXwWBe1ACoPCF6SqB\nw5UFFIvmeKw2g+vokAHIaPmdw1YIpiwAxQDDdALgcOpzARldBzcWb0ZD/mkTuYCiOfuHMLaCCMAC\niLVrTjG4MJ0AhMsFZKabUW+v+nC2KQgnTU2QkRG944cjCBxoGmi0rU4jrbEVgw9TCoDeLCAjDz0z\nWAAWi/xy6By2oVYQJrMAYkUAjASBY63wEJQFoPCN6QRgIGcBQfhaFZvNAoimC8gMQWC7XQp9NAsP\nQQmAwjemE4CBnAUE4QlQmm1JyFhzAcWJ0AeBXS5HIXT/SlhQAqDwhSkFIBxZQGZIyYMwZagYDIiH\nGzMEgXW7gMIUBDZDzAmUACh8YzoB0JsFZFQAOjrMUQ5vyD9tpBWEyVxA0bYA9GYBhWvVNTMEgEEJ\ngMI3phOAcOW+d3bKmWG0CVubAhO5gAZkEDgAC0AJgMLsmFIAwpEFFIsWQCyngcZUEDgMaaCxeL0p\nBh+mE4BwZQHF4g0Zq72AYskCGAwWp9084SGFyTCVADg1J07NiUX4H5ZRATDTDRmWRmXKAugmLHEW\ngy6gzs7Ym3AoBh+mEgCH04FVWBE6cucGiwWgJ0BptiUho20BGGkFEa4gcEdH7E04FIMPcwmATvcP\nDB4BiEUXkBmygKIdBI5Fi1Mx+DCVAOh94IHx3PdYvCFjNQ002nUAZugGqlxAiljAdAKgpwgMBkdW\nRjhXqwonsWQBhOscKxeQIhYwlQDoLQID5QJyx2xrAseSBWBo0R2DFoASAIXZMZUAGHEBDYYsIEMx\nAGUBdGOkFYSRILDR6y3WJhyKwYfpBEBPERgMDgtAb4DSTM3gor0cJBhrBRGuNFDlAlLEAqYSAJUF\n1JtwzU7Dic0mO2BGsw2yGdxssWhxKgYfphIAI0HgWK7MDEs7aJO4gFpaIDU1umMIVysI5QJSDDRM\nJwDKAughXGsjhJPWVkhJie4YwuFmi7fGG77eYm3CoRh8mEoAjGYBDeRmcJqm4dAcuhvjmUUAYtIC\nMLDqmqZzQedYtDgVgw9TCUA4g8CxdkO6Zv/haIsRTsxgARhtBaFn0iGEMFRwp1xAiljAdAKgXECS\ncJ6LcBJrFoDeLCAwdp6VC0gRC5hKAFQWUA9GBcDmMMddbgYLIBxBYDB2zcWaxakYnJhKAIy2ghjI\nLiAjM1OjAcpwEnMWgM4gMBgTWuUCUsQCphIAI0Fgo83gzGIB6PVPh2tmGm5i0gLQEQQGY0KrXECK\nWCBoARBCnCKE2CSEqBBC3O5ju9lCCLsQ4jxv2xh1e8RiMzgjAhCrMYBoC4DRVhDhOM+xZnEqBidB\nCYAQwgo8DpwCTAYuEkJM8rLdH4B3Aa9pLeHKAtI089yQiYn6BMCoa8IsAtDaag4XUKhbQYBxATDD\nhEMJgMIXwVoAc4BKTdN2aJpmA14Bzvaw3Q3A60Ctr52FKwhss0FcHFhM4PAy5ALS6ZowkwCYwQJQ\nQeAejIihYvAR7COxCKh2e72r671uhBBFSFF4oustr5U04TLHzeL+gYHvAjKLBRCuIHCsxQASE5UF\noPCOvivfO3rKIh8G7tA0TROyqsmjC2jRokVsrttM5d5KyorKKC0t9blT98pMf8VSZpmNwcAXgJYW\nyMmJ7hjCFQSOs8Rhc8ZWFlBCghQjxcCgrKyMsrKykO0vWAHYDRS7vS5GWgHuHAG80vWQzgNOFULY\nNE1b7L7RokWL+G/5f2lf2+734Q+yMtMiLNJtJHz/GbFoAYTLNx1uYs0CMCK08Rb9WUBmmXTojTkp\nYoPS0tJez8d77703qP0FKwArgXFCiBJgD3ABcJH7BpqmjXb9LIR4DljS9+HvwsjNCD2ZQP5+xyw3\nI+ifkRn1TeudmYYbM8QAjLSCGOiVwMoCUPgiKAHQNM0uhLgeeA+wAs9omlYuhLim6/OnjOzPSBYQ\n9NyQifie3tvt0e1P7044XEBGZqbhxkwWgKbJtQl8Ec4gsBmsTmUBKHwRrAWApmnvAO/0ec/jg1/T\ntMt97ctIFhDovyHtdpkFZAYGQwwg2haAxSK/HA7///dwBYHNYnUqC0DhCxMkRvYQiAso1gRA1QFE\nBiM9l8KRbmsWF5CyABS+MJ0AGHUB6fF9u+oAzICqA4gM0W66pywARSxgKgEw0gsIYtMCGOguIDP0\nAoLoN90zS9zJSEBcMfgwlQAYdQHFW+J1zcjMcjNCeATAIiw4NSdOzRnk6ILHDN1AQd951jQtbEJr\nFqszMVFZAArvmE4A9LaDBjkj0+MCikULwOa06c5OEUIYbo4XLmLJAnBoDizCgkXouw2MCIBZJh3K\nAlD4wlQCoHcNXBd60x/NJgB66wDC4Q4LN7FkARhJAQXjAmCGay4uDpxOmRGlUPTFVAIQyENPrwvI\nDDcjhMcFBOYoBnM45N+WlBTVYQD6sl/CKbJmueaEUFaAwjumEgCjQeBYdAHpTcuLRQvA5f7RsY59\n2NFjaRlJtQVjImuWGACoOIDCO6YSAMMxgBh1AemtAzDinjBDNbBZ/P+g3wLQm2oL+s+x0ym/rPov\n5bCiLACFN0wlAIFUAisXkMQMFkBbGyQnR3UI3eiyAAykgIL+c+yqQDaDJQSqGEzhHVMJgNFCsFh0\nAQ1kAWhvN4f/HwxYAGEIApvpegNVDKbwjqkEwHAMwEAdgFluyHC0gwZzCEBHh3kEQM9DbzC0HgFl\nASi8YyoBCKQOINZuSJdf2F9anlH/tBkEINYsgECCwHrOsZkCwKAsAIV3TCUAAcUAYswFBNGdnYYT\nMwmA3nNsVGRjzeIEZQEovGMqATAcA4hBFxDoL1JSAhA4uiyAMLnZzFIF7EJZAApvmEoAAqkDiDUX\nEITPPaG3U2W4MJMA6LYAwpBqG4vXm2JwYioBCKQOIFZdQNFsUxAuzCQA0awENuP1piwAhSdMJQCD\noQ4AwuMCMtKqOFyYSQDCVQkci0FgZQEovGEqAQgoBjCALQAVAwiccFQCKwtAMdAwlQAYtQBiNQag\nRwBsTltMpoGaYSF0CF+mld4Jh5mCwMoCUHjDVAIQUAwgRl1AKg00vEQ7CyjWrjfF4MRUAhDIkpCx\n6ALS051RCUBwhCULSKfFqWIAiljBVAIQSC+gWJyRJSXJh6UvlAAERzQrgc12vSkLQOENUwmA4RhA\nDK4JDPoEwMiSkKAEoC/hqgSOxUIwPdebYnBiKgEYDGsCQ/gsgGivCGamZnCqDqAHJQAKb5hKAAKJ\nAQzUG1K5gIIjXOsBxOKEIzlZrtWgUPTFVAIwWHoBJSWFvkjJDCuCmUkAorkegNmCwEoAFN4wlQAE\nUgcQizMyvRZALNYBmEUAwlEJHKu9gJQAKLwRtAAIIU4RQmwSQlQIIW738PkPhRDfCSHWCiG+FEJM\n9bYvozGAWG0FoVxA4SdcFsBATTqIFE7NyRPfPMGmuk3RHoqCIAVACGEFHgdOASYDFwkhJvXZbBtw\nnKZpU4