├── CNN
    ├── CNN1D.ipynb
    ├── CNN1D_Optimize.ipynb
    ├── CNN1D_v1.0.json
    └── readme.md
├── CNN1D_Optimize v0.3.ipynb
├── LSTM
    └── LSTM_Autoencoder.ipynb
└── README.md


/CNN/CNN1D.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "CNN1D.ipynb",
  7 |       "version": "0.3.2",
  8 |       "provenance": [],
  9 |       "include_colab_link": true
 10 |     },
 11 |     "kernelspec": {
 12 |       "name": "python3",
 13 |       "display_name": "Python 3"
 14 |     },
 15 |     "accelerator": "GPU"
 16 |   },
 17 |   "cells": [
 18 |     {
 19 |       "cell_type": "markdown",
 20 |       "metadata": {
 21 |         "id": "view-in-github",
 22 |         "colab_type": "text"
 23 |       },
 24 |       "source": [
 25 |         "<a href=\"https://colab.research.google.com/github/SamuelXJames/Signal-Denoising-Autoencoder/blob/master/CNN1D.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 26 |       ]
 27 |     },
 28 |     {
 29 |       "metadata": {
 30 |         "id": "ontfmefnFpH9",
 31 |         "colab_type": "code",
 32 |         "outputId": "7fa6d14c-f279-4626-c873-a9e98f6f5a75",
 33 |         "colab": {
 34 |           "base_uri": "https://localhost:8080/",
 35 |           "height": 1962
 36 |         }
 37 |       },
 38 |       "cell_type": "code",
 39 |       "source": [
 40 |         "#Additional Packages to Install\n",
 41 |         "# !pip install pydrive\n",
 42 |         "# !pip install git+https://github.com/hyperopt/hyperopt.\n",
 43 |         "# !pip install kopt\n",
 44 |         "# !pip install oauth2client\n",
 45 |         "# !pip install tensorboardcolab\n",
 46 |         "\n",
 47 |         "import numpy as np\n",
 48 |         "import matplotlib.pyplot as plt\n",
 49 |         "import tensorflow as tf\n",
 50 |         "from tensorboardcolab import *\n",
 51 |         "import pydrive\n",
 52 |         "from keras.layers import Dense,Conv1D,RepeatVector,TimeDistributed,Input\n",
 53 |         "from keras.layers import Conv1D,MaxPooling1D,UpSampling1D\n",
 54 |         "from keras.layers import Activation,BatchNormalization\n",
 55 |         "from keras.models import Model,Sequential\n",
 56 |         "from keras.optimizers import RMSprop\n",
 57 |         "from sklearn.preprocessing import MinMaxScaler\n",
 58 |         "from pydrive.auth import GoogleAuth\n",
 59 |         "from pydrive.drive import GoogleDrive\n",
 60 |         "from google.colab import auth\n",
 61 |         "from oauth2client.client import GoogleCredentials\n",
 62 |         "from kopt import CompileFN, KMongoTrials, test_fn\n",
 63 |         "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
 64 |         "\n",
 65 |         "\n",
 66 |         "#Authenticate/Create PyDrive client \n",
 67 |         "# auth.authenticate_user()\n",
 68 |         "# gauth = GoogleAuth()\n",
 69 |         "# gauth.credentials = GoogleCredentials.get_application_default()\n",
 70 |         "# drive = GoogleDrive(gauth)\n",
 71 |         "\n",
 72 |         "\n",
 73 |         "\n",
 74 |         "\n",
 75 |         "tbc=TensorBoardColab()\n",
 76 |         "batch_size = 1000\n",
 77 |         "\n",
 78 |         "\n",
 79 |         "def create_data():\n",
 80 |         "  length = 1\n",
 81 |         "  dt = 0.0005\n",
 82 |         "  noise_factor = 0.35\n",
 83 |         "  data = []\n",
 84 |         "  data_noisy = []\n",
 85 |         "  for i in range(batch_size):\n",
 86 |         "      freq = 30*np.random.random() + 1\n",
 87 |         "      t = np.arange(0,length,dt)\n",
 88 |         "      #signal = np.arange(-0.5*length,0.5*length,dt)\n",
 89 |         "      signal = np.sin(2*np.pi*freq*t)\n",
 90 |         "      noisy_signal = signal+noise_factor*np.random.rand(len(t))\n",
 91 |         "      data.append(signal)\n",
 92 |         "      data_noisy.append(noisy_signal)\n",
 93 |         "  data = np.array(data)\n",
 94 |         "  data_noisy = np.array(data_noisy)\n",
 95 |         "  return t,data, data_noisy\n",
 96 |         "#Create Test/Train Data\n",
 97 |         "t,signal,signal_noisy = create_data()\n",
 98 |         "\n",
 99 |         "#Scale Data - Need a better scaler than MinMaxScaler\n",
100 |         "scaler = MinMaxScaler(feature_range = (0,1))\n",
101 |         "data  = np.vstack((signal,signal_noisy))\n",
102 |         "data = scaler.fit_transform(data)\n",
103 |         "signal = data[0:batch_size][:]\n",
104 |         "signal_noisy = data[batch_size:2*batch_size][:]\n",
105 |         "\n",
106 |         "#Prediction Data\n",
107 |         "x_pred = signal_noisy[0][:]\n",
108 |         "#Pre Autoencoder\n",
109 |         "x_pred_pre = signal_noisy[0] \n",
110 |         "x_pred_pre = x_pred_pre.reshape((-1,len(x_pred_pre)))\n",
111 |         "x_pred_pre = scaler.inverse_transform(x_pred_pre)\n",
112 |         "#Clean\n",
113 |         "clean_signal = signal[0][:]\n",
114 |         "clean_signal = clean_signal.reshape((-1,len(clean_signal)))\n",
115 |         "clean_signal = scaler.inverse_transform(clean_signal)\n",
116 |         "\n",
117 |         "#Input Shaping\n",
118 |         "features = 1\n",
119 |         "timesteps = len(signal[0])\n",
120 |         "shape = (batch_size,timesteps,features)\n",
121 |         "#Input shape should equal [samples, timesteps, features]\n",
122 |         "x = signal.reshape(shape)\n",
123 |         "y = signal_noisy.reshape(shape)\n",
124 |         "#x_pred = x_pred.reshape(1,timesteps,features)\n",
125 |         "\n",
126 |         "\n",
127 |         "#A custom loss function may work better than some \n",
128 |         "#of the loss functionss provided by Keras\n",
129 |         "#Here is a loss function from Hvass Labs that\n",
130 |         "#invokes a warmup period\n",
131 |         "warmup_steps = 10\n",
132 |         "def loss_mse_warmup(y_true, y_pred):\n",
133 |         "    \n",
134 |         "\n",
135 |         "    \n",
136 |         "    y_true_slice = y_true[:, warmup_steps:, :]\n",
137 |         "    y_pred_slice = y_pred[:, warmup_steps:, :]\n",
138 |         "\n",
139 |         "    # These sliced tensors both have this shape:\n",
140 |         "    # [batch_size, sequence_length - warmup_steps, num_y_signals]\n",
141 |         "\n",
142 |         "    # Calculate the MSE loss for each value in these tensors.\n",
143 |         "    # This outputs a 3-rank tensor of the same shape.\n",
144 |         "    loss = tf.losses.mean_squared_error(labels=y_true_slice,\n",
145 |         "                                        predictions=y_pred_slice)\n",
146 |         "\n",
147 |         "    # Keras may reduce this across the first axis (the batch)\n",
148 |         "    # but the semantics are unclear, so to be sure we use\n",
149 |         "    # the loss across the entire tensor, we reduce it to a\n",
150 |         "    # single scalar with the mean function.\n",
151 |         "    loss_mean = tf.reduce_mean(loss)\n",
152 |         "\n",
153 |         "    return loss_mean\n",
154 |         "\n",
155 |         "#For Optimization\n",
156 |         "def create_autoencoder(inputs,params):\n",
157 |         "  encoded = inputs\n",
158 |         "  encoded_filter_range = np.linspace(params['max_filters'],params['min_filters']\n",
159 |         "                                     ,params['layers'])\n",
160 |         "  \n",
161 |         "  for num_filters in encoded_filter_range: \n",
162 |         "    encoded = Conv1D(num_filters,params['kernel_size'],padding = 'same')(encoded)\n",
163 |         "  \n",
164 |         "    if(num_filters==max_filters):\n",
165 |         "      encoded = BatchNormalization()(encoded)\n",
166 |         "  \n",
167 |         "    encoded = Activation('relu')(encoded)\n",
168 |         "    encoded = MaxPooling1D(2,padding = 'same')(encoded)\n",
169 |         "  \n",
170 |         "  decoded_filter_range = np.linspace(min_filters,max_filters,layers)\n",
171 |         "  for num_filters in decoded_filter_range: \n",
172 |         "    encoded = Conv1D(num_filters,params['kernel_size'],padding = 'same')(encoded)\n",
173 |         "  \n",
174 |         "    encoded = Activation('relu')(encoded)\n",
175 |         "    encoded = UpSampling1D(2)(encoded)\n",
176 |         "    \n",
177 |         "  decoded = Conv1D(1,params['kernel_size'],activation = 'sigmoid',padding = 'same')(encoded)\n",
178 |         "  \n",
179 |         "  autoencoder = Model(inputs,decoded)\n",
180 |         "  \n",
181 |         "  return autoencoder\n",
182 |         "\n",
183 |         "\n",
184 |         "#NOT THE OPTIMIZED MODEL\n",
185 |         "def create_Conv1D():\n",
186 |         "  inputs = Input(shape = (timesteps,features))\n",
187 |         "  \n",
188 |         "  encoded = Conv1D(32,3, padding = 'same')(inputs)\n",
189 |         "  encoded = BatchNormalization()(encoded)\n",
190 |         "  encoded = Activation('relu')(encoded)\n",
191 |         "  encoded = MaxPooling1D(2,padding = 'same')(encoded)\n",
192 |         "  \n",
193 |         "  \n",
194 |         "  encoded = Conv1D(16,3,padding = 'same')(encoded)\n",
195 |         "  encoded = Activation('relu')(encoded)\n",
196 |         "  encoded = MaxPooling1D(2,padding = 'same')(encoded)\n",
197 |         "  \n",
198 |         "  encoded = Conv1D(8,3, padding = 'same')(encoded)\n",
199 |         "  encoded = Activation('relu')(encoded)\n",
200 |         "  encoded = MaxPooling1D(2,padding = 'same')(encoded)\n",
201 |         "  \n",
202 |         "  \n",
203 |         "  decoded = Conv1D(8,3,padding = 'same')(encoded)\n",
204 |         "  decoded = Activation('relu')(decoded)\n",
205 |         "  decoded = UpSampling1D(2)(decoded)\n",
206 |         "  \n",
207 |         "  decoded = Conv1D(16,3, padding = 'same')(decoded)\n",
208 |         "  decoded = Activation('relu')(decoded)\n",
209 |         "  decoded = UpSampling1D(2)(decoded)\n",
210 |         "  \n",
211 |         "  decoded = Conv1D(32,3,padding = 'same')(decoded)\n",
212 |         "  decoded = Activation('relu')(decoded)\n",
213 |         "  decoded = UpSampling1D(2)(decoded)\n",
214 |         "  \n",
215 |         "  decoded = Conv1D(1,3,activation = 'sigmoid',padding = 'same')(decoded)\n",
216 |         "  \n",
217 |         "  autoencoder = Model(inputs,decoded)\n",
218 |         "  \n",
219 |         "  return autoencoder\n",
220 |         "\n",
221 |         "\n",
222 |         "\n",
223 |         "\n",
224 |         "\n",
225 |         "def nn_predict(model,prediction_data):\n",
226 |         "  pad_length = timesteps-(np.size(prediction_data)%timesteps)\n",
227 |         "  if pad_length != timesteps:\n",
228 |         "    prediction_data = np.pad(prediction_data,[(0,0),(0,pad_length)],\n",
229 |         "                             mode = 'constant', constant_values = 0)\n",
230 |         "   \n",
231 |         "  prediction_data = prediction_data.reshape((-1,timesteps))\n",
232 |         "  \n",
233 |         "  predicted_data = []\n",
234 |         "  \n",
235 |         "  for i in range(np.size(prediction_data,0)):\n",
236 |         "    pred_part = prediction_data[i].reshape(1,timesteps,features) \n",
237 |         "    pred_part = model.predict(pred_part)\n",
238 |         "    predicted_data.append(pred_part)\n",
239 |         "  \n",
240 |         "  predicted_data = (np.array(predicted_data)).flatten()\n",
241 |         "  \n",
242 |         "  predicted_data = predicted_data.reshape(-1,len(predicted_data))\n",
243 |         "  predicted_data = scaler.inverse_transform(predicted_data)\n",
244 |         "  return predicted_data\n",
245 |         "\n",
246 |         "\n",
247 |         "\n",
248 |         "#NN HyperParameters\n",
249 |         "epochs = 1\n",
250 |         "optimizer = RMSprop(lr=1e-3)\n",
251 |         "\n",
252 |         "\n",
253 |         "#NN Fitting\n",
254 |         "autoencoder = create_Conv1D()\n",
255 |         "autoencoder.compile(optimizer = optimizer, loss = 'mse')\n",
256 |         "autoencoder.summary()\n",
257 |         "\n",
258 |         "autoencoder.fit(y,x,epochs=epochs, verbose = 0,\n",
259 |         "                callbacks=[TensorBoardColabCallback(tbc)])\n",
260 |         "\n",
261 |         "\n",
262 |         "\n",
263 |         "#Prediction Data\n",
264 |         "prediction = nn_predict(autoencoder,x_pred).flatten()\n",
265 |         "\n",
266 |         "         \n",
267 |         "#Plot Prediction                                   \n",
268 |         "fig = plt.figure()\n",
269 |         "ax = plt.subplot(111)\n",