HfAn/ztr9AVgSLRZM8bEFg1Q20m3AU28WqxWkmC+DNTW9y3dLruPzNy6M9FAXBWwBzgEpN\n03ZommYDXgHOdt9A07SvNU1r7Hq5HBjubWeDYU1gCE8aaII1IepZQGYSgHBUAidYE2LS4jSTALy2\n8TWeOP0Jth3cxo6GHdEezqAnWAEoAqrdXu/qes8bPwGWevswoDqAASoAhmenlng6HdEt9zSTAISj\nDkDvOTabxWkmAfi86nNOHHUiJ446kbIdZdEezqAn2MtU07uhEOJ44ArgaE+fL1q0iF0rd/H0lqc5\ndMYhSktL/e5TpYH2oHd2Gk7M1AxOT7sNo0FgvVZWLF5vkaC+tZ5DHYcYmzOWuUVzWbF7BT+e/uNo\nDyumKCsro6ysLGT7C/Yy3Q0Uu70uRloBvegK/D4NnKJp2kFPO1q0aBGv//V1rv/+9RyWf5iug8db\nYzcNNOQWgNWYBXDwIGRn695cFx0dcuZtBvS23E6w6h+w3nNstiCwWSyADbUbmDxkMkII5hTN4aW1\nL0V7SDHP8F34AAAgAElEQVRHaWlpr8nxvffeG9T+gnUBrQTGCSFKhBAJwAXAYvcNhBAjgDeASzRN\nq/S1s8GyHoCuGIDDWDtoIzGATZsgJwdWrNC9e13YbOaxAMLlZovFCYcRAXA4wjeOjbUbmTxkMgDT\nh05nY+1G2u0mME0GMUEJgKZpduB64D1gI/CqpmnlQohrhBDXdG12N5ANPCGEWC2E8PrYCagOIAZv\nyGg+nADefFN+f/tt3bvXRWeneSwAPQ+9QILAei0AM11vegXg9dfluMPVOG7D/g1MGTJFjik+mTE5\nYyivLQ/PwRS6CLoOQNO0dzRNm6Bp2lhN0x7oeu8pTdOe6vr5Sk3TcjVNm9H1NcfbvgKpA1AxAIkR\nF9Dq1XDuufDtt7p3r4tYEwDDQWCdWWdmCwLrjQH8+9/yewhdzL3YULuBKflTul8fnn846/avC8/B\nFLowVyVwAHUAA9UFFFAQWKcLaN06OO882LZN9+510dlpHt+3SwA0H2kKAQWBY9Di1GsBLF8Op58O\na9aEZxwbanssAICpBVNZW7M2PAdT6MJUAmBz2GlrMVgHEIM3ZGJi6OsA9KYoOp3ywb9gAezY4fsB\naRQzWQBxcWC1+g4EG14QRuc5jsUg8J490NIiJwbr14d+DHWtdXTYOxiWPqz7PSUA0cdUAnDgoIOT\nFg783izRtAB27ZLZP0OHQkoK1NToPoRPNE26PmLpwReuc2y26831P7H5GPry5TBvHowZA9u3h34M\n62rWcXjB4Qghut9TAhB9TCUANoedqh39LQC7XaYt9iVW2/NGMwZQWQnjxsmfR40K3c3uOscWE11R\n/gTAaBBY7zk2WwwA5DXn61wsWyYFIJTXhDtra9YyNb93G7Ci9CI6HB3sb9kf+gMqdGGi2xWwOMAZ\nR0tL77cvvlimLa5c2ft9I3nZZroh9aaBhsM/XVkJY8fKn0eNkm6gYNlUt4mxj5dgmfxm8DsLIXos\nAMOptjHocgR5LnxdcxUVMHEiFBVBXV1oCsfa7e385tPf8M3ub1hbs5bDCw7v9bkQgqkFU1lXowLB\n0cI0AmC3gybsjBll7RWcrKiATz+Fxx+H//u/3r+TaE0csAIQrjYF27bB6NHy51DM9pZsXsJxzx3H\n3uY9WPIqgttZiElOhtZW758btgC6kg40P4ETs11v4F8M9+yBYcNk3KS4GHbuDO54+5r3Mf/5+fz1\nm7/yi/d+wep9q5la0L8R8NR85QaKJqYRgP37AYuDcWPiegnAW2/BOefA5ZdLP2W1W+chI3nZZvJN\nRzMGsH+/9P8DjBjR+3waZcnmJVy15CqWXLSE66bdiiXBXCuPp6T4sQAcxoLAQghdqcdmu97AvwDs\n3QuFhfLnYCcG1Y3VHPnMkZw69lS23biNr6q/YvW+1cwaNqvftlMLprJ2vxKAaGEaAVi0CITFzpjR\nVrZu7Xn/ww9lxkpKCnz/+/Dqqz2fJcYl0mH3/dDRNGhuNtcN6U8A7r0XOmx2rCL0RXF1dZCbK38e\nOTLwmd7X1V/zk8U/YclFS5g7fC4JlhQsiT6m21HA10Ovuhr+t9hOe5uxqboeS8uMFoCvGMDu3XJi\nEAoBqG2pZeFLC7lhzg0sKl1ESnwKf1r4Jx495VGPbTdUIDi6mEYAli0DLA7GjOqxAGw2+PxzOOEE\n+fqcc3qqWEG6gDocvgVg2TLIyJAPO7OQkiJdE548CZ2d8NcnNLA4+G61/pRYvdZQfT3k5cmfR4yA\nqirdh+imprmG818/n+fOfo7ZRbMBiCc5pgTgT38CrDZ2VxsUAB3FYGYMAqeny4mQJ375S7jllp5O\nroEKgFNzcul/L+WsCWdx85E3d79/y1G3cMPcGzz+zpT8KZTXlkd9MaPBimkE4Lf3OdHQGDfW0m0B\nrFol/dWuGesJJ8DatVBbK1/reegtXSqrXt2yz6JOXJysBfDkn168GCZMsmPR4njzTf2D1lsUV1fX\nWwB27jRWC6BpGj9844dcPv1yTh9/es/xSUEkxIYAtLTASy9BTp6d3VXGTEM911xHh7ksTpCToKam\n/u9v3gyffAJ33tnzXqAC8Njyx2jsaOR3J/5O9++kJaQxLH0YlQd8tglThAnTCMDCk2QV8JgxPRWq\nn34Kxx3Xs01SEixcKOMCoM8F9P77cNJJYRp0EKSnw6FD/d//97/hgovsWC1xbDKwap6eB1N7e29f\nb2amFMbGRp+/1ovn1jxHY0cj98y/p9f78aQg4k3QctINb0HgsjKYNg2ycuzs3G7cBeTP1bZ6NRym\nr6FtxPAmAP/8p8yyS03teW/MGHq5YfWwtmYt931+Hy+f+7Kh2BUoN1A0MY0ACKvsBFpSImelDocU\ngPnze2939tk9bqA4SxwaGg6n5xaGra2y7cFRR0GrrZXxj403TcpZWlp/AWhvl4J12ul24i1xVBhI\nqtETA/jsMzj8cPngB/nwd1kBetjfsp87PryDv53xt349m6xaMsSbywLwFgR+7z04+WRISrGxc7tx\nC8CXpVVbK4vtpk0zOtrw4k0AXn0VLryw93tjx8p0Yb2WocPp4Io3r+DBBQ8yJmeM4bEpAYgephEA\nVyfQ5GQYPhw2boQvv4Rjj+293emnw8cf98zsfM18N2yACROku+WT7Z9QcaCCZ1Y/E+a/RB+efLKr\nVsmbLzPHRkJcvKGbUI8FsGxZ//M5cqT+OMCtH9zKj6b9iBmFM/p9FqelmE4AvLmA3n0XTjkFEpLt\n7NhqPAbg6zx/8YWccJgtBuBJAKqrZUxoVp/knOxs6cJyuVr98dya50iMSwx4cZcjCo9g+e7lAf2u\nIjhMIwDunUDnzJFBupEjIT+/93Y5OTBzJnz0kXztKxC8di1M7Uo93lC7gSOHH8kXVV+E608whCcX\n0IoV8m+XNQBxpKZKl40e9MQAli+X+3dHbyB49d7VvL/1fe6ef7fHz+OcKWhx5nMB9RWAnTvlg3Dq\nVBBWG53tcdTV6d+nv2KwlSth7twABxxGMjL6X28ffwzHH++5ettlBfijqaOJX3/8ax479bFebR6M\ncOzIY1m2a5lfd64i9JhGANw7gZ53Hrz4Ilx9tedt3d1ACdYErxeOuwBUN1ZzythT2FS3CafmDPXw\nDeNPAOIscYwbh243kD8LQNPk/vs+nEaM0FcNfPuHt3P3cXeTnpju8XOLMwUtzkuaSZTwFANYsUK2\nPLBY5HkeNTKezZv179NfGuiaNTB9eoADDiPp6f0tgM8+A28rr+oVgMdXPM6C0QuYWTgz4LFlJWUx\nMW+isgKigHkEQHN0rwb2ve/JG+naaz1ve/bZsGSJfKglxnmvBl67Vvq8Aaqaqjgs/zCykrKobgyi\n+ilEeHIBffNN4ALgLwbgsiQOxK3jysVXdsdN9Bzjs52fsb1hO1fOvNLrNgnOTOxxBqLJEcBTDGD1\napjR5cGyO+2MGB5nKODpLwZgVgHw5AJavRqOOMLz9noEoLmzmUeWP8Jdx94V9PhOHnMyb215K+j9\nKIxhGgFwr3wVQgbRvDUWGz1afrZrl3cXkKbJALC7BVCcUczkIZPZWLsxXH+GbvoGgQ8ckJ05J0zo\nWQ7SkAD4cQFt3gyTJsHfV/2dZ1Y/w+LNcuXO8eNhyxbf+37o64e45chbfLamiHdm4jCZAHhyAa1Z\n0yMANqeNkcXxhgTAVwygtlammJqp5sRFXwGw2eTSoO7ZSu5/lx4BeGrlU8wfOZ9JQyYFPb7vT/4+\nr2983W+bDUVoMY0AOJwOQ6uBTZ0qZ/jeXEB790qRKCiQr6ubqhmROYJJeZPYULshVMMOmL4uoNWr\n5czRag2PC2jTJiku9W31nDDqBJ5b8xwgLYBt22T1qie21G/h6+qvuWzaZT6Pn+jMwmY1nwD0dQGt\nWdOToWN32hk1IgALwIultWWLbKgmhKyX+Puqv1PXaiDAEEb6CsDmzbLnjyv988XvXiTxvkQ210l/\n2Nixvq89h9PBYyse49ajbg3J+KYVTCMpLomPtn8Ukv0p9GEaATDa+2b4cPmQ9+YCcvn/hZApoIc6\nDjEkdQiThkwyxTqkfQWgvBwmy/Wyw+IC2rRJPpzqWuv46RE/5fOqz9nXvI/kZNkbyFsc4M9f/5mf\nzvopKfEpPo+v2ZJAOE0VyMvM7P3Qa2qSXyNGyNc2h43Ro4wJgK8YgHun1a+qv+KqJVfxxy//GODo\nQ0tfAfjuu96pqm9XvM20gmnc+oF8oE+eLK9JbxODpRVLyU/N764EDxYhBP933P9x+4e3m+oaGuiY\nRgDcYwB6yM2VKWzeXEDr1vWYt7uadlGUUYRFWJiUN4nyOnMIgHsMoLxcumigZzWwsWNlQY5TR8za\nX6fKzZt7LICRWSM5d+K5vLz2ZUAKQ7mHU1LXWscrG17hZ7N/5vf4Npsg3plJY4d5rIDs7N7rSFRU\nyAe0K1nF7rQzdlS8rmCnC1+tINwF4P2t7zN/5Hze3fpugKMPLf4EoLallvtPuJ9Ve1exeu9qMjNl\nwaC3YsS/rvyrruvCCBccdgGjs0dz9itn892+77qv5ZYWeOcdeO01Y0WLCv+YRgCMWgA5OdJv7s0F\ntGOHrGgE6f8fkSmnfZOGSAGItq+xbwzAXQBc5yItDbKyZKzDH/46VUoXkEZNcw25yblcNfMqnlj5\nBA