270 |         "ax.plot(t,prediction,label = 'Denoised Signal')\n",
271 |         "ax.plot(t,clean_signal.flatten(), label = 'Clean Signal')\n",
272 |         "ax.plot(t,x_pred_pre.flatten(), label = 'Noisy Signal')\n",
273 |         "plt.xlim(0,0.25)\n",
274 |         "ax.legend(loc = 3)\n",
275 |         "  \n",
276 |         "  \n",
277 |         "#Save Model to Google Drive\n",
278 |         "# autoencoder.save('model.h5', include_optimizer=False)\n",
279 |         "# model_file = drive.CreateFile({'title' : 'CNN1D_v1.0.h5'})\n",
280 |         "# model_file.SetContentFile('model.h5')\n",
281 |         "# model_file.Upload()\n",
282 |         "# drive.CreateFile({'id': model_file.get('id')})\n",
283 |         "  \n",
284 |         "  \n",
285 |         "  \n",
286 |         "  \n",
287 |         "  \n",
288 |         "  "
289 |       ],
290 |       "execution_count": 2,
291 |       "outputs": [
292 |         {
293 |           "output_type": "stream",
294 |           "text": [
295 |             "Requirement already satisfied: pydrive in /usr/local/lib/python3.6/dist-packages (1.3.1)\n",
296 |             "Requirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.6/dist-packages (from pydrive) (1.6.7)\n",
297 |             "Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pydrive) (4.1.3)\n",
298 |             "Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.6/dist-packages (from pydrive) (3.13)\n",
299 |             "Requirement already satisfied: httplib2<1dev,>=0.9.2 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive) (0.11.3)\n",
300 |             "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive) (3.0.0)\n",
301 |             "Requirement already satisfied: six<2dev,>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->pydrive) (1.11.0)\n",
302 |             "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive) (0.2.2)\n",
303 |             "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive) (4.0)\n",
304 |             "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->pydrive) (0.4.4)\n",
305 |             "Collecting git+https://github.com/hyperopt/hyperopt.\n",
306 |             "  Cloning https://github.com/hyperopt/hyperopt. to /tmp/pip-req-build-4t8air2a\n",
307 |             "fatal: could not read Username for 'https://github.com': No such device or address\n",
308 |             "\u001b[31mCommand \"git clone -q https://github.com/hyperopt/hyperopt. /tmp/pip-req-build-4t8air2a\" failed with error code 128 in None\u001b[0m\n",
309 |             "Collecting kopt\n",
310 |             "  Downloading https://files.pythonhosted.org/packages/a7/e2/650da402870396bcf6660e98e55499f6ec7d05c8443f3bfb2c0e64dda172/kopt-0.1.0.tar.gz\n",
311 |             "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from kopt) (0.16.0)\n",
312 |             "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from kopt) (3.13)\n",
313 |             "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from kopt) (1.14.6)\n",
314 |             "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from kopt) (0.22.0)\n",
315 |             "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from kopt) (1.1.0)\n",
316 |             "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.6/dist-packages (from kopt) (0.20.1)\n",
317 |             "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from kopt) (2.1.2)\n",
318 |             "Requirement already satisfied: keras>=2.0.4 in /usr/local/lib/python3.6/dist-packages (from kopt) (2.2.4)\n",
319 |             "Requirement already satisfied: hyperopt in /usr/local/lib/python3.6/dist-packages (from kopt) (0.1.1)\n",
320 |             "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->kopt) (2018.7)\n",
321 |             "Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas->kopt) (2.5.3)\n",
322 |             "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->kopt) (0.10.0)\n",
323 |             "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->kopt) (1.11.0)\n",
324 |             "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->kopt) (2.3.0)\n",
325 |             "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras>=2.0.4->kopt) (2.8.0)\n",
326 |             "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras>=2.0.4->kopt) (1.0.5)\n",
327 |             "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras>=2.0.4->kopt) (1.0.6)\n",
328 |             "Requirement already satisfied: pymongo in /usr/local/lib/python3.6/dist-packages (from hyperopt->kopt) (3.7.2)\n",
329 |             "Requirement already satisfied: networkx in /usr/local/lib/python3.6/dist-packages (from hyperopt->kopt) (2.2)\n",
330 |             "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx->hyperopt->kopt) (4.3.0)\n",
331 |             "Building wheels for collected packages: kopt\n",
332 |             "  Running setup.py bdist_wheel for kopt ... \u001b[?25l-\b \bdone\n",
333 |             "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/21/07/fb/61d6d91da2defece9f6f91aa94f4370fc920324e28eb9010a9\n",
334 |             "Successfully built kopt\n",
335 |             "Installing collected packages: kopt\n",
336 |             "Successfully installed kopt-0.1.0\n",
337 |             "Wait for 8 seconds...\n",
338 |             "TensorBoard link:\n",
339 |             "http://a5f902a5.ngrok.io\n",
340 |             "_________________________________________________________________\n",
341 |             "Layer (type)                 Output Shape              Param #   \n",
342 |             "=================================================================\n",
343 |             "input_1 (InputLayer)         (None, 2000, 1)           0         \n",
344 |             "_________________________________________________________________\n",
345 |             "conv1d_1 (Conv1D)            (None, 2000, 32)          128       \n",
346 |             "_________________________________________________________________\n",
347 |             "batch_normalization_1 (Batch (None, 2000, 32)          128       \n",
348 |             "_________________________________________________________________\n",
349 |             "activation_1 (Activation)    (None, 2000, 32)          0         \n",
350 |             "_________________________________________________________________\n",
351 |             "max_pooling1d_1 (MaxPooling1 (None, 1000, 32)          0         \n",
352 |             "_________________________________________________________________\n",
353 |             "conv1d_2 (Conv1D)            (None, 1000, 16)          1552      \n",
354 |             "_________________________________________________________________\n",
355 |             "activation_2 (Activation)    (None, 1000, 16)          0         \n",
356 |             "_________________________________________________________________\n",
357 |             "max_pooling1d_2 (MaxPooling1 (None, 500, 16)           0         \n",
358 |             "_________________________________________________________________\n",
359 |             "conv1d_3 (Conv1D)            (None, 500, 8)            392       \n",
360 |             "_________________________________________________________________\n",
361 |             "activation_3 (Activation)    (None, 500, 8)            0         \n",
362 |             "_________________________________________________________________\n",
363 |             "max_pooling1d_3 (MaxPooling1 (None, 250, 8)            0         \n",
364 |             "_________________________________________________________________\n",
365 |             "conv1d_4 (Conv1D)            (None, 250, 8)            200       \n",
366 |             "_________________________________________________________________\n",
367 |             "activation_4 (Activation)    (None, 250, 8)            0         \n",
368 |             "_________________________________________________________________\n",
369 |             "up_sampling1d_1 (UpSampling1 (None, 500, 8)            0         \n",
370 |             "_________________________________________________________________\n",
371 |             "conv1d_5 (Conv1D)            (None, 500, 16)           400       \n",
372 |             "_________________________________________________________________\n",
373 |             "activation_5 (Activation)    (None, 500, 16)           0         \n",
374 |             "_________________________________________________________________\n",
375 |             "up_sampling1d_2 (UpSampling1 (None, 1000, 16)          0         \n",
376 |             "_________________________________________________________________\n",
377 |             "conv1d_6 (Conv1D)            (None, 1000, 32)          1568      \n",
378 |             "_________________________________________________________________\n",
379 |             "activation_6 (Activation)    (None, 1000, 32)          0         \n",
380 |             "_________________________________________________________________\n",
381 |             "up_sampling1d_3 (UpSampling1 (None, 2000, 32)          0         \n",
382 |             "_________________________________________________________________\n",
383 |             "conv1d_7 (Conv1D)            (None, 2000, 1)           97        \n",
384 |             "=================================================================\n",
385 |             "Total params: 4,465\n",
386 |             "Trainable params: 4,401\n",
387 |             "Non-trainable params: 64\n",
388 |             "_________________________________________________________________\n"
389 |           ],
390 |           "name": "stdout"
391 |         },
392 |         {
393 |           "output_type": "execute_result",
394 |           "data": {
395 |             "text/plain": [
396 |               "<matplotlib.legend.Legend at 0x7ff697e37ba8>"
397 |             ]
398 |           },
399 |           "metadata": {
400 |             "tags": []
401 |           },
402 |           "execution_count": 2
403 |         },
404 |         {
405 |           "output_type": "display_data",
406 |           "data": {
407 |             "image/png": 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a3RcVnR+XTWHXEvZku+LWrILyJTXi7PD4/IlpXIYOsoyrpgY9kbS1Xc3va89M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\nF5OIOPWylQcAAAAASUVORK5CYII=\n",
408 |             "text/plain": [
409 |               "<matplotlib.figure.Figure at 0x7ff706097e80>"
410 |             ]
411 |           },
412 |           "metadata": {
413 |             "tags": []
414 |           }
415 |         }
416 |       ]
417 |     }
418 |   ]
419 | }
420 | 


--------------------------------------------------------------------------------
/CNN/CNN1D_Optimize.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Copy of CNN1D Optimize.ipynb",
  7 |       "version": "0.3.2",
  8 |       "provenance": [],
  9 |       "private_outputs": true,
 10 |       "collapsed_sections": [],
 11 |       "include_colab_link": true
 12 |     },
 13 |     "kernelspec": {
 14 |       "name": "python3",
 15 |       "display_name": "Python 3"
 16 |     },
 17 |     "accelerator": "GPU"
 18 |   },
 19 |   "cells": [
 20 |     {
 21 |       "cell_type": "markdown",
 22 |       "metadata": {
 23 |         "id": "view-in-github",
 24 |         "colab_type": "text"
 25 |       },
 26 |       "source": [
 27 |         "<a href=\"https://colab.research.google.com/github/SamuelXJames/Signal-Denoising-Autoencoder/blob/master/CNN1D_Optimize.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 28 |       ]
 29 |     },
 30 |     {
 31 |       "metadata": {
 32 |         "id": "ontfmefnFpH9",
 33 |         "colab_type": "code",
 34 |         "colab": {}
 35 |       },
 36 |       "cell_type": "code",
 37 |       "source": [
 38 |         "#Additional Packages to Install\n",
 39 |         "# !pip install pydrive\n",
 40 |         "# !pip install git+https://github.com/hyperopt/hyperopt.git\n",
 41 |         "# !pip install kopt\n",
 42 |         "# !pip install oauth2client\n",
 43 |         "# !pip install tensorboardcolab\n",
 44 |         "# !pip install pymongo\n",
 45 |         "# !pip install git+https://github.com/rthalley/dnspython.git\n",
 46 |         "# !pip install dnspython\n",
 47 |         "\n",
 48 |         "import numpy as np\n",
 49 |         "import pandas as pd\n",
 50 |         "import matplotlib.pyplot as plt\n",
 51 |         "import tensorflow as tf\n",
 52 |         "import pydrive\n",
 53 |         "import pymongo\n",
 54 |         "import dns\n",
 55 |         "from tensorboardcolab import *\n",
 56 |         "from random import randint\n",
 57 |         "from keras.layers import Dense,Conv1D,Input\n",
 58 |         "from keras.layers import MaxPooling1D,UpSampling1D\n",
 59 |         "from keras.layers import Activation,BatchNormalization\n",
 60 |         "from keras.models import Model\n",
 61 |         "from keras.optimizers import RMSprop\n",
 62 |         "from sklearn.preprocessing import MinMaxScaler\n",
 63 |         "from pydrive.auth import GoogleAuth\n",
 64 |         "from pydrive.drive import GoogleDrive\n",
 65 |         "from google.colab import auth\n",
 66 |         "from oauth2client.client import GoogleCredentials\n",
 67 |         "from kopt import CompileFN, KMongoTrials, test_fn\n",
 68 |         "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
 69 |         "from hyperopt.mongoexp import MongoTrials\n",
 70 |         "from google.colab import drive\n",
 71 |         "\n",
 72 |         "\n",
 73 |         "\n",
 74 |         "\n",
 75 |         "\n",
 76 |         "\n",
 77 |         "#MongoTrials\n",
 78 |         "SRV_string  = ''\n",
 79 |         "client = pymongo.MongoClient(SRV_string)\n",
 80 |         "client.test_database\n",
 81 |         "print(client)\n",
 82 |         "hostname = ''\n",
 83 |         "port = 27017\n",
 84 |         "\n",
 85 |         "\n",
 86 |         "\n",
 87 |         "\n",
 88 |         "\n",
 89 |         "# #Authenticate/Create PyDrive client \n",
 90 |         "# auth.authenticate_user()\n",
 91 |         "# gauth = GoogleAuth()\n",
 92 |         "# gauth.credentials = GoogleCredentials.get_application_default()\n",
 93 |         "# drive = GoogleDrive(gauth)\n",
 94 |         "\n",
 95 |         "#Altrnative Saving Method\n",
 96 |         "drive.mount('/content/gdrive')\n",
 97 |         "\n",
 98 |         "  \n",
 99 |         "#tbc=TensorBoardColab()\n",
100 |         "batch_size = 1000\n",
101 |         "validation_split = 0.15\n",
102 |         "timesteps = 2000\n",
103 |         "features = 1\n",
104 |         "global timesteps\n",
105 |         "global fe#atures\n",
106 |         "\n",
107 |         "\n",
108 |         "#Generate Test/Train Data\n",
109 |         "def create_data(timesteps = 2000):\n",
110 |         "  length = 1\n",
111 |         "  dt = 0.0005\n",
112 |         "  noise_factor = 0.35\n",
113 |         "  data_1 = []\n",
114 |         "  data_noisy_1 = []\n",
115 |         "  for i in range(batch_size):\n",
116 |         "      freq = 30*np.random.random() + 1\n",
117 |         "      t = np.arange(0,length,dt)\n",
118 |         "      #signal = np.arange(-0.5*length,0.5*length,dt)\n",
119 |         "      signal = np.sin(2*np.pi*freq*t)\n",
120 |         "      noisy_signal = signal+noise_factor*np.random.rand(len(t))\n",
121 |         "      data_1.append(signal)\n",
122 |         "      data_noisy_1.append(noisy_signal)\n",
123 |         "  data_1 = np.array(data_1)\n",
124 |         "  data_noisy_1 = np.array(data_noisy_1)\n",
125 |         "  \n",
126 |         "  #Scale Data - Need a better scaler than MinMaxScaler\n",
127 |         "  scaler = MinMaxScaler(feature_range = (0,1))\n",
128 |         "  data_1  = np.vstack((data_1,data_noisy_1))\n",
129 |         "  data_1 = scaler.fit_transform(data_1)\n",
130 |         "  signal = data_1[0:batch_size][:]\n",
131 |         "  signal_noisy = data_1[batch_size:2*batch_size][:]\n",
132 |         "  \n",
133 |         "  #Input Shaping\n",
134 |         "  features = 1\n",
135 |         "  timesteps = len(signal[0])\n",
136 |         "  shape = (batch_size,timesteps,features)\n",
137 |         "  #Input shape should equal [samples, timesteps, features]\n",
138 |         "  x = signal.reshape(shape)\n",
139 |         "  y = signal_noisy.reshape(shape)\n",
140 |         "  \n",
141 |         "  split_point = int((1-validation_split)*batch_size)\n",
142 |         "  \n",
143 |         "  x_train = x[0:split_point,:,:]\n",
144 |         "  y_train = y[0:split_point,:,:]\n",
145 |         "  x_test = x[split_point:batch_size,:,:]\n",
146 |         "  y_test = y[split_point:batch_size][:]\n",
147 |         "\n",
148 |         "  return (x_train,y_train,features),(x_test,y_test)\n",
149 |         "\n",
150 |         "def calculate_compressions_window_range(timesteps = 2000,window_size = 2,layers = 3,\n",
151 |         "                                       filter_spacing = None):\n",
152 |         "  window_range = []\n",
153 |         "  for i in range(layers):\n",
154 |         "    if timesteps%window_size != 0:\n",
155 |         "      if timesteps%2 == 0:\n",
156 |         "        tmp_window = 2\n",
157 |         "      elif timesteps%3 == 0:\n",
158 |         "        tmp_window = 3\n",
159 |         "      elif timesteps%5 == 0:\n",
160 |         "        tmp_window = 5\n",
161 |         "      elif timesteps%7 == 0:\n",
162 |         "        tmp_window = 7\n",
163 |         "      window_range.append(tmp_window)\n",
164 |         "      timesteps /= tmp_window\n",
165 |         "    else:\n",
166 |         "      window_range.append(window_size)\n",
167 |         "      timesteps /= window_size\n",
168 |         "    \n",
169 |         "    if filter_spacing == 'half':\n",
170 |         "      window_range = window_range[0:layers]\n",
171 |         "    \n",
172 |         "   \n",
173 |         "  return window_range \n",
174 |         "\n",
175 |         "def create_filter_range(filter_spacing,max_filters,min_filters,layers):\n",
176 |         "  #Linear Filter Range\n",
177 |         "  if filter_spacing == 'lin':\n",
178 |         "    filter_range = np.linspace(max_filters,min_filters,layers,\n",
179 |         "                               dtype = 'int64')\n",
180 |         "  #Half Filter Range\n",
181 |         "  if filter_spacing == 'half':\n",
182 |         "    if max_filters%2 != 0:\n",
183 |         "      max_filters+=1\n",
184 |         "    if min_filters%2 != 0:\n",
185 |         "      min_filters+=1\n",
186 |         "    filter_range = [max_filters]\n",
187 |         "    x = max_filters\n",
188 |         "    while x> min_filters:\n",
189 |         "      x = x/2\n",
190 |         "      filter_range.append(x)\n",
191 |         "    if filter_range[-1] < min_filters:\n",
192 |         "      del filter_range[-1]\n",
193 |         "    filter_range = np.asarray(filter_range,dtype = 'int64')\n",
194 |         "  \n",
195 |         "  #Random Filter Range\n",
196 |         "  if filter_spacing == 'random':\n",
197 |         "    filter_range = [randint(min_filters, max_filters) for z in range(layers)]\n",
198 |         "    filter_range.sort(reverse= True)\n",
199 |         "    filter_range = np.asarray(filter_range)\n",
200 |         "  \n",
201 |         "  return filter_range\n",
202 |         "  \n",
203 |         "\n",
204 |         "#Gnerate Model For Optimization\n",
205 |         "def create_autoencoder(train_data,max_filters = 32, min_filters = 8, layers = 3,\n",
206 |         "                      kernel_size = 3,window_size = 2,filter_spacing = 'lin'):\n",
207 |         "  \n",
208 |         "  timesteps = 2000\n",
209 |         "  features = 1\n",
210 |         "  \n",
211 |         "  filter_range = create_filter_range(filter_spacing,max_filters,min_filters,\n",
212 |         "                                    layers)\n",
213 |         "  layers = len(filter_range)\n",
214 |         "  \n",
215 |         "  window_range = calculate_compressions_window_range(window_size = window_size,\n",
216 |         "                                                     layers = layers)\n",
217 |         "  \n",
218 |         "  \n",
219 |         "  inputs = Input(shape = (timesteps,features),name = 'Input')\n",
220 |         "  encoded = inputs\n",
221 |         "  \n",
222 |         "  \n",
223 |         "    \n",
224 |         "  conv_name = ''\n",
225 |         "  count = 1\n",
226 |         "  for num_filters in filter_range: \n",
227 |         "    conv_name = 'Conv_Layer_No._{0}'.format(count)\n",
228 |         "    encoded = Conv1D(num_filters,kernel_size,padding = 'same',name = conv_name)(encoded)\n",
229 |         "    count+=1\n",
230 |         "    if(num_filters==max_filters):\n",
231 |         "      encoded = BatchNormalization()(encoded)\n",
232 |         "  \n",
233 |         "    encoded = Activation('relu')(encoded)\n",
234 |         "    encoded = MaxPooling1D(window_range[count-2],padding = 'same')(encoded)\n",
235 |         "  \n",
236 |         "  filter_range = np.sort(filter_range)\n",
237 |         "  for num_filters in filter_range: \n",
238 |         "    conv_name = 'Conv_Layer_No._{0}'.format(count)\n",
239 |         "    encoded = Conv1D(num_filters,kernel_size,padding = 'same',name = conv_name)(encoded)\n",
240 |         "    count+=1\n",
241 |         "    encoded = Activation('relu')(encoded)\n",
242 |         "    encoded = UpSampling1D(window_range[-(count-layers-1)])(encoded)\n",
243 |         "    \n",
244 |         "  decoded = Conv1D(1,kernel_size,activation = 'sigmoid',padding = 'same')(encoded)\n",
245 |         "  \n",
246 |         "  autoencoder = Model(inputs,decoded)\n",
247 |         "  autoencoder.summary()\n",
248 |         "  optimizer = RMSprop(lr=1e-3)\n",
249 |         "  autoencoder.compile(optimizer = optimizer, loss = 'mse')\n",
250 |         "  \n",
251 |         "  return autoencoder\n",
252 |         "\n",
253 |         "\n",
254 |         "\n",
255 |         "\n",
256 |         "\n",
257 |         "#HyperParameter Range\n",
258 |         "hyper_params = {\n",
259 |         "    \"data\":{\n",
260 |         "        'timesteps': 2000\n",
261 |         "        \n",
262 |         "        \n",
263 |         "    },\n",
264 |         "    \"model\":{\n",
265 |         "        \"max_filters\":hp.choice('mx_filters',np.arange(32,320,dtype = 'int32')),\n",
266 |         "        \"min_filters\":hp.choice('mn_filters',np.arange(4,32,dtype = 'int32')),\n",
267 |         "        'layers':hp.choice('ly',np.arange(1,3,dtype = 'int32')),\n",
268 |         "        'filter_spacing':hp.choice('fs',['lin','half'])\n",
269 |         "        \n",
270 |         "    },\n",
271 |         "    \"fit\":{\n",
272 |         "        'epochs': 1,\n",
273 |         "        'patience': 3\n",
274 |         "    }\n",
275 |         "}\n",
276 |         "\n",
277 |         "\n",
278 |         "\n",
279 |         "#NN Evaluation\n",
280 |         "db_name = 'Autoencoder'\n",
281 |         "exp_name ='exp_1'\n",
282 |         "objective = CompileFN(db_name,exp_name,\n",
283 |         "                      data_fn = create_data,\n",
284 |         "                      model_fn = create_autoencoder,\n",
285 |         "                      loss_metric = 'loss',\n",
286 |         "                      loss_metric_mode = 'min',\n",
287 |         "                      valid_split = None,\n",
288 |         "                      save_model = 'best',\n",
289 |         "                      save_results = True)\n",
290 |         "                      \n",
291 |         "                      \n",
292 |         "\n",
293 |         "\n",
294 |         "# model_file.Upload()\n",
295 |         "# drive.CreateFile({'id': model_file.get('id')})\n",
296 |         "\n",
297 |         "#test_fn(objective, hyper_params)\n",
298 |         "trials = Trials()\n",
299 |         "best = fmin(objective, hyper_params, trials=trials, algo=tpe.suggest, max_evals=2)\n",
300 |         "\n",
301 |         "trial_data = trials.results\n",
302 |         "print(trial_data)\n",
303 |         "\n",
304 |         "val_loss = []\n",
305 |         "loss = []\n",
306 |         "filter_spacing = []\n",
307 |         "max_filters = []\n",
308 |         "min_filters = []\n",
309 |         "layers = []\n",
310 |         "for i in range(len(trial_data)):\n",
311 |         "    trial = trial_data[i]\n",
312 |         "    val_loss.append('%.6f'%(trial['history']['loss']['val_loss'][0])) \n",
313 |         "    loss.append('%.6f'%(trial['history']['loss']['loss'][0]))\n",
314 |         "    filter_spacing.append(trial['param']['model']['filter_spacing'])\n",
315 |         "    max_filters.append(trial['param']['model']['max_filters'])\n",
316 |         "    min_filters.append(trial['param']['model']['min_filters'])\n",
317 |         "    layers.append(trial['param']['model']['layers'])\n",
318 |         "\n",
319 |         "\n",
320 |         "new_dict = {'val_loss':val_loss,'loss':loss,\n",
321 |         "            'f_spacing':filter_spacing,'max_f':max_filters,'min_f':min_filters}\n",
322 |         "\n",
323 |         "data_frame = pd.DataFrame.from_dict(new_dict, orient='columns')\n",
324 |         "print(data_frame)\n",
325 |         "    \n",
326 |         "\n",
327 |         "#Mongo Trials - Cont'd\n",
328 |         "#trials = MongoTrials(SRV_string,exp_key = 'Optimization_1')\n",
329 |         "#trials = MongoTrials('mongo://{0}:{1}/{2}/jobs'.format(hostname,port,db_name), exp_key='exp1')\n",
330 |         "#trials = KMongoTrials(db_name, exp_name,\n",
331 |         "#                      ip=hostname,\n",
332 |         "#                      port=port)\n",
333 |         "\n",
334 |         "\n",
335 |         "#Save Results to Google Drive\n",
336 |         "\n",
337 |         "with open('/content/gdrive/My Drive/Optimization_Results.txt', 'w') as f:\n",
338 |         "  f.write('{0}'.format(data_frame))\n",
339 |         "!cat /content/gdrive/My\\ Drive/Optimization_Results.txt\n",
340 |         "# model_file = drive.CreateFile({'title' : 'Optimization_Data.txt'})\n",
341 |         "# model_file.SetContentFile(data_frame)\n",
342 |         "# model_file.Upload()\n",
343 |         "# drive.CreateFile({'id': model_file.get('id')})\n",
344 |         "\n",
345 |         "\n",
346 |         " \n",
347 |         "#Save Model to Google Drive\n",
348 |         "# model_file = drive.CreateFile({'title' : 'CNN1D_v1.0.h5'})\n",
349 |         "# model_file.SetContentFile('model.h5')\n",
350 |         "# model_file.Upload()\n",
351 |         "# drive.CreateFile({'id': model_file.get('id')})\n",
352 |         "  \n",
353 |         "  \n",
354 |         "  \n",
355 |         "  \n",
356 |         "  \n",
357 |         "  "
358 |       ],
359 |       "execution_count": 0,
360 |       "outputs": []
361 |     }
362 |   ]
363 | }
364 | 


--------------------------------------------------------------------------------
/CNN/CNN1D_v1.0.json:
--------------------------------------------------------------------------------
  1 | {
  2 |   "architecture": {
  3 |     "backend": "tensorflow",
  4 |     "class_name": "Model",
  5 |     "config": {
  6 |       "input_layers": [
  7 |         [
  8 |           "input_1",
  9 |           0,
 10 |           0
 11 |         ]
 12 |       ],
 13 |       "layers": [
 14 |         {
 15 |           "class_name": "InputLayer",
 16 |           "config": {
 17 |             "batch_input_shape": [
 18 |               null,
 19 |               2000,
 20 |               1
 21 |             ],
 22 |             "dtype": "float32",
 23 |             "name": "input_1",
 24 |             "sparse": false
 25 |           },
 26 |           "inbound_nodes": [],
 27 |           "name": "input_1"
 28 |         },
 29 |         {
 30 |           "class_name": "Conv1D",
 31 |           "config": {
 32 |             "activation": "linear",
 33 |             "activity_regularizer": null,
 34 |             "bias_constraint": null,
 35 |             "bias_initializer": {
 36 |               "class_name": "Zeros",
 37 |               "config": {}
 38 |             },
 39 |             "bias_regularizer": null,
 40 |             "data_format": "channels_last",
 41 |             "dilation_rate": [
 42 |               1
 43 |             ],
 44 |             "filters": 32,
 45 |             "kernel_constraint": null,
 46 |             "kernel_initializer": {
 47 |               "class_name": "VarianceScaling",
 48 |               "config": {
 49 |                 "distribution": "uniform",
 50 |                 "mode": "fan_avg",
 51 |                 "scale": 1.0,
 52 |                 "seed": null
 53 |               }
 54 |             },
 55 |             "kernel_regularizer": null,
 56 |             "kernel_size": [
 57 |               3
 58 |             ],
 59 |             "name": "conv1d_1",
 60 |             "padding": "same",
 61 |             "strides": [
 62 |               1
 63 |             ],
 64 |             "trainable": true,
 65 |             "use_bias": true
 66 |           },
 67 |           "inbound_nodes": [
 68 |             [
 69 |               [
 70 |                 "input_1",
 71 |                 0,
 72 |                 0,
 73 |                 {}
 74 |               ]
 75 |             ]
 76 |           ],
 77 |           "name": "conv1d_1"
 78 |         },
 79 |         {
 80 |           "class_name": "BatchNormalization",
 81 |           "config": {
 82 |             "axis": -1,
 83 |             "beta_constraint": null,
 84 |             "beta_initializer": {
 85 |               "class_name": "Zeros",
 86 |               "config": {}
 87 |             },
 88 |             "beta_regularizer": null,
 89 |             "center": true,
 90 |             "epsilon": 0.001,
 91 |             "gamma_constraint": null,
 92 |             "gamma_initializer": {
 93 |               "class_name": "Ones",
 94 |               "config": {}
 95 |             },
 96 |             "gamma_regularizer": null,
 97 |             "momentum": 0.99,
 98 |             "moving_mean_initializer": {
 99 |               "class_name": "Zeros",
100 |               "config": {}
101 |             },
102 |             "moving_variance_initializer": {
103 |               "class_name": "Ones",
104 |               "config": {}
105 |             },
106 |             "name": "batch_normalization_1",
107 |             "scale": true,
108 |             "trainable": true
109 |           },
110 |           "inbound_nodes": [
111 |             [
112 |               [
113 |                 "conv1d_1",
114 |                 0,
115 |                 0,
116 |                 {}
117 |               ]
118 |             ]
119 |           ],
120 |           "name": "batch_normalization_1"
121 |         },
122 |         {
123 |           "class_name": "Activation",
124 |           "config": {
125 |             "activation": "relu",
126 |             "name": "activation_1",
127 |             "trainable": true
128 |           },
129 |           "inbound_nodes": [
130 |             [
131 |               [
132 |                 "batch_normalization_1",
133 |                 0,
134 |                 0,
135 |                 {}
136 |               ]
137 |             ]
138 |           ],
139 |           "name": "activation_1"
140 |         },
141 |         {
142 |           "class_name": "MaxPooling1D",
143 |           "config": {
144 |             "data_format": "channels_last",
145 |             "name": "max_pooling1d_1",
146 |             "padding": "same",
147 |             "pool_size": [
148 |               2
149 |             ],
150 |             "strides": [
151 |               2
152 |             ],
153 |             "trainable": true
154 |           },
155 |           "inbound_nodes": [
156 |             [
157 |               [
158 |                 "activation_1",
159 |                 0,
160 |                 0,
161 |                 {}
162 |               ]
163 |             ]
164 |           ],
165 |           "name": "max_pooling1d_1"
166 |         },
167 |         {
168 |           "class_name": "Conv1D",
169 |           "config": {
170 |             "activation": "linear",
171 |             "activity_regularizer": null,
172 |             "bias_constraint": null,
173 |             "bias_initializer": {
174 |               "class_name": "Zeros",
175 |               "config": {}
176 |             },
177 |             "bias_regularizer": null,
178 |             "data_format": "channels_last",
179 |             "dilation_rate": [
180 |               1
181 |             ],
182 |             "filters": 16,
183 |             "kernel_constraint": null,
184 |             "kernel_initializer": {
185 |               "class_name": "VarianceScaling",
186 |               "config": {
187 |                 "distribution": "uniform",
188 |                 "mode": "fan_avg",
189 |                 "scale": 1.0,
190 |                 "seed": null
191 |               }
192 |             },
193 |             "kernel_regularizer": null,
194 |             "kernel_size": [
195 |               3
196 |             ],
197 |             "name": "conv1d_2",
198 |             "padding": "same",
199 |             "strides": [
200 |               1
201 |             ],
202 |             "trainable": true,
203 |             "use_bias": true
204 |           },
205 |           "inbound_nodes": [
206 |             [
207 |               [
208 |                 "max_pooling1d_1",
209 |                 0,
210 |                 0,
211 |                 {}
212 |               ]
213 |             ]
214 |           ],
215 |           "name": "conv1d_2"
216 |         },
217 |         {
218 |           "class_name": "Activation",
219 |           "config": {
220 |             "activation": "relu",
221 |             "name": "activation_2",
222 |             "trainable": true
223 |           },
224 |           "inbound_nodes": [
225 |             [
226 |               [
227 |                 "conv1d_2",
228 |                 0,
229 |                 0,
230 |                 {}
231 |               ]
232 |             ]
233 |           ],
234 |           "name": "activation_2"
235 |         },
236 |         {
237 |           "class_name": "MaxPooling1D",
238 |           "config": {
239 |             "data_format": "channels_last",
240 |             "name": "max_pooling1d_2",
241 |             "padding": "same",
242 |             "pool_size": [
243 |               2
244 |             ],
245 |             "strides": [
246 |               2
247 |             ],
248 |             "trainable": true
249 |           },
250 |           "inbound_nodes": [
251 |             [
252 |               [
253 |                 "activation_2",
254 |                 0,
255 |                 0,
256 |                 {}
257 |               ]
258 |             ]
259 |           ],
260 |           "name": "max_pooling1d_2"
261 |         },
262 |         {
263 |           "class_name": "Conv1D",
264 |           "config": {
265 |             "activation": "linear",
266 |             "activity_regularizer": null,
267 |             "bias_constraint": null,
268 |             "bias_initializer": {
269 |               "class_name": "Zeros",
270 |               "config": {}
271 |             },
272 |             "bias_regularizer": null,
273 |             "data_format": "channels_last",
274 |             "dilation_rate": [
275 |               1
276 |             ],
277 |             "filters": 8,
278 |             "kernel_constraint": null,
279 |             "kernel_initializer": {
280 |               "class_name": "VarianceScaling",
281 |               "config": {
282 |                 "distribution": "uniform",
283 |                 "mode": "fan_avg",
284 |                 "scale": 1.0,
285 |                 "seed": null
286 |               }
287 |             },
288 |             "kernel_regularizer": null,
289 |             "kernel_size": [
290 |               3
291 |             ],
292 |             "name": "conv1d_3",
293 |             "padding": "same",
294 |             "strides": [
295 |               1
296 |             ],
297 |             "trainable": true,
298 |             "use_bias": true
299 |           },
300 |           "inbound_nodes": [
301 |             [
302 |               [
303 |                 "max_pooling1d_2",
304 |                 0,
305 |                 0,
306 |                 {}
307 |               ]
308 |             ]
309 |           ],
310 |           "name": "conv1d_3"
311 |         },
312 |         {
313 |           "class_name": "Activation",
314 |           "config": {
315 |             "activation": "relu",
316 |             "name": "activation_3",
317 |             "trainable": true
318 |           },
319 |           "inbound_nodes": [
320 |             [
321 |               [
322 |                 "conv1d_3",
323 |                 0,
324 |                 0,
325 |                 {}
326 |               ]
327 |             ]
328 |           ],
329 |           "name": "activation_3"
330 |         },
331 |         {
332 |           "class_name": "MaxPooling1D",
333 |           "config": {
334 |             "data_format": "channels_last",
335 |             "name": "max_pooling1d_3",
336 |             "padding": "same",
337 |             "pool_size": [
338 |               2
339 |             ],
340 |             "strides": [
341 |               2
342 |             ],
343 |             "trainable": true
344 |           },
345 |           "inbound_nodes": [
346 |             [
347 |               [
348 |                 "activation_3",
349 |                 0,
350 |                 0,
351 |                 {}
352 |               ]
353 |             ]
354 |           ],
355 |           "name": "max_pooling1d_3"
356 |         },
357 |         {
358 |           "class_name": "Conv1D",
359 |           "config": {
360 |             "activation": "linear",
361 |             "activity_regularizer": null,
362 |             "bias_constraint": null,
363 |             "bias_initializer": {
364 |               "class_name": "Zeros",
365 |               "config": {}
366 |             },
367 |             "bias_regularizer": null,
368 |             "data_format": "channels_last",
369 |             "dilation_rate": [
370 |               1
371 |             ],
372 |             "filters": 8,
373 |             "kernel_constraint": null,
374 |             "kernel_initializer": {
375 |               "class_name": "VarianceScaling",
376 |               "config": {
377 |                 "distribution": "uniform",
378 |                 "mode": "fan_avg",
379 |                 "scale": 1.0,
380 |                 "seed": null
381 |               }
382 |             },
383 |             "kernel_regularizer": null,
384 |             "kernel_size": [
385 |               3
386 |             ],
387 |             "name": "conv1d_4",
388 |             "padding": "same",
389 |             "strides": [
390 |               1
391 |             ],
392 |             "trainable": true,
393 |             "use_bias": true
394 |           },
395 |           "inbound_nodes": [
396 |             [
397 |               [
398 |                 "max_pooling1d_3",
399 |                 0,
400 |                 0,
401 |                 {}
402 |               ]
403 |             ]
404 |           ],
405 |           "name": "conv1d_4"
406 |         },
407 |         {
408 |           "class_name": "Activation",
409 |           "config": {
410 |             "activation": "relu",
411 |             "name": "activation_4",
412 |             "trainable": true
413 |           },
414 |           "inbound_nodes": [
415 |             [
416 |               [
417 |                 "conv1d_4",
418 |                 0,
419 |                 0,
420 |                 {}
421 |               ]
422 |             ]
423 |           ],
424 |           "name": "activation_4"
425 |         },
426 |         {
427 |           "class_name": "UpSampling1D",
428 |           "config": {
429 |             "name": "up_sampling1d_1",
430 |             "size": 2,
431 |             "trainable": true
432 |           },
433 |           "inbound_nodes": [
434 |             [
435 |               [
436 |                 "activation_4",
437 |                 0,
438 |                 0,
439 |                 {}
440 |               ]
441 |             ]
442 |           ],
443 |           "name": "up_sampling1d_1"
444 |         },
445 |         {
446 |           "class_name": "Conv1D",
447 |           "config": {
448 |             "activation": "linear",
449 |             "activity_regularizer": null,
450 |             "bias_constraint": null,
451 |             "bias_initializer": {
452 |               "class_name": "Zeros",
453 |               "config": {}
454 |             },
455 |             "bias_regularizer": null,
456 |             "data_format": "channels_last",
457 |             "dilation_rate": [
458 |               1
459 |             ],
460 |             "filters": 16,
461 |             "kernel_constraint": null,
462 |             "kernel_initializer": {
463 |               "class_name": "VarianceScaling",
464 |               "config": {
465 |                 "distribution": "uniform",
466 |                 "mode": "fan_avg",
467 |                 "scale": 1.0,
468 |                 "seed": null
469 |               }
470 |             },
471 |             "kernel_regularizer": null,
472 |             "kernel_size": [
473 |               3
474 |             ],
475 |             "name": "conv1d_5",
476 |             "padding": "same",
477 |             "strides": [
478 |               1
479 |             ],
480 |             "trainable": true,
481 |             "use_bias": true
482 |           },
483 |           "inbound_nodes": [
484 |             [
485 |               [
486 |                 "up_sampling1d_1",
487 |                 0,
488 |                 0,
489 |                 {}
490 |               ]
491 |             ]
492 |           ],
493 |           "name": "conv1d_5"
494 |         },
495 |         {
496 |           "class_name": "Activation",
497 |           "config": {
498 |             "activation": "relu",
499 |             "name": "activation_5",
500 |             "trainable": true
501 |           },
502 |           "inbound_nodes": [
503 |             [
504 |               [
505 |                 "conv1d_5",
506 |                 0,
507 |                 0,
508 |                 {}
509 |               ]
510 |             ]
511 |           ],
512 |           "name": "activation_5"
513 |         },
514 |         {
515 |           "class_name": "UpSampling1D",
516 |           "config": {
517 |             "name": "up_sampling1d_2",
518 |             "size": 2,
519 |             "trainable": true
520 |           },
521 |           "inbound_nodes": [
522 |             [
523 |               [
524 |                 "activation_5",
525 |                 0,
526 |                 0,
527 |                 {}
528 |               ]
529 |             ]
530 |           ],
531 |           "name": "up_sampling1d_2"
532 |         },
533 |         {
534 |           "class_name": "Conv1D",
535 |           "config": {
536 |             "activation": "linear",
537 |             "activity_regularizer": null,
538 |             "bias_constraint": null,
539 |             "bias_initializer": {
540 |               "class_name": "Zeros",
541 |               "config": {}
542 |             },
543 |             "bias_regularizer": null,
544 |             "data_format": "channels_last",
545 |             "dilation_rate": [
546 |               1
547 |             ],
548 |             "filters": 32,
549 |             "kernel_constraint": null,
550 |             "kernel_initializer": {
551 |               "class_name": "VarianceScaling",
552 |               "config": {
553 |                 "distribution": "uniform",
554 |                 "mode": "fan_avg",
555 |                 "scale": 1.0,
556 |                 "seed": null
557 |               }
558 |             },
559 |             "kernel_regularizer": null,
560 |             "kernel_size": [
561 |               3
562 |             ],
563 |             "name": "conv1d_6",
564 |             "padding": "same",
565 |             "strides": [
566 |               1
567 |             ],
568 |             "trainable": true,
569 |             "use_bias": true
570 |           },
571 |           "inbound_nodes": [
572 |             [
573 |               [
574 |                 "up_sampling1d_2",
575 |                 0,
576 |                 0,
577 |                 {}
578 |               ]
579 |             ]
580 |           ],
581 |           "name": "conv1d_6"
582 |         },
583 |         {
584 |           "class_name": "Activation",
585 |           "config": {
586 |             "activation": "relu",
587 |             "name": "activation_6",
588 |             "trainable": true
589 |           },
590 |           "inbound_nodes": [
591 |             [
592 |               [
593 |                 "conv1d_6",
594 |                 0,
595 |                 0,
596 |                 {}
597 |               ]
598 |             ]
599 |           ],
600 |           "name": "activation_6"
601 |         },
602 |         {
603 |           "class_name": "UpSampling1D",
604 |           "config": {
605 |             "name": "up_sampling1d_3",
606 |             "size": 2,
607 |             "trainable": true
608 |           },
609 |           "inbound_nodes": [
610 |             [
611 |               [
612 |                 "activation_6",
613 |                 0,
614 |                 0,
615 |                 {}
616 |               ]
617 |             ]
618 |           ],
619 |           "name": "up_sampling1d_3"
620 |         },
621 |         {
622 |           "class_name": "Conv1D",
623 |           "config": {
624 |             "activation": "sigmoid",
625 |             "activity_regularizer": null,
626 |             "bias_constraint": null,
627 |             "bias_initializer": {
628 |               "class_name": "Zeros",
629 |               "config": {}
630 |             },
631 |             "bias_regularizer": null,
632 |             "data_format": "channels_last",
633 |             "dilation_rate": [
634 |               1
635 |             ],
636 |             "filters": 1,
637 |             "kernel_constraint": null,
638 |             "kernel_initializer": {
639 |               "class_name": "VarianceScaling",
640 |               "config": {
641 |                 "distribution": "uniform",
642 |                 "mode": "fan_avg",
643 |                 "scale": 1.0,
644 |                 "seed": null
645 |               }
646 |             },
647 |             "kernel_regularizer": null,
648 |             "kernel_size": [
649 |               3
650 |             ],
651 |             "name": "conv1d_7",
652 |             "padding": "same",
653 |             "strides": [
654 |               1
655 |             ],
656 |             "trainable": true,
657 |             "use_bias": true
658 |           },
659 |           "inbound_nodes": [
660 |             [
661 |               [
662 |                 "up_sampling1d_3",
663 |                 0,
664 |                 0,
665 |                 {}
666 |               ]
667 |             ]
668 |           ],
669 |           "name": "conv1d_7"
670 |         }
671 |       ],
672 |       "name": "model_1",
673 |       "output_layers": [
674 |         [
675 |           "conv1d_7",
676 |           0,
677 |           0
678 |         ]
679 |       ]
680 |     },
681 |     "keras_version": "2.2.4"
682 |   },
683 |   "average_pooling_2d_same_offset": false,
684 |   "average_pooling_2d_valid_offset": false,
685 |   "conv2d_same_offset_depth_1": false,
686 |   "conv2d_same_offset_depth_2": false,
687 |   "conv2d_valid_offset_depth_1": false,
688 |   "conv2d_valid_offset_depth_2": false,
689 |   "hash": "52eb9d5fb2ab23766f31d123e2d04aeb2f7217af4423aae720a314e08a24c709",
690 |   "image_data_format": "channels_last",
691 |   "input_shapes": [
692 |     [
693 |       1,
694 |       1,
695 |       1,
696 |       2000,
697 |       1
698 |     ]
699 |   ],
700 |   "max_pooling_2d_same_offset": false,
701 |   "max_pooling_2d_valid_offset": false,
702 |   "separable_conv2d_same_offset_depth_1": false,
703 |   "separable_conv2d_same_offset_depth_2": false,
704 |   "separable_conv2d_valid_offset_depth_1": false,
705 |   "separable_conv2d_valid_offset_depth_2": false,
706 |   "tests": [
707 |     {
708 |       "inputs": [
709 |         {
710 |           "shape": [
711 |             1,
712 |             1,
713 |             1,
714 |             2000,
715 |             1
716 |           ],
717 |           "values": [
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723 |             "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",
724 |             "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",
725 |             "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",
726 |             "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",
727 |             "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",
728 |             "A4hWP056BMCUQbK9DmsKv1WRpj/W+pq/4kZ6vjLZ5Lwf3Ya/FcFRvNW2+L1UAO89OLcVv2ACkD3T9XY+d5OHP6r50b6KSbW/KE7cPgGN/75GMhw+pv7DvvMXjz/SO9k+gYH8vhd3tj6i8e49vbtAP74R/z4JLI4/OJUvvAybWz9bvu6+cBncvoantz+Q04M/lOrPP1Mfxr2GnJY+BOYgPymSDL7/hly/rWNXP00dGb8ggeS+WKtsv++2qz272rO//8rRv9VXUL8FepA/4X1nvpHCnz+rBdQ+fnwKPm6Qlj93Aao/s9CdP1AZST52imc/J7wOPx2+n78ehh09JUWKvdvxOT88heq+re3CvoyXCL9kSQm+I/BRv0dV3T7hJQ+/JUNQv+pPyj/ga5s+P1LhP4FHLL88U4u9x2Vbv4CXgL8="
729 |           ]
730 |         }
731 |       ],
732 |       "outputs": [
733 |         {
734 |           "shape": [
735 |             1,
736 |             1,
737 |             1,
738 |             2000,
739 |             1
740 |           ],
741 |           "values": [
742 |             "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",
743 |             "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",
744 |             "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",
745 |             "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",
746 |             "Et++MhQEhTMlNRc03SwCNQ0ceTXTR8k18coNNpSmizb+e9c2sBc+NxUptjfqg8M3PlCiOD5UqjnLs5s598jmOdPXAzrXm4s5NxMyOTfqgjnbTz45IRIvOaMeRzl6Hak4IC1SOJIaKze28c82UsVWNi49nzXnmXQ1nrMwNdK5NDT5sYsznESPNEa7UzX4rUw1qW63NVoFjTVbhXw1bSKHNdTePjUa23U1Zul+NVu3djU0V6c1f0nJNX0QsDXp1Qg23EJFNo97ADZOoLc1lzadNdGxQjWqJC41aPwDNfuTzTScAdE0pjVcNDgh/jPzc/kzERTXM+C+ADSMBb0zyhfDM5pyBzTk++8zUzzjM8jGsTMF0EAz4POiMg9JezIlXDkx61AwMNFl9zBfZGMxYx/RMYqbUjO4mYE04PbCNOCVgjbsyj43B3mVN0rJ5TdnwBI4+ohdOACrmzj3Oic5Rk1DOQCoYjkIJFQ5GFtlOX6FRzmGmL84HW4VOM2rDjhb1Ow21j3MNTwstDXnW7s1rdmNNVBeizXEaE411vssNWRsNTVlXk81tZjrNUw+YTbNyrs2jzM3N5REpDfQK5g3c9azNzOlrDeaqKY3KgKZNwRlbzeD0jk3OD8PN+YHKjerRh43dLYMN1GuLDeHtYo3xGAFOGPCQzjNJbI4P2juOCGxNzm3bzw5z1YcOZOnKDnQ5gM5kdMmOReD3DiCIWE4NxmBONpm0zjLjQU56GEYOT8RZTnsnxE5/CjLOBGQCTlyV9c4RkGxOPDPrTgLFMY4yUK7OCxKuDj6j6o4UyGGOIT7XDjxaz84eeMhN78EKDbDXO40KGNdMyjGrzGE9kgwH2xCLcqSqStwJS0tUTyiLg39ajBjR00xQZ7IMTd2PjJ761UyPfg6MnpICjIX0tMx/mbEMRH51zFCreYxv2maMY8hmTG3jJsxLZj1MahJITLLyDMyUhlUMrY8gzKqTboy6Dn8MhjOBzPI+lUzGy6fM28WjTPTuxk0ux57NFmdYjRB3t00",
747 |             "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",
748 |             "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",
749 |             "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",
750 |             "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",
751 |             "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",
752 |             "XJaRNWoBmjVNDpw1PbnGNUR+PjYQnuA2YEjRNlw4XDf6RYg3wAhkN1WXbDeV5pc3KhylN32OwTeUFRI4KskkOFlSQzgeSp44R30xOYL9VTlIxOI5O5Y9OgvARjrIfxo6re1KOiidQTrL8zE6Ap+DOshUOzqUTSA6ShYoOhD7zTmk55k5amcgOUJxyThDDZ04IiEzOO5IETjoxv03b6ivN/pzcDfR5Yo3B/l+NwZWkzckwsw3gnqfN9qMXTd2hZk35ozYN1eg7zfuvQw4i/0mODfdWTh4xro4lifHOIyVuTgwdY44sm9fONSOYjg1Zg44DpnZN8zMyDfzARM4AamFOIjGtjh+V+c4n5xBOSXKqzn5aQM6fB4HOg8kGDomDk86kMc7OnSzrjpGue46hOgYO4gXCzuu7dw7sSRdO29Iwjs="
753 |           ]
754 |         }
755 |       ]
756 |     }
757 |   ],
758 |   "trainable_params": {
759 |     "batch_normalization_1": {
760 |       "beta": [
761 |         "cYKxvdNjAb4MGG29heiCvhpdhz64za293jpNvW33pL3+ZoO96HhDvZ5xN732zWq97sy5vRcN67zuU6O9WYoXvj9I372e7269smOTvZOVQL1INby9V0UJvqaqj7ubEAS+1Gzrvb3HDr6zNXu9cFKqveuaA734uCO+N/Mdvtq6vb0="
762 |       ],
763 |       "gamma": [
764 |         "nH6APyNZgj9u7oI/08xZPwDGWj/RjII/RDJnPwVygT+ueH8/tjFNPxRGaj9fF4I/6nKAP5qefT/smnI/TkqBP33sdT/LC3c/+peCP1k5dT+DZX8/M4Y5P7TqcD/u5Hc/kOV4P+2/YD8xOIM/wwx+P+NlWz8cxmY/l3xFP/t0fj8="
765 |       ],
766 |       "moving_mean": [
767 |         "xTQ2PqRYJ772T4k+lQgiPCZn1DwY2kW+0X+Iu/IveL4AufW9UASqOx/0+ztgg8g+E19VPg0HjrwLIIS9+x+Avu7h9j1glYG9oXIuPsx8Bb4ATWC+lwaaOqTYv7yXvHE+QEZYPuuJCzyQOWo+AKFOPtl8Qz7ICB28SdRtvCNPMD4="
768 |       ],
769 |       "moving_variance": [
770 |         "hpM3PBL+HDxKxNE8r7TzOHEtuzmoDlg8nndbOL08qzw3WKc7eyL4Nz5Cgjj7EWA9mI18PNJpSTnKhcA6VxS2PFqAqTu2nb06FwopPGHsxzuzt4s8he9POat5tzlW3KM8UsaBPDSflzi+GZg8sAFtPLoEVDwz1hM5mZmoOXrVLTw="
771 |       ]
772 |     },
773 |     "conv1d_1": {
774 |       "bias": [
775 |         "OV98OdkEszlg6r63lEMkt3kwEbng0cO5gB25NmaXpLgIavm4L7ggN6DgnDhWKgm5mp0nOTIxADlvZI65udlcuJxUFzdEloW5LTWats7zCjX3RrY3sJEPutiV0znOiiq3bSSIOehDcLgWdXg4vY8TOSdIvDi+V6i4TZUTOcR3xbk="
776 |       ],
777 |       "weights": [
778 |         "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"
779 |       ]
780 |     },
781 |     "conv1d_2": {
782 |       "bias": [
783 |         "4LGFOeclsD64Yji+5d+JvKUaEj6bfha+u15svi50VL68zFa9A44pvSEnNb0JZFC9GjMfvcdyUr4lvgm9XZ7RvQ=="
784 |       ],
785 |       "weights": [
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793 |         "fzo5PmUQzr0bYBk+UTwSPdSGgL1DUqa99YYcvUvqLb7y1G48QjYOvv5haT1z2gc+obSbvAVrQDzdzEC+93qkvr9gBT6o0q+7i2lFvYkltL46Tby97wcwPB9EhL0Xx1080PqOPRQNoD2cwf892bbnPZ7BWb37s9Q9WggIvgviMT62zTe+pLtivstjFj4dfos8LTYIvoXYhL4mTvs8WA3XvjQt9Dxn7gI9RdwmPsbTazyYGWe8KCmYvjHuyr6Qo6g9Ly3VvWmgT74Gwjs+pkkJvsUNpL7vwF++J4SJvbzVCT57QlQ+YIgWPtTJNT58lnk99ldKvAnhn731ZzO7Rnc/PqjeP73aK9W+/3tTPvgHAL3wYYE9MzkrPv+LIb5Q59q9+YI4vav1l70Y8Iy9Sx4xvqPSp712/CO+91DovX+eBj6G40M+DivIvvRKWz4y9wG+Eoj7vRoTZb4XR9S9YhwDPgAtVj2g45e9wlM+PrJLCj2UScO9nl9lPlo/ED1tVHU+xo/YPVOvQj6t8WQ9J1auvAoHVr4TDkQ+RXV8ve4UbD7Dx9Q97JyXvZO1db5CvLc96nA6Pujkpj1HxHE9pKcCPof1Q74hex67nqYUPm29JL4QHIS7DFozvnprMT53p469DScPvjIKdT2zC3i94JsJPkrZ7L285jq9A33evVQy4z0zNpK9o6dPPZou1TvxSFs92+HwvS0xTj5EId69Dc8AvhAAuT01Gig+NlzTvXSYLz6PL0Y9X/zhvZUhAz7LFfK8twbcvQSPIj5alzU+bfLaO8PScT5/9KW9KXEMvhpU3btCVwg+Qq02Pp98Lb6/uZm9J8M8PuRpwrlVLyo+cLW6Pf65sD1Nh3I+Kqz7vVUyib4XxS6+fnqgvauOYz0V/ys+HtksPn9sBL54nZM98OsDO59Ec736bmy7+nPHPWVHTT7HhSw+Y6DxvYur/T3d7Jy93rravGSyL734hUo92aRbPpYy4DtBqza9pdJoPUJ8Ob5kVoy90f4Nu5mF2z2hWOk9"
794 |       ]
795 |     },
796 |     "conv1d_3": {
797 |       "bias": [
798 |         "MoojvbzO5jw46sC8BM7OvcB8A71NQII8iX/hvHrFY74="
799 |       ],
800 |       "weights": [
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803 |       ]
804 |     },
805 |     "conv1d_4": {
806 |       "bias": [
807 |         "Z8kbPmOhsLxHcsu7mgabPcGsgr4sOJo83qg8u+CO4Dw="
808 |       ],
809 |       "weights": [
810 |         "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"
811 |       ]
812 |     },
813 |     "conv1d_5": {
814 |       "bias": [
815 |         "t6UrvWBHCbzmQvi9yUm5PI7exjvf3g88Q8wyPV+K6L2J21a964qevO3FXz1edUi+3UEcvesYhDw5fHI8begzvg=="
816 |       ],
817 |       "weights": [
818 |         "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",
819 |         "9USnvcsuJr6z3Gg+WQvHPOoySz4Lrek9xFj/PaU7F70vfgA+l9iJvo4Ho77sLmS9oDTzPQ4+2r0Rw0e+C3WXPUwlrz5UcQm/Nosivreolz7HyJs8C6plvgf7oLxRqB2+tpo5vjxZwzxBHU0+QYlGPozqQL7nOQy+sTmWO/yjKL+eJxM+1I/LvfC1Ez5f/AW+2sakPcUaJj7TJyy+j0kPPQLngT5Xyx29lJ36vG9VMT2ePiU+yf84vuZdc77BdbE9yTE8vrHWkj2Jxcy9VgOcvZcYbz5dHM49rLQqPYEnhL5J0Sq+FG0FPub5nL3jpo4+QlNeviYEwz4V/bW9iQvHPSMByT0BbFK9unGcvp15Ab13Xrk9k2GBPU13k77Jkjg+dFXJPm60sT4Knzg+H1mlPJEfuD0ZRYe+2KmCvnlHAb8K3JA+eItHPtkAIr35clK91nUEvij3nb7yrmK+RFabvhUKgj4ExAy+XwKkvgMav75C8P++nlAJvhKfQL6n6IM97bY3PlO1gj4X/4E9CiKDvtCKgz0XE8O9rv0YvoNjzbyLemi9MyNLPrdbS70VSlU+pVI+PlbZbb7Ee4W+9j8bvudLHD1Yvli9D02DPiSBYT7CZ4I+i+VDvm5GALylNQO+7OOJPdknKL70OUy+pZeSPf892T1n+jQ+E/KQPY2Jlr4Eo4E+Yl7nvVJKSL3Xnlw+keixPb+exL2cHr29lLrEvjmnsr4Mhiu+Hh9APqVE1b6J6G2/CxfQvrMElL720b2+SzARvjQZ3Tysq2O+Al2EPe3i1r2K5km8olv8unbDiL2aXIs9zEhKvbRHWz4Z6T2+CXwCviHEk7z/NqQ9sdAlPrAbzL2+xa+9jNOKPTVNkT6zpzu+cJSGvJzTGL4T/Pg9XExDPmapPT1fwiO9OGKzvdeUiD7n/2M+6TcGvqpNA77Fwg8+niSxvW8/ID6/TYe+IA5YPgO9RD7LgiU9Z3CsPjAZPL68CgO+3h91PFGe6j24FLs8nF/VvmWipT280Jk9"
820 |       ]
821 |     },
822 |     "conv1d_6": {
823 |       "bias": [
824 |         "kHHMvApPi7234UC92XdaPRToNb3IgUA9tLedPcjRXLzYdpc7DRqJPasSA7yozIc8aYC3vbyXrzy7fSO8vt+COh6Sxb2YFpK96BRDvTmBtr13DtA8O50nPAzTLb3Gh1k83SVFvcjOoTzNngs8zUojvF6CRjwFmLo8AewXvfrO5bs="
825 |       ],
826 |       "weights": [
827 |         "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",
828 |         "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",
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835 |       ]
836 |     },
837 |     "conv1d_7": {
838 |       "bias": [
839 |         "nX+lvA=="
840 |       ],
841 |       "weights": [
842 |         "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"
843 |       ]
844 |     }
845 |   }
846 | }
847 | 


--------------------------------------------------------------------------------
/CNN/readme.md:
--------------------------------------------------------------------------------
1 | # 1D CNN Autoencoder 
2 | A denoising autoencoder that relies on one dimensional convolutional layers for denoising
3 | 
4 | **Work in Progress**
5 | * Hyperparameter Optimization
6 | * Implement better Scaling Methods
7 | * Train with LFP Data
8 | * C++ Implementation 
9 | 


--------------------------------------------------------------------------------
/CNN1D_Optimize v0.3.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "Copy of CNN1D Optimize(draft).ipynb",
  7 |       "version": "0.3.2",
  8 |       "provenance": [],
  9 |       "collapsed_sections": [],
 10 |       "include_colab_link": true
 11 |     },
 12 |     "kernelspec": {
 13 |       "name": "python3",
 14 |       "display_name": "Python 3"
 15 |     },
 16 |     "accelerator": "GPU"
 17 |   },
 18 |   "cells": [
 19 |     {
 20 |       "cell_type": "markdown",
 21 |       "metadata": {
 22 |         "id": "view-in-github",
 23 |         "colab_type": "text"
 24 |       },
 25 |       "source": [
 26 |         "<a href=\"https://colab.research.google.com/github/SamuelXJames/Signal-Denoising-Autoencoder/blob/master/CNN1D_Optimize%20v0.3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 27 |       ]
 28 |     },
 29 |     {
 30 |       "metadata": {
 31 |         "id": "ontfmefnFpH9",
 32 |         "colab_type": "code",
 33 |         "outputId": "cb39272a-87bf-403b-cba8-0e846594f65d",
 34 |         "colab": {
 35 |           "base_uri": "https://localhost:8080/",
 36 |           "height": 2536
 37 |         }
 38 |       },
 39 |       "cell_type": "code",
 40 |       "source": [
 41 |         "#Additional Packages to Install\n",
 42 |         "# !pip install pydrive\n",
 43 |         "# !pip install git+https://github.com/hyperopt/hyperopt.git\n",
 44 |         "# !pip install kopt\n",
 45 |         "# !pip install oauth2client\n",
 46 |         "# !pip install tensorboardcolab\n",
 47 |         "# !pip install pymongo\n",
 48 |         "# !pip install git+https://github.com/rthalley/dnspython.git\n",
 49 |         "# !pip install dnspython\n",
 50 |         "\n",
 51 |         "import numpy as np\n",
 52 |         "import pandas as pd\n",
 53 |         "import matplotlib.pyplot as plt\n",
 54 |         "import tensorflow as tf\n",
 55 |         "import pydrive\n",
 56 |         "import pymongo\n",
 57 |         "import dns\n",
 58 |         "from tensorboardcolab import *\n",
 59 |         "from random import randint\n",
 60 |         "from keras.layers import Dense,Conv1D,Input\n",
 61 |         "from keras.layers import MaxPooling1D,UpSampling1D\n",
 62 |         "from keras.layers import Activation,BatchNormalization\n",
 63 |         "from keras.models import Model\n",
 64 |         "from keras.optimizers import RMSprop\n",
 65 |         "from sklearn.preprocessing import MinMaxScaler\n",
 66 |         "from pydrive.auth import GoogleAuth\n",
 67 |         "from pydrive.drive import GoogleDrive\n",
 68 |         "from google.colab import auth\n",
 69 |         "from oauth2client.client import GoogleCredentials\n",
 70 |         "from kopt import CompileFN, KMongoTrials, test_fn\n",
 71 |         "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
 72 |         "from hyperopt.mongoexp import MongoTrials\n",
 73 |         "from google.colab import drive\n",
 74 |         "\n",
 75 |         "\n",
 76 |         "\n",
 77 |         "\n",
 78 |         "#MongoTrials\n",
 79 |         "# SRV_string  = ''\n",
 80 |         "# client = pymongo.MongoClient(SRV_string)\n",
 81 |         "# client.test_database\n",
 82 |         "# print(client)\n",
 83 |         "# hostname = ''\n",
 84 |         "# port = 27017\n",
 85 |         "\n",
 86 |         "\n",
 87 |         "\n",
 88 |         "#Mounting Google Drive\n",
 89 |         "# drive.mount('/content/gdrive')\n",
 90 |         "# #Authenticate/Create PyDrive client \n",
 91 |         "# auth.authenticate_user()\n",
 92 |         "# gauth = GoogleAuth()\n",
 93 |         "# gauth.credentials = GoogleCredentials.get_application_default()\n",
 94 |         "# drive = GoogleDrive(gauth)\n",
 95 |         "\n",
 96 |         "\n",
 97 |         "  \n",
 98 |         "#tbc=TensorBoardColab()\n",
 99 |         "batch_size = 1000\n",
100 |         "validation_split = 0.15\n",
101 |         "timesteps = 2000\n",
102 |         "features = 1\n",
103 |         "global timesteps\n",
104 |         "global features\n",
105 |         "\n",
106 |         "\n",
107 |         "#Generate Test/Train Data\n",
108 |         "def create_data(timesteps = 2000):\n",
109 |         "  length = 1\n",
110 |         "  dt = 0.0005\n",
111 |         "  noise_factor = 0.35\n",
112 |         "  data_1 = []\n",
113 |         "  data_noisy_1 = []\n",
114 |         "  for i in range(batch_size):\n",
115 |         "      freq = 30*np.random.random() + 1\n",
116 |         "      t = np.arange(0,length,dt)\n",
117 |         "      #signal = np.arange(-0.5*length,0.5*length,dt)\n",
118 |         "      signal = np.sin(2*np.pi*freq*t)\n",
119 |         "      noisy_signal = signal+noise_factor*np.random.rand(len(t))\n",
120 |         "      data_1.append(signal)\n",
121 |         "      data_noisy_1.append(noisy_signal)\n",
122 |         "  data_1 = np.array(data_1)\n",
123 |         "  data_noisy_1 = np.array(data_noisy_1)\n",
124 |         "  \n",
125 |         "  #Scale Data - Need a better scaler than MinMaxScaler\n",
126 |         "  scaler = MinMaxScaler(feature_range = (0,1))\n",
127 |         "  data_1  = np.vstack((data_1,data_noisy_1))\n",
128 |         "  data_1 = scaler.fit_transform(data_1)\n",
129 |         "  signal = data_1[0:batch_size][:]\n",
130 |         "  signal_noisy = data_1[batch_size:2*batch_size][:]\n",
131 |         "  \n",
132 |         "  #Input Shaping\n",
133 |         "  features = 1\n",
134 |         "  timesteps = len(signal[0])\n",
135 |         "  shape = (batch_size,timesteps,features)\n",
136 |         "  #Input shape should equal [samples, timesteps, features]\n",
137 |         "  x = signal.reshape(shape)\n",
138 |         "  y = signal_noisy.reshape(shape)\n",
139 |         "  \n",
140 |         "  split_point = int((1-validation_split)*batch_size)\n",
141 |         "  \n",
142 |         "  x_train = x[0:split_point,:,:]\n",
143 |         "  y_train = y[0:split_point,:,:]\n",
144 |         "  x_test = x[split_point:batch_size,:,:]\n",
145 |         "  y_test = y[split_point:batch_size][:]\n",
146 |         "\n",
147 |         "  return (x_train,y_train,features),(x_test,y_test)\n",
148 |         "\n",
149 |         "\n",
150 |         "\n",
151 |         "#Gnerate Model For Optimization\n",
152 |         "def create_autoencoder(train_data,max_filters = 32, min_filters = 8, layers = 3,\n",
153 |         "                      kernel_size = 3,filter_spacing = 'lin'):\n",
154 |         "  \n",
155 |         "  timesteps = 2000\n",
156 |         "  features = 1\n",
157 |         "  inputs = Input(shape = (timesteps,features),name = 'Input')\n",
158 |         "  encoded = inputs\n",
159 |         "  \n",
160 |         "  #Linear Filter Range\n",
161 |         "  if filter_spacing == 'lin':\n",
162 |         "    filter_range = np.linspace(max_filters,min_filters,layers,\n",
163 |         "                               dtype = 'int64')\n",
164 |         "  #Half Filter Range\n",
165 |         "  if filter_spacing == 'half':\n",
166 |         "    if max_filters%2 != 0:\n",
167 |         "      max_filters+=1\n",
168 |         "    if min_filters%2 != 0:\n",
169 |         "      min_filters+=1\n",
170 |         "    filter_range = [max_filters]\n",
171 |         "    x = max_filters\n",
172 |         "    while x> min_filters:\n",
173 |         "      x = x/2\n",
174 |         "      filter_range.append(x)\n",
175 |         "    if filter_range[-1] < min_filters:\n",
176 |         "      del filter_range[-1]\n",
177 |         "    filter_range = np.asarray(filter_range,dtype = 'int64')\n",
178 |         "  \n",
179 |         "  #Random Filter Range\n",
180 |         "  if filter_spacing == 'random':\n",
181 |         "    filter_range = [randint(min_filters, max_filters) for z in range(layers)]\n",
182 |         "    filter_range.sort(reverse= True)\n",
183 |         "    filter_range = np.asarray(filter_range)\n",
184 |         "    \n",
185 |         "  conv_name = ''\n",
186 |         "  count = 1\n",
187 |         "  for num_filters in filter_range: \n",
188 |         "    conv_name = 'Conv_Layer_No._{0}'.format(count)\n",
189 |         "    encoded = Conv1D(num_filters,kernel_size,padding = 'same',name = conv_name)(encoded)\n",
190 |         "    count+=1\n",
191 |         "    if(num_filters==max_filters):\n",
192 |         "      encoded = BatchNormalization()(encoded)\n",
193 |         "  \n",
194 |         "    encoded = Activation('relu')(encoded)\n",
195 |         "    encoded = MaxPooling1D(2,padding = 'same')(encoded)\n",
196 |         "  \n",
197 |         "  filter_range = np.sort(filter_range)\n",
198 |         "  for num_filters in filter_range: \n",
199 |         "    conv_name = 'Conv_Layer_No._{0}'.format(count)\n",
200 |         "    encoded = Conv1D(num_filters,kernel_size,padding = 'same',name = conv_name)(encoded)\n",
201 |         "    count+=1\n",
202 |         "    encoded = Activation('relu')(encoded)\n",
203 |         "    encoded = UpSampling1D(2)(encoded)\n",
204 |         "    \n",
205 |         "  decoded = Conv1D(1,kernel_size,activation = 'sigmoid',padding = 'same')(encoded)\n",
206 |         "  \n",
207 |         "  autoencoder = Model(inputs,decoded)\n",
208 |         "  autoencoder.summary()\n",
209 |         "  optimizer = RMSprop(lr=1e-3)\n",
210 |         "  autoencoder.compile(optimizer = optimizer, loss = 'mse')\n",
211 |         "  \n",
212 |         "  return autoencoder\n",
213 |         "\n",
214 |         "\n",
215 |         "\n",
216 |         "\n",
217 |         "\n",
218 |         "#HyperParameter Range\n",
219 |         "hyper_params = {\n",
220 |         "    \"data\":{\n",
221 |         "        'timesteps': 2000\n",
222 |         "        \n",
223 |         "        \n",
224 |         "    },\n",
225 |         "    \"model\":{\n",
226 |         "        \"max_filters\":hp.choice('mx_filters',np.arange(32,320,dtype = 'int32')),\n",
227 |         "        \"min_filters\":hp.choice('mn_filters',np.arange(4,32,dtype = 'int32')),\n",
228 |         "        'layers':hp.choice('ly',np.arange(1,3,dtype = 'int32')),\n",
229 |         "        'filter_spacing':hp.choice('fs',['lin','half','random'])\n",
230 |         "        \n",
231 |         "    },\n",
232 |         "    \"fit\":{\n",
233 |         "        'epochs': 1,\n",
234 |         "        'patience': 3\n",
235 |         "    }\n",
236 |         "}\n",
237 |         "\n",
238 |         "\n",
239 |         "\n",
240 |         "#NN Evaluation\n",
241 |         "db_name = 'Autoencoder'\n",
242 |         "exp_name ='exp_1'\n",
243 |         "objective = CompileFN(db_name,exp_name,\n",
244 |         "                      data_fn = create_data,\n",
245 |         "                      model_fn = create_autoencoder,\n",
246 |         "                      loss_metric = 'loss',\n",
247 |         "                      loss_metric_mode = 'min',\n",
248 |         "                      valid_split = None,\n",
249 |         "                      save_model = 'best',\n",
250 |         "                      save_results = True)\n",
251 |         "                      \n",
252 |         "                      \n",
253 |         "\n",
254 |         "\n",
255 |         "\n",
256 |         "trials = Trials()\n",
257 |         "best = fmin(objective, hyper_params, trials=trials, algo=tpe.suggest, max_evals=2)\n",
258 |         "\n",
259 |         "trial_data = trials.results\n",
260 |         "\n",
261 |         "#Organize trials into table\n",
262 |         "val_loss = []\n",
263 |         "loss = []\n",
264 |         "filter_spacing = []\n",
265 |         "max_filters = []\n",
266 |         "min_filters = []\n",
267 |         "layers = []\n",
268 |         "for i in range(len(trial_data)):\n",
269 |         "    trial = trial_data[i]\n",
270 |         "    val_loss.append('%.6f'%(trial['history']['loss']['val_loss'][0])) \n",
271 |         "    loss.append('%.6f'%(trial['history']['loss']['loss'][0]))\n",
272 |         "    filter_spacing.append(trial['param']['model']['filter_spacing'])\n",
273 |         "    max_filters.append(trial['param']['model']['max_filters'])\n",
274 |         "    min_filters.append(trial['param']['model']['min_filters'])\n",
275 |         "    layers.append(trial['param']['model']['layers'])\n",
276 |         "\n",
277 |         "\n",
278 |         "new_dict = {'val_loss':val_loss,'loss':loss,\n",
279 |         "            'f_spacing':filter_spacing,'max_f':max_filters,'min_f':min_filters}\n",
280 |         "\n",
281 |         "data_frame = pd.DataFrame.from_dict(new_dict, orient='columns')\n",
282 |         "print(data_frame)\n",
283 |         "    \n",
284 |         "\n",
285 |         "#Mongo Trials - Cont'd\n",
286 |         "#trials = MongoTrials(SRV_string,exp_key = 'Optimization_1')\n",
287 |         "#trials = MongoTrials('mongo://{0}:{1}/{2}/jobs'.format(hostname,port,db_name), exp_key='exp1')\n",
288 |         "#trials = KMongoTrials(db_name, exp_name,\n",
289 |         "#                      ip=hostname,\n",
290 |         "#                      port=port)\n",
291 |         "\n",
292 |         "\n",
293 |         "\n",
294 |         "\n",
295 |         "\n",
296 |         " \n",
297 |         "#Save Model to Google Drive\n",
298 |         "# model_file = drive.CreateFile({'title' : 'CNN1D_v1.0.h5'})\n",
299 |         "# model_file.SetContentFile('model.h5')\n",
300 |         "# model_file.Upload()\n",
301 |         "# drive.CreateFile({'id': model_file.get('id')})\n",
302 |         "  \n",
303 |         "  \n",
304 |         "  \n",
305 |         "  \n",
306 |         "  \n",
307 |         "  "
308 |       ],
309 |       "execution_count": 0,
310 |       "outputs": [
311 |         {
312 |           "output_type": "stream",
313 |           "text": [
314 |             "2018-12-30 05:37:42,783 [INFO] tpe_transform took 0.022910 seconds\n",
315 |             "2018-12-30 05:37:42,785 [INFO] TPE using 0 trials\n",
316 |             "2018-12-30 05:37:42,799 [INFO] Load data...\n"
317 |           ],
318 |           "name": "stderr"
319 |         },
320 |         {
321 |           "output_type": "stream",
322 |           "text": [
323 |             "MongoClient(host=['autoencoderoptimization-shard-00-02-08kaz.mongodb.net:27017', 'autoencoderoptimization-shard-00-00-08kaz.mongodb.net:27017', 'autoencoderoptimization-shard-00-01-08kaz.mongodb.net:27017'], document_class=dict, tz_aware=False, connect=True, authsource='admin', replicaset='AutoEncoderOptimization-shard-0', ssl=True, retrywrites=True)\n"
324 |           ],
325 |           "name": "stdout"
326 |         },
327 |         {
328 |           "output_type": "stream",
329 |           "text": [
330 |             "2018-12-30 05:37:43,636 [INFO] Fit...\n"
331 |           ],
332 |           "name": "stderr"
333 |         },
334 |         {
335 |           "output_type": "stream",
336 |           "text": [
337 |             "_________________________________________________________________\n",
338 |             "Layer (type)                 Output Shape              Param #   \n",
339 |             "=================================================================\n",
340 |             "Input (InputLayer)           (None, 2000, 1)           0         \n",
341 |             "_________________________________________________________________\n",
342 |             "Conv_Layer_No._1 (Conv1D)    (None, 2000, 44)          176       \n",
343 |             "_________________________________________________________________\n",
344 |             "batch_normalization_5 (Batch (None, 2000, 44)          176       \n",
345 |             "_________________________________________________________________\n",
346 |             "activation_17 (Activation)   (None, 2000, 44)          0         \n",
347 |             "_________________________________________________________________\n",
348 |             "max_pooling1d_9 (MaxPooling1 (None, 1000, 44)          0         \n",
349 |             "_________________________________________________________________\n",
350 |             "Conv_Layer_No._2 (Conv1D)    (None, 1000, 22)          2926      \n",
351 |             "_________________________________________________________________\n",
352 |             "activation_18 (Activation)   (None, 1000, 22)          0         \n",
353 |             "_________________________________________________________________\n",
354 |             "max_pooling1d_10 (MaxPooling (None, 500, 22)           0         \n",
355 |             "_________________________________________________________________\n",
356 |             "Conv_Layer_No._3 (Conv1D)    (None, 500, 11)           737       \n",
357 |             "_________________________________________________________________\n",
358 |             "activation_19 (Activation)   (None, 500, 11)           0         \n",
359 |             "_________________________________________________________________\n",
360 |             "max_pooling1d_11 (MaxPooling (None, 250, 11)           0         \n",
361 |             "_________________________________________________________________\n",
362 |             "Conv_Layer_No._4 (Conv1D)    (None, 250, 5)            170       \n",
363 |             "_________________________________________________________________\n",
364 |             "activation_20 (Activation)   (None, 250, 5)            0         \n",
365 |             "_________________________________________________________________\n",
366 |             "max_pooling1d_12 (MaxPooling (None, 125, 5)            0         \n",
367 |             "_________________________________________________________________\n",
368 |             "Conv_Layer_No._5 (Conv1D)    (None, 125, 5)            80        \n",
369 |             "_________________________________________________________________\n",
370 |             "activation_21 (Activation)   (None, 125, 5)            0         \n",
371 |             "_________________________________________________________________\n",
372 |             "up_sampling1d_9 (UpSampling1 (None, 250, 5)            0         \n",
373 |             "_________________________________________________________________\n",
374 |             "Conv_Layer_No._6 (Conv1D)    (None, 250, 11)           176       \n",
375 |             "_________________________________________________________________\n",
376 |             "activation_22 (Activation)   (None, 250, 11)           0         \n",
377 |             "_________________________________________________________________\n",
378 |             "up_sampling1d_10 (UpSampling (None, 500, 11)           0         \n",
379 |             "_________________________________________________________________\n",
380 |             "Conv_Layer_No._7 (Conv1D)    (None, 500, 22)           748       \n",
381 |             "_________________________________________________________________\n",
382 |             "activation_23 (Activation)   (None, 500, 22)           0         \n",
383 |             "_________________________________________________________________\n",
384 |             "up_sampling1d_11 (UpSampling (None, 1000, 22)          0         \n",
385 |             "_________________________________________________________________\n",
386 |             "Conv_Layer_No._8 (Conv1D)    (None, 1000, 44)          2948      \n",
387 |             "_________________________________________________________________\n",
388 |             "activation_24 (Activation)   (None, 1000, 44)          0         \n",
389 |             "_________________________________________________________________\n",
390 |             "up_sampling1d_12 (UpSampling (None, 2000, 44)          0         \n",
391 |             "_________________________________________________________________\n",
392 |             "conv1d_5 (Conv1D)            (None, 2000, 1)           133       \n",
393 |             "=================================================================\n",
394 |             "Total params: 8,270\n",
395 |             "Trainable params: 8,182\n",
396 |             "Non-trainable params: 88\n",
397 |             "_________________________________________________________________\n",
398 |             "['loss']\n",
399 |             "Train on 850 samples, validate on 150 samples\n",
400 |             "Epoch 1/1\n",
401 |             " - 9s - loss: 0.0420 - val_loss: 0.0162\n"
402 |           ],
403 |           "name": "stdout"
404 |         },
405 |         {
406 |           "output_type": "stream",
407 |           "text": [
408 |             "2018-12-30 05:38:11,890 [INFO] Evaluate...\n",
409 |             "2018-12-30 05:38:15,225 [INFO] Done!\n",
410 |             "2018-12-30 05:38:15,253 [INFO] tpe_transform took 0.026401 seconds\n",
411 |             "2018-12-30 05:38:15,254 [INFO] TPE using 1/1 trials with best loss 0.016185\n",
412 |             "2018-12-30 05:38:15,265 [INFO] Load data...\n",
413 |             "2018-12-30 05:38:16,061 [INFO] Fit...\n"
414 |           ],
415 |           "name": "stderr"
416 |         },
417 |         {
418 |           "output_type": "stream",
419 |           "text": [
420 |             "_________________________________________________________________\n",
421 |             "Layer (type)                 Output Shape              Param #   \n",
422 |             "=================================================================\n",
423 |             "Input (InputLayer)           (None, 2000, 1)           0         \n",
424 |             "_________________________________________________________________\n",
425 |             "Conv_Layer_No._1 (Conv1D)    (None, 2000, 290)         1160      \n",
426 |             "_________________________________________________________________\n",
427 |             "batch_normalization_6 (Batch (None, 2000, 290)         1160      \n",
428 |             "_________________________________________________________________\n",
429 |             "activation_25 (Activation)   (None, 2000, 290)         0         \n",
430 |             "_________________________________________________________________\n",
431 |             "max_pooling1d_13 (MaxPooling (None, 1000, 290)         0         \n",
432 |             "_________________________________________________________________\n",
433 |             "Conv_Layer_No._2 (Conv1D)    (None, 1000, 145)         126295    \n",
434 |             "_________________________________________________________________\n",
435 |             "activation_26 (Activation)   (None, 1000, 145)         0         \n",
436 |             "_________________________________________________________________\n",
437 |             "max_pooling1d_14 (MaxPooling (None, 500, 145)          0         \n",
438 |             "_________________________________________________________________\n",
439 |             "Conv_Layer_No._3 (Conv1D)    (None, 500, 72)           31392     \n",
440 |             "_________________________________________________________________\n",
441 |             "activation_27 (Activation)   (None, 500, 72)           0         \n",
442 |             "_________________________________________________________________\n",
443 |             "max_pooling1d_15 (MaxPooling (None, 250, 72)           0         \n",
444 |             "_________________________________________________________________\n",
445 |             "Conv_Layer_No._4 (Conv1D)    (None, 250, 36)           7812      \n",
446 |             "_________________________________________________________________\n",
447 |             "activation_28 (Activation)   (None, 250, 36)           0         \n",
448 |             "_________________________________________________________________\n",
449 |             "max_pooling1d_16 (MaxPooling (None, 125, 36)           0         \n",
450 |             "_________________________________________________________________\n",
451 |             "Conv_Layer_No._5 (Conv1D)    (None, 125, 36)           3924      \n",
452 |             "_________________________________________________________________\n",
453 |             "activation_29 (Activation)   (None, 125, 36)           0         \n",
454 |             "_________________________________________________________________\n",
455 |             "up_sampling1d_13 (UpSampling (None, 250, 36)           0         \n",
456 |             "_________________________________________________________________\n",
457 |             "Conv_Layer_No._6 (Conv1D)    (None, 250, 72)           7848      \n",
458 |             "_________________________________________________________________\n",
459 |             "activation_30 (Activation)   (None, 250, 72)           0         \n",
460 |             "_________________________________________________________________\n",
461 |             "up_sampling1d_14 (UpSampling (None, 500, 72)           0         \n",
462 |             "_________________________________________________________________\n",
463 |             "Conv_Layer_No._7 (Conv1D)    (None, 500, 145)          31465     \n",
464 |             "_________________________________________________________________\n",
465 |             "activation_31 (Activation)   (None, 500, 145)          0         \n",
466 |             "_________________________________________________________________\n",
467 |             "up_sampling1d_15 (UpSampling (None, 1000, 145)         0         \n",
468 |             "_________________________________________________________________\n",
469 |             "Conv_Layer_No._8 (Conv1D)    (None, 1000, 290)         126440    \n",
470 |             "_________________________________________________________________\n",
471 |             "activation_32 (Activation)   (None, 1000, 290)         0         \n",
472 |             "_________________________________________________________________\n",
473 |             "up_sampling1d_16 (UpSampling (None, 2000, 290)         0         \n",
474 |             "_________________________________________________________________\n",
475 |             "conv1d_6 (Conv1D)            (None, 2000, 1)           871       \n",
476 |             "=================================================================\n",
477 |             "Total params: 338,367\n",
478 |             "Trainable params: 337,787\n",
479 |             "Non-trainable params: 580\n",
480 |             "_________________________________________________________________\n",
481 |             "['loss']\n",
482 |             "Train on 850 samples, validate on 150 samples\n",
483 |             "Epoch 1/1\n",
484 |             " - 13s - loss: 0.0350 - val_loss: 0.0081\n"
485 |           ],
486 |           "name": "stdout"
487 |         },
488 |         {
489 |           "output_type": "stream",
490 |           "text": [
491 |             "2018-12-30 05:38:52,246 [INFO] Evaluate...\n",
492 |             "2018-12-30 05:38:57,060 [INFO] Done!\n"
493 |           ],
494 |           "name": "stderr"
495 |         },
496 |         {
497 |           "output_type": "stream",
498 |           "text": [
499 |             "[{'loss': 0.01618463173508644, 'status': 'ok', 'eval': {'loss': 0.01618463173508644}, 'param': {'data': {'timesteps': 2000}, 'fit': {'epochs': 1, 'patience': 3, 'batch_size': 32, 'early_stop_monitor': 'val_loss'}, 'model': {'filter_spacing': 'half', 'layers': 1, 'max_filters': 43, 'min_filters': 4}}, 'path': {'model': '/root/.kopt/data//Autoencoder/exp_1//train_models/c71a4995-ffda-442f-bc8c-a1561ba3e901.h5', 'results': '/root/.kopt/data//Autoencoder/exp_1//train_models/c71a4995-ffda-442f-bc8c-a1561ba3e901.json'}, 'name': {'data': 'create_data', 'model': 'create_autoencoder', 'optim_metric': 'loss', 'optim_metric_mode': 'loss'}, 'history': {'params': {'batch_size': 32, 'epochs': 1, 'steps': None, 'samples': 850, 'verbose': 2, 'do_validation': True, 'metrics': ['loss', 'val_loss']}, 'loss': {'epoch': [0], 'val_loss': [0.016184631971021494], 'loss': [0.04198666546274634]}}, 'time': {'start': '2018-12-30 05:37:42.798942', 'end': '2018-12-30 05:38:15.224955', 'duration': {'total': 32.426013, 'dataload': 0.25152, 'training': 32.174493}}}, {'loss': 0.008108320645987988, 'status': 'ok', 'eval': {'loss': 0.008108320645987988}, 'param': {'data': {'timesteps': 2000}, 'fit': {'epochs': 1, 'patience': 3, 'batch_size': 32, 'early_stop_monitor': 'val_loss'}, 'model': {'filter_spacing': 'half', 'layers': 1, 'max_filters': 290, 'min_filters': 25}}, 'path': {'model': '/root/.kopt/data//Autoencoder/exp_1//train_models/2f78e313-d03d-4ea2-af98-5d8818884475.h5', 'results': '/root/.kopt/data//Autoencoder/exp_1//train_models/2f78e313-d03d-4ea2-af98-5d8818884475.json'}, 'name': {'data': 'create_data', 'model': 'create_autoencoder', 'optim_metric': 'loss', 'optim_metric_mode': 'loss'}, 'history': {'params': {'batch_size': 32, 'epochs': 1, 'steps': None, 'samples': 850, 'verbose': 2, 'do_validation': True, 'metrics': ['loss', 'val_loss']}, 'loss': {'epoch': [0], 'val_loss': [0.00810831895718972], 'loss': [0.03497549022602684]}}, 'time': {'start': '2018-12-30 05:38:15.265511', 'end': '2018-12-30 05:38:57.060483', 'duration': {'total': 41.794972, 'dataload': 0.219787, 'training': 41.575185}}}]\n",
500 |             "  f_spacing      loss  max_f  min_f  val_loss\n",
501 |             "0      half  0.041987     43      4  0.016185\n",
502 |             "1      half  0.034975    290     25  0.008108\n"
503 |           ],
504 |           "name": "stdout"
505 |         }
506 |       ]
507 |     }
508 |   ]
509 | }


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/README.md:
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1 | # Signal Denoising Autoencoder
2 | A Deep Denoising Autencoder for Time Dependent Signals
3 | 


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