6ng+nTPa8D++TKJzlv4nkUpBX4PX5nJyRqWRxs87ByT5TIyuovAK7FcECe58KCONraPBdJecKX\nq80lABtrN/Laxte445g72H5wO/Wt9UH8FaGhrwC4u8JAiv3wjOHcNO8mHvzqQUAGiL/9tv++th7Y\nyso9Kzl/yvkhHaNFWPjHef/gqOKjOO/f5zH+8fH85PkHGDu1ngcfhCeflJbJY4+FdinTwYxpBMBo\nDKDbAvDiAqqq6jH1tzdspzijGIAhKUOwCAs1LSFaCzFAPLmA+goA6MvSceHt4dTaKgPMT2y9laFp\nQynOLObI4iMZmjaUN8rfYObM/jd6u72dv3zzl15NvXzR2QkpWj61rTqrhyJAdjY0NPS8rqiQQW+Q\nPnqb00a8NY7Ro/W3PvDVCqKyEhz53zLlr1Moryvn+JLjOXrE0Xy287Mg/5LgyczsfS76WQCtteSl\n5HH1EVfzwdYP2Nmwk1mzZGZaX55c+SQ/nvZjkuOTQz7OBGsCd8+/m8obKvm+5WX++W4lbVdM5OwH\nHub9DxwsXQrPPw8//al395RCP6YRgKAsAA8uoKoqmY1R01zDVUuu6rYAhBDSDRTlOIC7ADQ0SHfQ\n8OHy9ftb32d0tly1ZcoUz7NzT3jLBNq2DYZN2c6La5/n/Uvf727Le+tRt/LHr/7IzJkaq1b1/p1/\nrvsn04dOZ0r+FF3HttkgjaHsa96nb7ARoK8FsGVLjwXw9KqnGZk5kvSEdCZN0r8QujeR1TQpMI9u\nu44/LPgDL57zIolxiRxTfIwpig/T0+UDs6UF9u2TP7uut4NtBznYdpC8lDzSE9P50bQf8Zdv/sL8\n+bJYzJ02WxvPf/c8P53107CO98knBa/8aS5rf/sMX1/9Gf8p/w8LXlpAdkkVZWXy/i4t1V+trPCM\naQRg8ebFTB4yWff2OTluMQAPLiCXBfCrj37FTfNuYlHpou7PzBAHcFkwIGf/ruyR2pZaHln+SHdH\nxYULZX8gPXh7OFVVgZj+PBcffjFZSVnd75814SwOdR5is/Mdmpt7agXsTjsPfPEAtx99u+6/p7MT\n0kWBqQTAZQG43AUuF1B9az13fXwX//rev7BarMyZIwvE9OAtBnDgADjz1lPTuptbjryFS6ddCsDR\nI47mq11fhepPChghZLC/pqZn9u+Khdz76b1cPv1yEuMSAfjZnJ/x7OpnmXBYCzU1cr0AF//e8G9m\nDZsVUM8fPbS1wTXXwCOPSL//uHHSbVv2ozJOGn0Ss/42i/9te4m33tKYPx+OOSawluYKiWkE4JHl\nj3D/Cffr3j43V950nlxATU1yRmpPqOWNTW9w17F39bIuJg+ZzIb90U0Fzc/vWgWN3u6fZ1c/yzkT\nzqEkqwSQLbC/+sr30oYuvGUCVVVB/dDXuGTqJb3etwgLD530EDe99wuOO76ze7b3yvpXKEwrpLSk\nVPff09kJGZah7D0kVcSpOXl29bM8vuJxWm3R6REUHy87yLqC7Vu2SBfQs6uf5dSxp3ZbN3PnyjYZ\nekiweE4DrayE1LmvcvHhF/dyZc4pmsPamrW02aLfJmPoUDn7d3f/NHU08cJ3L3BPaU9319HZozl6\nxNG8svEfLFgAb7/ds49wBH9dbNwIs2fLQO8338hJkQurxcqdx97Je5e8x++//D0X/OcH3HRXHddd\nJ3/n9dfDMqQBj2kE4OSxJ3e7PfTgsgA8uYBcs/9/b3yVM8efSW5Kbq/P5xTNYdnuZSEZd6AMGdJj\nvroLwGsbX+OHU3/YvV1mpmwutnix/316q1LdVFVHW/xujijsX/Z52rjTmDRkEofm/IqPPoKG9gbu\n/OhO7jvhPkN/T2cnFFgnsKlepo389tPf8ujyR/lw24cc/8LxulYrCwcuN1B9vcymysuDZ9c828uF\nMXOmbByoV2Q9WVk7d4Jt2KcsGL2g1/sp8SlMHjKZlXtWBv23BEthoVz71z0A/Or6Vzlh1AkMTRva\na9ufz/k5f172Zy64yMHLMlmMb3Z/Q01zDaeOPTXkY9u2DU48EW66Cf71L+my8sSMwhl8e/W3jMwc\nybQnpzFswWu88T8bd90FZ54Z3BKngxHTCMAV068wtL3LAkjw4AJyCcBbW97inInn9PvdWcNmsblu\nM4c6PDTkjxBZWXJm2tkpZz6TJslspZ2NOzlu5HG9tv3Rj+C55/zv01uO+ur6LxmffKTXIPuzZz3L\njoS3+Ff7ZZzwwglcMOWCfmPwR2cnFCUcxnf7vuO/5f/lmdXP8O4l7/LfC/5LVlIWjy5/1ND+QoXL\nDeQKAO9okFk584bP694mJUXOIvv6uz3hLe14e1UHTamrOHL4kf0+O7r4aL6qjr4baMwYaamsWtXT\nrmLxlsX8YPIP+m17wqgTyEvJo67oBcrL5QIx95Tdwy+P+qWhZA29/OpX8LOfwU9+4n/xpqS4JB46\n+SFePvdlHl7+MOd8MYzZD1xGx7THmXT+vyj96f94/IM3eXnty3y47UN2Ne2KetafWTFN01oj7gaQ\nVZ5CQJzW3wVUVQWFI1t4vfpLXvvBa/1+NzEukelDp7Ns1zIWjlkYzLADxlWlvHdvT/+Yz3Z+xvyR\n8/sFw889F3796542xt7wtkDOts6vWZB/lNffy03J5dtrl3HYir+yIP1E/rDQd9WvJ2w2GJE4CZvT\nxoX/uZAvLv+ie1b50EkPseDFBVw7+1q/BWWhxmUB7Nwp/cnvbX2Pk8eejEX0nvuceabsL3XGGb73\nlxSXRLu9/4LOq/auIj9rgsdmeUcXH81La18K6u/wRH1rPbWttexo2MHOhp3sbNxJVWMVuw/tZs+h\nPYzOHs07P3yne/vx42UTxZoa2SOrw97Bpzs+5fmzn++3byEED5/8MKf98zROvvhYbn1pBVtztnL1\nEV46NAbBd9/JtT+eMdip/fhRx/PlqC/Z0bCD97e+z6qEVaRNaaC8oo1b/ulkaHY6WcV72O+Uk71x\nueMYnzue8TnjGZc7jjHZY8hIzKCxo5HKA5VsPbC1Ox12eMZwclNyyU3OJTcll4zEDOIt8eQk5+DQ\nHDR3NnOo4xAtthYSrAmkxKd0f9kcNvY276W2pZbmzmY0NKzCitViJSc5h7yUPKzCSmNHIw3tDTS0\nN9DYLn+2OW0UpRcB0iviaR3lUBK0AAghTgEeBqzA3zVN+4OHbR4FTgVagR9rmra67zaBzCpyc8HR\n6dkFZBm+ksPzD/favfK0cafx303/jZoAgOxptGyZdD2MHAkPvPUZx444tt92ycnw9NNwySUyHjBq\nlOf9Jccl02bv72uut65nTslPfI4lKymLW+b8inUfgrhY/9+gaXLd5meegT/+0cKnF35Kp6OT8bnj\nu7c5LP8wjhlxDE+tfIqbjrxJ/85DgMsCWLdOZlR9tvMzFo7u/z8/80yZVfLEE957UAEkxyd79OdX\nNK1jXLHnVWCOKj6Ka9++Fk3TAm6ZvK95H9/s/oaVe1aycu9K1tWso7GjkYLUAkZmjaQks4TizGIW\njl5IcWYxAsGl/7201z6OPFIGWC+6SLYc+XLncibkTejnInVxxLAj+P2Jv+eGpbNoa0pmxU/eN/RA\n2r8f3nhDNm90rULniRdflONyX5XMCCVZJf2E6dAheU3+4x8gdsPPr2mk9OgK6rQtbKnfwruV77Lt\n4DaaO5tJS0hjbM5YxuaMZUr+FHY37ebD7R9S31rPgbYD1LfV09TRRKejs9trkJaQRlpCGqkJqdgc\nNlptrbTaWmmxtRBviacwvZD81HxS41MRQuDUnNiddg60HaC2pRan5iQzKZOspKzur8zETOIscSzZ\nsgSrsHLcyOPMLQBCCCvwOLAA2A18I4R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gsgDyU/OpaeldZ1jdWE12crbX1uD+GD5cNr5r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\nWFKjbUt6hwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot(predicted[:,0])\n", "plot(predicted[:,1])\n", "plot(predicted[:,2])" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "#json_string = model.to_json()\n", "#open('rnn_morse_architecture.json', 'w').write(json_string)\n", "model.save_weights('rnn_morse_weights.h5')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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adgO1iBByJqaB66+3CL0kE3hM/gTugw/OVo6KJAsgaqKxACxbQSi22HfoOkKkqQkOPRRO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\nokH5/7KMhorCxJUNAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from keras.models import model_from_json\n", "#model = model_from_json(open('rnn_morse_architecture.json').read())\n", "model.load_weights('rnn_morse2_weights.h5')\n", "predicted = model.predict(X_test)\n", "plot(y_test)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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wsHvMCfTJC4O+eCsAnYC0Ksd7ys5V5QXgRKVUOrAKuN3LNBuFW8wgcCCwfQxAE6F1eVy2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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(predicted[:,5])\n", "plot(predicted[:,1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }