├── (Keras) Cornell Movie Review Dataset.ipynb ├── (Keras) Cornell Movie Review Dataset.py ├── (Keras) IMDB Dataset.ipynb ├── (Keras) IMDB Dataset.py ├── (PyTorch) Cornell Movie Review Dataset.ipynb ├── (PyTorch) Cornell Movie Review Dataset.py ├── (PyTorch) IMDB Dataset.ipynb ├── (PyTorch) IMDB Dataset.py ├── .gitignore ├── Dockerfile ├── README.md ├── data ├── scaledata.README.1.0.txt └── scaledata │ ├── Dennis+Schwartz │ ├── id.Dennis+Schwartz │ ├── label.3class.Dennis+Schwartz │ ├── label.4class.Dennis+Schwartz │ ├── rating.Dennis+Schwartz │ └── subj.Dennis+Schwartz │ ├── James+Berardinelli │ ├── id.James+Berardinelli │ ├── label.3class.James+Berardinelli │ ├── label.4class.James+Berardinelli │ ├── rating.James+Berardinelli │ └── subj.James+Berardinelli │ ├── Scott+Renshaw │ ├── id.Scott+Renshaw │ ├── label.3class.Scott+Renshaw │ ├── label.4class.Scott+Renshaw │ ├── rating.Scott+Renshaw │ └── subj.Scott+Renshaw │ └── Steve+Rhodes │ ├── id.Steve+Rhodes │ ├── label.3class.Steve+Rhodes │ ├── label.4class.Steve+Rhodes │ ├── rating.Steve+Rhodes │ └── subj.Steve+Rhodes ├── embed_regularize.py ├── jupyter_notebook_config.py ├── locked_dropout.py ├── weight_drop.py ├── yaringal_callbacks.py └── yaringal_dataset.py /(Keras) Cornell Movie Review Dataset.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # # (Keras) Cornell Movie Review Dataset 5 | 6 | # Based on https://github.com/yaringal/BayesianRNN/blob/master/Example/sentiment_lstm_regression.py 7 | 8 | # In[1]: 9 | 10 | 11 | import numpy as np 12 | from tensorflow.contrib.keras.python.keras.optimizers import SGD, RMSprop, Adagrad 13 | from tensorflow.contrib.keras.python.keras.models import Sequential 14 | from tensorflow.contrib.keras.python.keras.layers.core import Dense, Dropout 15 | from tensorflow.contrib.keras.python.keras.layers.embeddings import Embedding 16 | from tensorflow.contrib.keras.python.keras.layers.recurrent import LSTM, GRU, SimpleRNN 17 | from tensorflow.contrib.keras.python.keras.regularizers import l2 18 | from tensorflow.contrib.keras.python.keras.optimizers import Adam 19 | from sklearn.metrics import mean_squared_error 20 | import matplotlib.pyplot as plt 21 | 22 | from yaringal_callbacks import ModelTest 23 | from yaringal_dataset import loader 24 | 25 | get_ipython().magic('matplotlib inline') 26 | plt.style.use('fivethirtyeight') 27 | plt.rcParams["figure.figsize"] = (8, 5) 28 | 29 | # Global params: 30 | NB_WORDS = 20000 31 | SKIP_TOP = 0 32 | TEST_SPLIT = 0.2 33 | INIT_SEED = 2017 34 | GLOBAL_SEED = 2018 35 | MAXLEN = 200 36 | BATCH_SIZE = 128 37 | TEST_BATCH_SIZE = 512 38 | 39 | 40 | # In[2]: 41 | 42 | 43 | dataset = loader(INIT_SEED, MAXLEN, NB_WORDS, SKIP_TOP, TEST_SPLIT) 44 | 45 | X_train, X_test, Y_train, Y_test = dataset.X_train, dataset.X_test, dataset.Y_train, dataset.Y_test 46 | mean_y_train, std_y_train = dataset.mean_y_train, dataset.std_y_train 47 | 48 | 49 | # In[3]: 50 | 51 | 52 | def get_model(idrop=0.2, edrop=0.1, odrop=0.25, rdrop=0.2, weight_decay=1e-4, lr=1e-3): 53 | model = Sequential() 54 | model.add(Embedding(NB_WORDS, 128, embeddings_regularizer=l2(weight_decay), 55 | input_length=MAXLEN)) 56 | if edrop: 57 | model.add(Dropout(edrop)) 58 | model.add(LSTM(128, kernel_regularizer=l2(weight_decay), recurrent_regularizer=l2(weight_decay), 59 | bias_regularizer=l2(weight_decay), dropout=idrop, recurrent_dropout=rdrop)) 60 | if odrop: 61 | model.add(Dropout(odrop)) 62 | model.add(Dense(1, kernel_regularizer=l2(weight_decay), 63 | bias_regularizer=l2(weight_decay))) 64 | optimizer = Adam(lr) 65 | model.compile(loss='mse', metrics=["mse"], optimizer=optimizer) 66 | return model 67 | 68 | 69 | # ## Normal Variational LSTM (w/o Embedding Dropout) 70 | 71 | # In[4]: 72 | 73 | 74 | print('Build model...') 75 | model = get_model(rdrop=0.25, odrop=0.25, edrop=0, idrop=0.25, weight_decay=1e-4, lr=1e-3) 76 | 77 | 78 | # In[5]: 79 | 80 | 81 | modeltest_1 = ModelTest(X_test, Yt=Y_test, 82 | test_every_X_epochs=2, verbose=0, T=10, 83 | mean_y_train=mean_y_train, std_y_train=std_y_train, 84 | loss='euclidean', batch_size=TEST_BATCH_SIZE) 85 | 86 | 87 | # In[6]: 88 | 89 | 90 | history_1 = model.fit( 91 | X_train, Y_train, 92 | verbose=2, 93 | shuffle=True, 94 | # validation_data=[X_test, Y_test], 95 | batch_size=BATCH_SIZE, epochs=200, callbacks=[modeltest_1]) 96 | 97 | 98 | # In[24]: 99 | 100 | 101 | print("Best RMSE: {:.4f} Best Epoch: {}".format( 102 | np.min([x[1] ** 0.5 for x in modeltest_1.history]), 103 | (np.argmin([x[1] ** 0.5 for x in modeltest_1.history]) + 1)*2 104 | )) 105 | 106 | 107 | # ## Standard LSTM w/o Dropout 108 | 109 | # In[10]: 110 | 111 | 112 | print('Build model...') 113 | model = get_model(edrop=0, rdrop=0, odrop=0, idrop=0, weight_decay=1e-10, lr=1e-3) 114 | 115 | 116 | # In[11]: 117 | 118 | 119 | modeltest_2 = ModelTest(X_test, Yt=Y_test, 120 | test_every_X_epochs=2, verbose=0, T=1, 121 | mean_y_train=mean_y_train, std_y_train=std_y_train, 122 | loss='euclidean', batch_size=TEST_BATCH_SIZE) 123 | 124 | 125 | # In[12]: 126 | 127 | 128 | history_2 = model.fit( 129 | X_train, Y_train, 130 | verbose=2, 131 | shuffle=True, 132 | # validation_data=[X_test, Y_test], 133 | batch_size=BATCH_SIZE, epochs=200, callbacks=[modeltest_2] 134 | ) 135 | 136 | 137 | # In[25]: 138 | 139 | 140 | print("Best RMSE: {:.4f} Best Epoch: {}".format( 141 | np.min([x[1] ** 0.5 for x in modeltest_2.history]), 142 | (np.argmin([x[1] ** 0.5 for x in modeltest_2.history]) + 1)*2 143 | )) 144 | 145 | 146 | # ## LSTM with Standard Dropout (different mask at differnt time steps) 147 | 148 | # In[13]: 149 | 150 | 151 | print('Build model...') 152 | model = get_model(edrop=0.3, rdrop=0, odrop=0.3, idrop=0, weight_decay=1e-4, lr=1e-3) 153 | 154 | 155 | # In[14]: 156 | 157 | 158 | modeltest_3 = ModelTest(X_test, Yt=Y_test, 159 | test_every_X_epochs=2, verbose=0, T=10, 160 | mean_y_train=mean_y_train, std_y_train=std_y_train, 161 | loss='euclidean', batch_size=TEST_BATCH_SIZE) 162 | 163 | 164 | # In[15]: 165 | 166 | 167 | history_3 = model.fit( 168 | X_train, Y_train, 169 | verbose=2, 170 | shuffle=True, 171 | # validation_data=[X_test, Y_test], 172 | batch_size=BATCH_SIZE, epochs=200, callbacks=[modeltest_3]) 173 | 174 | 175 | # In[26]: 176 | 177 | 178 | print("Best RMSE: {:.4f} Best Epoch: {}".format( 179 | np.min([x[1] ** 0.5 for x in modeltest_3.history]), 180 | (np.argmin([x[1] ** 0.5 for x in modeltest_3.history]) + 1)*2 181 | )) 182 | 183 | 184 | # ## Visualizations 185 | 186 | # In[40]: 187 | 188 | 189 | plt.figure(figsize=(12, 4)) 190 | plt.subplot(1, 2, 1) 191 | plt.title("Raw MSE Comparison - Training Set") 192 | plt.plot(np.arange(len(history_1.history["mean_squared_error"])), 193 | history_1.history["mean_squared_error"], label="variational") 194 | plt.plot(np.arange(len(history_2.history["mean_squared_error"])), 195 | history_2.history["mean_squared_error"], "g-", label="no dropout") 196 | plt.plot(np.arange(len(history_3.history["mean_squared_error"])), 197 | history_3.history["mean_squared_error"], "y-", label="naive dropout") 198 | plt.legend(loc='best') 199 | plt.xlabel("epochs") 200 | plt.ylabel("Raw MSE") 201 | plt.subplot(1, 2, 2) 202 | plt.title("(MC - Approx) Histogram") 203 | plt.hist([x[1] ** 0.5 - x[0] ** 0.5 for x in modeltest_1.history], alpha=0.5, label="varational") 204 | plt.hist([x[1] ** 0.5 - x[0] ** 0.5 for x in modeltest_3.history], alpha=0.5, label="navie dropout") 205 | plt.legend(loc='best') 206 | plt.xlabel("Difference in Raw MSE") 207 | plt.ylabel("Count") 208 | plt.xticks(fontsize=8, rotation=0) 209 | 210 | 211 | # In[39]: 212 | 213 | 214 | plt.title("RMSE Comparison - Validation Set") 215 | plt.plot(np.arange(len(modeltest_1.history)), [x[1] ** 0.5 for x in modeltest_1.history], "b-", label="variational(mc)") 216 | plt.plot(np.arange(len(modeltest_2.history)), [x[0] ** 0.5 for x in modeltest_2.history], "g-", label="no dropout") 217 | plt.plot(np.arange(len(modeltest_3.history)), [x[1] ** 0.5 for x in modeltest_3.history], "y-", label="naive dropout(mc)") 218 | plt.legend(loc='best') 219 | plt.xlabel("epochs") 220 | plt.ylabel("RMSE") 221 | 222 | -------------------------------------------------------------------------------- /(Keras) IMDB Dataset.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # # (Keras) IMDB Dataset 5 | 6 | # In[1]: 7 | 8 | 9 | import numpy as np 10 | from tensorflow.contrib.keras.python.keras.optimizers import SGD, RMSprop, Adagrad 11 | from tensorflow.contrib.keras.python.keras.models import Sequential 12 | from tensorflow.contrib.keras.python.keras.layers.core import Dense, Dropout 13 | from tensorflow.contrib.keras.python.keras.layers.embeddings import Embedding 14 | from tensorflow.contrib.keras.python.keras.layers.recurrent import LSTM, GRU, SimpleRNN 15 | from tensorflow.contrib.keras.python.keras.regularizers import l2 16 | from tensorflow.contrib.keras.python.keras.optimizers import Adam 17 | from tensorflow.contrib.keras.python.keras.preprocessing import sequence 18 | from tensorflow.contrib.keras.python.keras.datasets import imdb 19 | from sklearn.metrics import mean_squared_error 20 | import matplotlib.pyplot as plt 21 | import matplotlib.ticker as ticker 22 | 23 | from yaringal_callbacks import ModelTest 24 | from yaringal_dataset import loader 25 | 26 | get_ipython().magic('matplotlib inline') 27 | plt.style.use('fivethirtyeight') 28 | plt.rcParams["figure.figsize"] = (8, 5) 29 | 30 | # Global params: 31 | NB_WORDS = 20000 32 | SKIP_TOP = 0 33 | TEST_SPLIT = 0.2 34 | INIT_SEED = 2017 35 | GLOBAL_SEED = 2018 36 | MAXLEN = 80 37 | BATCH_SIZE = 128 38 | TEST_BATCH_SIZE = 512 39 | WEIGHT_DECAY = 1e-4 40 | 41 | 42 | # In[2]: 43 | 44 | 45 | np.random.seed(100) 46 | 47 | 48 | # In[3]: 49 | 50 | 51 | (X_train, Y_train), (X_test, Y_test) = imdb.load_data(num_words=NB_WORDS) 52 | print(len(X_train), 'train sequences') 53 | print(len(X_test), 'test sequences') 54 | print('Pad sequences (samples x time)') 55 | X_train = sequence.pad_sequences(X_train, maxlen=MAXLEN) 56 | X_test = sequence.pad_sequences(X_test, maxlen=MAXLEN) 57 | print('x_train shape:', X_train.shape) 58 | print('x_test shape:', X_test.shape) 59 | 60 | 61 | # In[4]: 62 | 63 | 64 | def get_model(idrop=0.2, edrop=0.1, odrop=0.25, rdrop=0.2, weight_decay=WEIGHT_DECAY): 65 | model = Sequential() 66 | model.add(Embedding(NB_WORDS, 128, embeddings_regularizer=l2(weight_decay), 67 | input_length=MAXLEN)) # , batch_input_shape=(batch_size, maxlen))) 68 | if edrop: 69 | model.add(Dropout(edrop)) 70 | model.add(LSTM(128, kernel_regularizer=l2(weight_decay), recurrent_regularizer=l2(weight_decay), 71 | bias_regularizer=l2(weight_decay), dropout=idrop, recurrent_dropout=rdrop)) 72 | if odrop: 73 | model.add(Dropout(odrop)) 74 | model.add(Dense(1, kernel_regularizer=l2(weight_decay), 75 | bias_regularizer=l2(weight_decay), activation='sigmoid')) 76 | optimizer = Adam(1e-3) 77 | model.compile(loss='binary_crossentropy', metrics=["binary_accuracy"], optimizer=optimizer) 78 | return model 79 | 80 | 81 | # ## Normal Variational LSTM (w/o Embedding Dropout) 82 | # All models in this notebook do not have embedding dropout as Keras does not have such layer. 83 | 84 | # In[5]: 85 | 86 | 87 | print('Build model...') 88 | model = get_model(idrop=0.25, edrop=0, odrop=0.25, rdrop=0.25, weight_decay=1e-4) 89 | 90 | 91 | # In[6]: 92 | 93 | 94 | modeltest_1 = ModelTest(X_test, Yt=Y_test, 95 | test_every_X_epochs=1, verbose=0, 96 | loss='binary', batch_size=TEST_BATCH_SIZE) 97 | 98 | 99 | # In[7]: 100 | 101 | 102 | history_1 = model.fit( 103 | X_train, Y_train, 104 | verbose=2, 105 | shuffle=True, 106 | # validation_data=[X_test, Y_test], 107 | batch_size=BATCH_SIZE, epochs=20, callbacks=[modeltest_1]) 108 | 109 | 110 | # In[11]: 111 | 112 | 113 | best_epoch = np.argmin([x[1] for x in modeltest_1.history[:18]]) + 1 114 | print("Best Loss: {:.4f} Acc: {:.2f}% Best Epoch: {}".format( 115 | modeltest_1.history[best_epoch-1][1], 116 | modeltest_1.history[best_epoch-1][3] * 100, 117 | best_epoch 118 | )) 119 | 120 | 121 | # In[12]: 122 | 123 | 124 | plt.title("Log Loss Comparison") 125 | plt.plot(np.arange(len(modeltest_1.history)), [x[0] for x in modeltest_1.history], label="std") 126 | plt.plot(np.arange(len(modeltest_1.history)), [x[1] for x in modeltest_1.history], "g-", label="mc") 127 | plt.legend(loc='best') 128 | 129 | 130 | # In[13]: 131 | 132 | 133 | plt.title("Accuracy Comparison") 134 | plt.plot(np.arange(0, len(modeltest_1.history)), [x[2] for x in modeltest_1.history], label="std") 135 | plt.plot(np.arange(0, len(modeltest_1.history)), [x[3] for x in modeltest_1.history], "g-", label="mc") 136 | plt.legend(loc='best') 137 | 138 | 139 | # ## Standard LSTM 140 | # I choose to keep a very low weight decay because assigning zero seems to cause some problems. 141 | 142 | # In[14]: 143 | 144 | 145 | print('Build model...') 146 | model = get_model(edrop=0, rdrop=0, odrop=0, idrop=0, weight_decay=1e-10) 147 | 148 | 149 | # In[15]: 150 | 151 | 152 | modeltest_2 = ModelTest(X_test, Yt=Y_test, 153 | test_every_X_epochs=1, verbose=0, T=1, 154 | loss='binary', batch_size=TEST_BATCH_SIZE) 155 | 156 | 157 | # In[17]: 158 | 159 | 160 | history_2 = model.fit( 161 | X_train, Y_train, 162 | verbose=2, 163 | shuffle=True, 164 | # validation_data=[X_test, Y_test], 165 | batch_size=BATCH_SIZE, epochs=20, callbacks=[modeltest_2]) 166 | 167 | 168 | # In[25]: 169 | 170 | 171 | best_epoch = np.argmin([x[1] for x in modeltest_2.history]) + 1 172 | print("Best Loss: {:.4f} Acc: {:.2f}% Best Epoch: {}".format( 173 | modeltest_2.history[best_epoch-1][1], 174 | modeltest_2.history[best_epoch-1][3] * 100, 175 | best_epoch 176 | )) 177 | 178 | 179 | # ## LSTM with Standard Dropout (different mask at differnt time steps) 180 | 181 | # In[20]: 182 | 183 | 184 | print('Build model...') 185 | model = get_model(edrop=0.25, rdrop=0, odrop=0.25, idrop=0, weight_decay=1e-4) 186 | 187 | 188 | # In[21]: 189 | 190 | 191 | modeltest_3 = ModelTest(X_test, Yt=Y_test, 192 | test_every_X_epochs=1, verbose=0, T=10, 193 | loss='binary', batch_size=TEST_BATCH_SIZE) 194 | 195 | 196 | # In[22]: 197 | 198 | 199 | history_3 =model.fit( 200 | X_train, Y_train, 201 | verbose=2, 202 | shuffle=True, 203 | # validation_data=[X_test, Y_test], 204 | batch_size=BATCH_SIZE, epochs=20, callbacks=[modeltest_3]) 205 | 206 | 207 | # In[24]: 208 | 209 | 210 | best_epoch = np.argmin([x[1] for x in modeltest_3.history[:19]]) + 1 211 | print("Best Loss: {:.4f} Acc: {:.2f}% Best Epoch: {}".format( 212 | modeltest_3.history[best_epoch-1][1], 213 | modeltest_3.history[best_epoch-1][3] * 100, 214 | best_epoch 215 | )) 216 | 217 | 218 | # ## Visualizations 219 | 220 | # In[40]: 221 | 222 | 223 | bins = np.arange(-0.1, 0.035, 0.01) 224 | 225 | 226 | # In[53]: 227 | 228 | 229 | len(history_2.history["binary_accuracy"]) 230 | 231 | 232 | # In[54]: 233 | 234 | 235 | plt.figure(figsize=(12, 4)) 236 | plt.subplot(1, 2, 1) 237 | plt.title("Accuracy Comparison - Training Set") 238 | plt.plot(np.arange(len(history_2.history["binary_accuracy"])), 239 | np.array(history_1.history["binary_accuracy"][:20]) * 100, label="variational") 240 | plt.plot(np.arange(len(history_2.history["binary_accuracy"])), 241 | np.array(history_2.history["binary_accuracy"]) * 100, "g-", label="no dropout") 242 | plt.plot(np.arange(len(history_3.history["binary_accuracy"])), 243 | np.array(history_3.history["binary_accuracy"]) * 100, "y-", label="naive dropout") 244 | plt.legend(loc='best') 245 | plt.xlabel("epochs") 246 | plt.ylabel("Accuracy") 247 | plt.subplot(1, 2, 2) 248 | plt.title("(MC - Approx) Histogram") 249 | plt.hist([x[1] - x[0] for x in modeltest_1.history[:17]], bins=bins, alpha=0.5, label="varational") 250 | plt.hist([x[1] - x[0] for x in modeltest_3.history[:17]], bins=bins, alpha=0.5, label="navie dropout") 251 | plt.legend(loc='best') 252 | plt.xlabel("Difference in Loss") 253 | plt.ylabel("Count") 254 | plt.xticks(fontsize=8, rotation=0) 255 | 256 | 257 | # In[60]: 258 | 259 | 260 | plt.figure(figsize=(12, 4)) 261 | plt.subplot(1, 2, 1) 262 | plt.title("Log Loss Comparison - Validation Set") 263 | plt.plot(np.arange(len(modeltest_2.history)), [x[1] for x in modeltest_1.history[:20]], "b-", label="variational(mc)") 264 | plt.plot(np.arange(len(modeltest_2.history)), [x[1] for x in modeltest_2.history], "g-", label="no dropout") 265 | plt.plot(np.arange(len(modeltest_3.history)), [x[1] for x in modeltest_3.history], "y-", label="naive dropout(mc)") 266 | plt.legend(loc='best') 267 | plt.xlabel("epochs") 268 | plt.ylabel("Log Loss") 269 | plt.subplot(1, 2, 2) 270 | plt.title("Accuracy Comparison - Validation Set") 271 | plt.plot(np.arange(len(modeltest_2.history)), [x[3] * 100 for x in modeltest_1.history[:20]], "b-", label="variational(mc)") 272 | plt.plot(np.arange(len(modeltest_2.history)), [x[3] * 100 for x in modeltest_2.history], "g-", label="no dropout") 273 | plt.plot(np.arange(len(modeltest_3.history)), [x[3] * 100 for x in modeltest_3.history], "y-", label="naive dropout(mc)") 274 | plt.legend(loc='best') 275 | plt.xlabel("epochs") 276 | plt.ylabel("Accuracy (%)") 277 | 278 | 279 | # In[ ]: 280 | 281 | 282 | 283 | 284 | -------------------------------------------------------------------------------- /(PyTorch) Cornell Movie Review Dataset.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # # (PyTorch) Cornell Movie Review Dataset 5 | 6 | # In[1]: 7 | 8 | 9 | import torch 10 | import torch.nn as nn 11 | from torch.autograd import Variable 12 | import torch.nn.functional as F 13 | from sklearn.metrics import mean_squared_error 14 | from tqdm import tqdm_notebook 15 | import numpy as np 16 | import matplotlib.pyplot as plt 17 | 18 | from yaringal_dataset import loader 19 | from weight_drop import WeightDrop 20 | from embed_regularize import embedded_dropout 21 | from locked_dropout import LockedDropout 22 | 23 | get_ipython().magic('matplotlib inline') 24 | plt.style.use('fivethirtyeight') 25 | plt.rcParams["figure.figsize"] = (8, 5) 26 | 27 | # Global params: 28 | NB_WORDS = 20000 29 | SKIP_TOP = 0 30 | TEST_SPLIT = 0.2 31 | INIT_SEED = 2017 32 | GLOBAL_SEED = 2018 33 | MAXLEN = 200 34 | BATCH_SIZE = 128 35 | TEST_BATCH_SIZE = 512 36 | 37 | 38 | # In[2]: 39 | 40 | 41 | dataset = loader(INIT_SEED, MAXLEN, NB_WORDS, SKIP_TOP, TEST_SPLIT) 42 | 43 | X_train, X_test, Y_train, Y_test = dataset.X_train, dataset.X_test, dataset.Y_train, dataset.Y_test 44 | mean_y_train, std_y_train = dataset.mean_y_train, dataset.std_y_train 45 | 46 | 47 | # In[3]: 48 | 49 | 50 | def inverse_transform(v): 51 | return v * std_y_train + mean_y_train 52 | 53 | 54 | # In[4]: 55 | 56 | 57 | class Model(nn.Module): 58 | def __init__(self, nb_words, hidden_size=128, embedding_size=128, n_layers=1, 59 | wdrop=0.25, odrop=0.25, edrop=0.1, idrop=0.25, variational=False, 60 | standard_dropout=False, batch_first=True): 61 | super(Model, self).__init__() 62 | self.standard_dropout = standard_dropout 63 | self.lockdrop = LockedDropout(batch_first=batch_first) 64 | self.odrop = odrop 65 | self.idrop = idrop 66 | self.edrop = edrop 67 | self.n_layers = n_layers 68 | self.embedding = nn.Embedding(nb_words, embedding_size) 69 | self.rnns = [ 70 | nn.LSTM(embedding_size if l == 0 else hidden_size, 71 | hidden_size, num_layers=1, batch_first=batch_first) 72 | for l in range(n_layers) 73 | ] 74 | if wdrop: 75 | self.rnns = [WeightDrop(rnn, ['weight_hh_l0'], dropout=wdrop, variational=variational) 76 | for rnn in self.rnns] 77 | self.rnns = torch.nn.ModuleList(self.rnns) 78 | self.output_layer = nn.Linear(hidden_size, 1) 79 | self.init_weights() 80 | 81 | def init_weights(self): 82 | initrange = 0.1 83 | self.embedding.weight.data.uniform_(-initrange, initrange) 84 | self.output_layer.bias.data.fill_(0) 85 | self.output_layer.weight.data.uniform_(-initrange, initrange) 86 | 87 | def forward(self, X): 88 | emb = embedded_dropout(self.embedding, X, dropout=self.edrop if self.training else 0) 89 | if self.standard_dropout: 90 | raw_output = F.dropout(emb, p=self.idrop, training=self.training) 91 | else: 92 | raw_output = self.lockdrop(emb, self.idrop) 93 | new_hidden, new_cell_state = [], [] 94 | for l, rnn in enumerate(self.rnns): 95 | raw_output, (new_h, new_c) = rnn(raw_output) 96 | if self.standard_dropout: 97 | raw_output = F.dropout(raw_output, p=self.odrop, training=self.training) 98 | else: 99 | raw_output = self.lockdrop(raw_output, self.odrop) 100 | new_hidden.append(new_h) 101 | new_cell_state.append(new_c) 102 | hidden = torch.cat(new_hidden, 0) 103 | cell_state = torch.cat(new_cell_state, 0) 104 | final_output = self.output_layer(raw_output) 105 | return final_output[:, -1, 0], hidden, cell_state 106 | 107 | 108 | # In[5]: 109 | 110 | 111 | MC_ROUNDS = 10 112 | def fit(model, optimizer, X_train_tensor, Y_train_tensor, 113 | X_test_tensor, Y_test_tensor, n_epochs=30, mc_dropout=True): 114 | epoch_losses = [] 115 | criterion = torch.nn.MSELoss() 116 | for epoch in range(n_epochs): 117 | indices = torch.randperm(len(X_train)).cuda() 118 | losses, losses_raw = [], [] 119 | model.train() 120 | for i in range(0, len(X_train), BATCH_SIZE): #tqdm_notebook(range(0, len(X_train), BATCH_SIZE)): 121 | optimizer.zero_grad() 122 | pred, _, _ = model(Variable(X_train_tensor[indices[i:(i+BATCH_SIZE)]])) 123 | loss_raw = criterion( 124 | pred, 125 | Variable(Y_train_tensor[indices[i:(i+BATCH_SIZE)]], requires_grad=False) 126 | ) 127 | loss = F.mse_loss(inverse_transform(pred), 128 | inverse_transform( 129 | Variable(Y_train_tensor[indices[i:(i+BATCH_SIZE)]], requires_grad=False))) 130 | losses_raw.append(loss_raw.data.cpu()[0]) 131 | losses.append(loss.data.cpu()[0]) 132 | loss_raw.backward() 133 | optimizer.step() 134 | train_loss = np.mean(losses)** 0.5 135 | train_loss_raw = np.mean(losses_raw) 136 | # Standard dropout approximation 137 | losses, losses_raw = [], [] 138 | model.eval() 139 | for i in range(0, len(X_test), TEST_BATCH_SIZE): 140 | pred_test, _, _ = model(Variable(X_test_tensor[i:(i+TEST_BATCH_SIZE)], volatile=True)) 141 | loss_raw = F.mse_loss(pred_test, Variable(Y_test_tensor[i:(i+TEST_BATCH_SIZE)])) 142 | loss = F.mse_loss(inverse_transform(pred_test), 143 | inverse_transform(Variable(Y_test_tensor[i:(i+TEST_BATCH_SIZE)]))) 144 | losses_raw.append(loss_raw.data.cpu()[0]) 145 | losses.append(loss.data.cpu()[0]) 146 | std_test_loss = np.mean(losses) ** 0.5 147 | std_test_loss_raw = np.mean(losses_raw) 148 | if mc_dropout: 149 | # MC dropout 150 | losses, losses_raw = [], [] 151 | model.train() 152 | for i in range(0, len(X_test), TEST_BATCH_SIZE): 153 | pred_list = [] 154 | for j in range(MC_ROUNDS): 155 | pred_test, _, _ = model(Variable(X_test_tensor[i:(i+TEST_BATCH_SIZE)], volatile=True)) 156 | pred_list.append(pred_test.unsqueeze(0)) 157 | pred_all = torch.mean(torch.cat(pred_list, 0), 0) 158 | loss_raw = F.mse_loss(pred_all, Variable(Y_test_tensor[i:(i+TEST_BATCH_SIZE)])) 159 | loss = F.mse_loss(inverse_transform(pred_all), 160 | inverse_transform(Variable(Y_test_tensor[i:(i+TEST_BATCH_SIZE)]))) 161 | losses_raw.append(loss_raw.data.cpu()[0]) 162 | losses.append(loss.data.cpu()[0]) 163 | mc_test_loss = np.mean(losses) ** 0.5 164 | mc_test_loss_raw = np.mean(losses_raw) 165 | epoch_losses.append([ 166 | train_loss, std_test_loss, mc_test_loss, 167 | train_loss_raw, std_test_loss_raw, mc_test_loss_raw 168 | ]) 169 | print("Epoch: {} Train: {:.4f}/{:.4f}, Val Std: {:.4f}/{:.4f}, Val MC: {:.4f}/{:.4f}".format( 170 | epoch, train_loss, std_test_loss_raw, std_test_loss, std_test_loss_raw, mc_test_loss, mc_test_loss_raw)) 171 | else: 172 | epoch_losses.append([train_loss, std_test_loss, mc_test_loss]) 173 | print("Epoch: {} Train: {:.4f}/{:.4f}, Val Std: {:.4f}/{:.4f}".format( 174 | epoch, train_loss, std_test_loss_raw, std_test_loss, std_test_loss_raw)) 175 | return epoch_losses 176 | 177 | 178 | # In[6]: 179 | 180 | 181 | Y_train_tensor = torch.from_numpy(Y_train).float().cuda() 182 | Y_test_tensor = torch.from_numpy(Y_test).float().cuda() 183 | X_train_tensor = torch.from_numpy(X_train).long().cuda() 184 | X_test_tensor = torch.from_numpy(X_test).long().cuda() 185 | 186 | 187 | # ## Weight Dropped LSTM (w Embedding Dropout) 188 | 189 | # In[7]: 190 | 191 | 192 | model_1 = Model(NB_WORDS + dataset.index_from, wdrop=0.02, odrop=0.1, edrop=0.2, idrop=0.1) 193 | model_1.cuda() 194 | optimizer = torch.optim.Adam([ 195 | {'params': model_1.parameters(), 'lr': 1e-4, 'weight_decay': 1e-4} 196 | ],) 197 | epoch_losses_1 = fit( 198 | model_1, optimizer, X_train_tensor, Y_train_tensor, X_test_tensor, Y_test_tensor, n_epochs=100) 199 | 200 | 201 | # In[11]: 202 | 203 | 204 | print("Best RMSE: {:.4f} Best Epoch: {}".format( 205 | np.min([x[2] for x in epoch_losses_1]), 206 | np.argmin([x[2] for x in epoch_losses_1]) + 1 207 | )) 208 | 209 | 210 | # ## No Dropout 211 | 212 | # In[12]: 213 | 214 | 215 | model_2 = Model(NB_WORDS + dataset.index_from, wdrop=0, odrop=0, edrop=0, idrop=0) 216 | model_2.cuda() 217 | optimizer = torch.optim.Adam([ 218 | {'params': model_2.parameters(), 'lr': 1e-4} 219 | ],) 220 | epoch_losses_2 = fit( 221 | model_2, optimizer, X_train_tensor, Y_train_tensor, X_test_tensor, Y_test_tensor, n_epochs=100) 222 | 223 | 224 | # In[13]: 225 | 226 | 227 | print("Best RMSE: {:.4f} Best Epoch: {}".format( 228 | np.min([x[2] for x in epoch_losses_2]), 229 | np.argmin([x[2] for x in epoch_losses_2]) + 1 230 | )) 231 | 232 | 233 | # ## Naive Dropout (w/o Embedding Dropout) 234 | 235 | # In[14]: 236 | 237 | 238 | model_3 = Model(NB_WORDS + dataset.index_from, 239 | wdrop=0, odrop=0.2, edrop=0, idrop=0.2, standard_dropout=True) 240 | model_3.cuda() 241 | optimizer = torch.optim.Adam([ 242 | {'params': model_3.parameters(), 'lr': 1e-4, 'weight_decay': 1e-4} 243 | ],) 244 | epoch_losses_3 = fit( 245 | model_3, optimizer, X_train_tensor, Y_train_tensor, X_test_tensor, Y_test_tensor, n_epochs=100) 246 | 247 | 248 | # In[16]: 249 | 250 | 251 | print("Best RMSE: {:.4f} Best Epoch: {}".format( 252 | np.min([x[2] for x in epoch_losses_3]), 253 | np.argmin([x[2] for x in epoch_losses_3]) + 1 254 | )) 255 | 256 | 257 | # ## Variational LSTM 258 | 259 | # In[17]: 260 | 261 | 262 | model_4 = Model(NB_WORDS + dataset.index_from, wdrop=0.02, odrop=0.1, edrop=0.2, idrop=0.1, variational=True) 263 | model_4.cuda() 264 | optimizer = torch.optim.Adam([ 265 | {'params': model_4.parameters(), 'lr': 1e-4, 'weight_decay': 1e-4} 266 | ],) 267 | epoch_losses_4 = fit( 268 | model_4, optimizer, X_train_tensor, Y_train_tensor, X_test_tensor, Y_test_tensor, n_epochs=100) 269 | 270 | 271 | # In[19]: 272 | 273 | 274 | print("Best RMSE: {:.4f} Best Epoch: {}".format( 275 | np.min([x[2] for x in epoch_losses_4]), 276 | np.argmin([x[2] for x in epoch_losses_4]) + 1 277 | )) 278 | 279 | 280 | # ## Variational LSTM w/o Recurrent Dropout 281 | 282 | # In[20]: 283 | 284 | 285 | model_5 = Model(NB_WORDS + dataset.index_from, wdrop=0, odrop=0.1, edrop=0.2, idrop=0.1) 286 | model_5.cuda() 287 | optimizer = torch.optim.Adam([ 288 | {'params': model_5.parameters(), 'lr': 1e-4, 'weight_decay': 1e-4} 289 | ],) 290 | epoch_losses_5 = fit( 291 | model_5, optimizer, X_train_tensor, Y_train_tensor, X_test_tensor, Y_test_tensor, n_epochs=100) 292 | 293 | 294 | # In[21]: 295 | 296 | 297 | print("Best RMSE: {:.4f} Best Epoch: {}".format( 298 | np.min([x[2] for x in epoch_losses_5]), 299 | np.argmin([x[2] for x in epoch_losses_5]) + 1 300 | )) 301 | 302 | 303 | # ## Visualizations 304 | 305 | # In[52]: 306 | 307 | 308 | bins = np.arange(-0.012, 0.005, 0.001) 309 | 310 | 311 | # In[61]: 312 | 313 | 314 | plt.figure(figsize=(12, 4)) 315 | plt.subplot(1, 2, 1) 316 | plt.title("Raw MSE Comparison - Training Set") 317 | plt.plot(np.arange(len(epoch_losses_1)), [x[3] for x in epoch_losses_1], label="weight dropped") 318 | plt.plot(np.arange(len(epoch_losses_2)), [x[3] for x in epoch_losses_2], "g-", label="no dropout") 319 | plt.plot(np.arange(len(epoch_losses_3)), [x[3] for x in epoch_losses_3], "y-", label="naive dropout") 320 | plt.plot(np.arange(len(epoch_losses_4)), [x[3] for x in epoch_losses_4], "m-", label="variational") 321 | plt.plot(np.arange(len(epoch_losses_5)), [x[3] for x in epoch_losses_5], "c-", label="variational-2") 322 | plt.legend(loc='best') 323 | plt.xlabel("epochs") 324 | plt.ylabel("logloss") 325 | plt.subplot(1, 2, 2) 326 | plt.title("(MC - Approx) Histogram") 327 | plt.hist([x[2] - x[1] for x in epoch_losses_1], bins=bins, alpha=0.3, label="w-dropped") 328 | plt.hist([x[2] - x[1] for x in epoch_losses_3], bins=bins, alpha=0.3, label="naive") 329 | plt.hist([x[2] - x[1] for x in epoch_losses_4], bins=bins, alpha=0.3, label="varational w-drop") 330 | plt.legend(loc='best') 331 | plt.xlabel("Difference in Raw MSE") 332 | plt.ylabel("Count") 333 | plt.xticks(fontsize=8, rotation=0) 334 | 335 | 336 | # In[60]: 337 | 338 | 339 | plt.figure(figsize=(10, 6)) 340 | plt.title("RMSE Comparison - Validation Set") 341 | plt.plot(np.arange(len(epoch_losses_1)), [x[2] for x in epoch_losses_1], label="weight dropped") 342 | plt.plot(np.arange(len(epoch_losses_2)), [x[2] for x in epoch_losses_2], "g-", label="no dropout") 343 | plt.plot(np.arange(len(epoch_losses_3)), [x[2] for x in epoch_losses_3], "y-", label="naive dropout") 344 | plt.plot(np.arange(len(epoch_losses_4)), [x[2] for x in epoch_losses_4], "m-", label="variational") 345 | plt.plot(np.arange(len(epoch_losses_5)), [x[2] for x in epoch_losses_5], "c-", label="v w/o r-drop") 346 | plt.legend(loc='best') 347 | plt.xlabel("epochs") 348 | plt.ylabel("logloss") 349 | 350 | 351 | # In[ ]: 352 | 353 | 354 | 355 | 356 | -------------------------------------------------------------------------------- /(PyTorch) IMDB Dataset.py: -------------------------------------------------------------------------------- 1 | 2 | # coding: utf-8 3 | 4 | # # (PyTorch) IMDB Dataset 5 | 6 | # In[1]: 7 | 8 | 9 | import torch 10 | import torch.nn as nn 11 | from torch.autograd import Variable 12 | import torch.nn.functional as F 13 | from tensorflow.contrib.keras.python.keras.datasets import imdb 14 | from tensorflow.contrib.keras.python.keras.preprocessing import sequence 15 | from sklearn.metrics import mean_squared_error 16 | from tqdm import tqdm_notebook 17 | import numpy as np 18 | import matplotlib.pyplot as plt 19 | # import matplotlib.ticker as ticker 20 | 21 | from yaringal_dataset import loader 22 | from weight_drop import WeightDrop 23 | from embed_regularize import embedded_dropout 24 | from locked_dropout import LockedDropout 25 | 26 | get_ipython().magic('matplotlib inline') 27 | plt.style.use('fivethirtyeight') 28 | plt.rcParams["figure.figsize"] = (10, 5) 29 | 30 | # Global params: 31 | NB_WORDS = 20000 32 | SKIP_TOP = 0 33 | TEST_SPLIT = 0.2 34 | INIT_SEED = 2017 35 | GLOBAL_SEED = 2018 36 | MAXLEN = 80 37 | BATCH_SIZE = 128 38 | TEST_BATCH_SIZE = 512 39 | 40 | 41 | # In[2]: 42 | 43 | 44 | (X_train, Y_train), (X_test, Y_test) = imdb.load_data(num_words=NB_WORDS) 45 | print(len(X_train), 'train sequences') 46 | print(len(X_test), 'test sequences') 47 | print('Pad sequences (samples x time)') 48 | X_train = sequence.pad_sequences(X_train, maxlen=MAXLEN) 49 | X_test = sequence.pad_sequences(X_test, maxlen=MAXLEN) 50 | print('x_train shape:', X_train.shape) 51 | print('x_test shape:', X_test.shape) 52 | 53 | 54 | # In[3]: 55 | 56 | 57 | class Model(nn.Module): 58 | def __init__(self, nb_words, hidden_size=128, embedding_size=128, n_layers=1, 59 | wdrop=0.25, odrop=0.25, edrop=0.1, idrop=0.25, variational=False, 60 | standard_dropout=False, batch_first=True): 61 | super(Model, self).__init__() 62 | self.standard_dropout = standard_dropout 63 | self.lockdrop = LockedDropout(batch_first=batch_first) 64 | self.odrop = odrop 65 | self.idrop = idrop 66 | self.edrop = edrop 67 | self.n_layers = n_layers 68 | self.embedding = nn.Embedding(nb_words, embedding_size) 69 | self.rnns = [ 70 | nn.LSTM(embedding_size if l == 0 else hidden_size, 71 | hidden_size, num_layers=1, batch_first=batch_first) 72 | for l in range(n_layers) 73 | ] 74 | if wdrop: 75 | self.rnns = [WeightDrop(rnn, ['weight_hh_l0'], dropout=wdrop, variational=variational) 76 | for rnn in self.rnns] 77 | self.rnns = torch.nn.ModuleList(self.rnns) 78 | self.output_layer = nn.Linear(hidden_size, 1) 79 | self.init_weights() 80 | 81 | def init_weights(self): 82 | initrange = 0.1 83 | self.embedding.weight.data.uniform_(-initrange, initrange) 84 | self.output_layer.bias.data.fill_(0) 85 | self.output_layer.weight.data.uniform_(-initrange, initrange) 86 | 87 | def forward(self, X): 88 | emb = embedded_dropout(self.embedding, X, dropout=self.edrop if self.training else 0) 89 | if self.standard_dropout: 90 | raw_output = F.dropout(emb, p=self.idrop, training=self.training) 91 | else: 92 | raw_output = self.lockdrop(emb, self.idrop) 93 | new_hidden, new_cell_state = [], [] 94 | for l, rnn in enumerate(self.rnns): 95 | raw_output, (new_h, new_c) = rnn(raw_output) 96 | if self.standard_dropout: 97 | raw_output = F.dropout(raw_output, p=self.odrop, training=self.training) 98 | else: 99 | raw_output = self.lockdrop(raw_output, self.odrop) 100 | new_hidden.append(new_h) 101 | new_cell_state.append(new_c) 102 | hidden = torch.cat(new_hidden, 0) 103 | cell_state = torch.cat(new_cell_state, 0) 104 | final_output = self.output_layer(raw_output) 105 | return final_output[:, -1, 0], hidden, cell_state 106 | 107 | 108 | # In[4]: 109 | 110 | 111 | MC_ROUNDS = 10 112 | def fit(model, optimizer, X_train_tensor, Y_train_tensor, X_test_tensor, Y_test_tensor, n_epochs=30): 113 | epoch_losses = [] 114 | criterion = torch.nn.BCEWithLogitsLoss() 115 | for epoch in range(n_epochs): 116 | indices = torch.randperm(len(X_train)).cuda() 117 | losses, acc = [], [] 118 | model.train() 119 | for i in range(0, len(X_train), BATCH_SIZE): #tqdm_notebook(range(0, len(X_train), BATCH_SIZE)): 120 | optimizer.zero_grad() 121 | pred, _, _ = model(Variable(X_train_tensor[indices[i:(i+BATCH_SIZE)]])) 122 | # print(pred.size()) 123 | loss = criterion( 124 | pred, 125 | Variable(Y_train_tensor[indices[i:(i+BATCH_SIZE)]], requires_grad=False) 126 | ) 127 | acc.append( 128 | torch.eq( 129 | (F.sigmoid(pred).data > 0.5).float(), 130 | Y_train_tensor[indices[i:(i+BATCH_SIZE)]] 131 | ) 132 | ) 133 | losses.append(loss.data.cpu()[0]) 134 | loss.backward() 135 | optimizer.step() 136 | train_acc = torch.mean(torch.cat(acc).float()) 137 | train_loss = np.mean(losses) 138 | # Standard dropout approximation 139 | losses, acc=[], [] 140 | model.eval() 141 | for i in range(0, len(X_test), TEST_BATCH_SIZE): 142 | pred_test, _, _ = model(Variable(X_test_tensor[i:(i+TEST_BATCH_SIZE)], volatile=True)) 143 | # print(pred.size()) 144 | loss = F.binary_cross_entropy_with_logits( 145 | pred_test, Variable(Y_test_tensor[i:(i+TEST_BATCH_SIZE)])) 146 | acc.append( 147 | torch.eq( 148 | (F.sigmoid(pred_test).data > 0.5).float(), 149 | Y_test_tensor[i:(i+TEST_BATCH_SIZE)] 150 | ) 151 | ) 152 | losses.append(loss.data.cpu()[0]) 153 | std_test_acc = torch.mean(torch.cat(acc).float()) 154 | std_test_loss = np.mean(losses) 155 | # MC dropout 156 | losses, acc = [], [] 157 | model.train() 158 | for i in range(0, len(X_test), TEST_BATCH_SIZE): 159 | pred_list = [] 160 | for j in range(MC_ROUNDS): 161 | pred_test, _, _ = model(Variable(X_test_tensor[i:(i+TEST_BATCH_SIZE)], volatile=True)) 162 | pred_list.append(pred_test.unsqueeze(0)) 163 | pred_all = torch.mean(torch.cat(pred_list, 0), 0) 164 | loss = F.binary_cross_entropy_with_logits( 165 | pred_all, Variable(Y_test_tensor[i:(i+TEST_BATCH_SIZE)])) 166 | acc.append( 167 | torch.eq( 168 | (F.sigmoid(pred_all).data > 0.5).float(), 169 | Y_test_tensor[i:(i+TEST_BATCH_SIZE)] 170 | ) 171 | ) 172 | losses.append(loss.data.cpu()[0]) 173 | mc_test_acc = torch.mean(torch.cat(acc).float()) 174 | mc_test_loss = np.mean(losses) 175 | epoch_losses.append([ 176 | train_loss, std_test_loss, mc_test_loss, 177 | train_acc, std_test_acc, mc_test_acc]) 178 | print("Epoch: {} Train: {:.4f}/{:.2f}%, Val Std: {:.4f}/{:.2f}%, Val MC: {:.4f}/{:.2f}%".format( 179 | epoch, train_loss, train_acc*100, std_test_loss, std_test_acc*100, mc_test_loss, mc_test_acc*100)) 180 | return epoch_losses 181 | 182 | 183 | # In[5]: 184 | 185 | 186 | Y_train_tensor = torch.from_numpy(Y_train).float().cuda() 187 | Y_test_tensor = torch.from_numpy(Y_test).float().cuda() 188 | X_train_tensor = torch.from_numpy(X_train).long().cuda() 189 | X_test_tensor = torch.from_numpy(X_test).long().cuda() 190 | 191 | 192 | # ## Weight Dropped LSTM (w Embedding Dropout) 193 | 194 | # In[6]: 195 | 196 | 197 | model_1 = Model(NB_WORDS, wdrop=0.02, odrop=0.1, edrop=0.2, idrop=0.1) 198 | model_1.cuda() 199 | optimizer = torch.optim.Adam([ 200 | {'params': model_1.parameters(), 'lr': 1e-4, 'weight_decay': 1e-4} 201 | ]) 202 | epoch_losses_1 = fit( 203 | model_1, optimizer, X_train_tensor, Y_train_tensor, X_test_tensor, Y_test_tensor, n_epochs=20) 204 | 205 | 206 | # In[16]: 207 | 208 | 209 | best_epoch = np.argmin([x[2] for x in epoch_losses_1]) + 1 210 | print("Best Loss: {:.4f} Acc: {:.2f}% Best Epoch: {}".format( 211 | epoch_losses_1[best_epoch-1][2], 212 | epoch_losses_1[best_epoch-1][5] * 100, 213 | best_epoch 214 | )) 215 | 216 | 217 | # ## No Dropout 218 | 219 | # In[8]: 220 | 221 | 222 | model_2 = Model(NB_WORDS, wdrop=0, odrop=0, edrop=0, idrop=0) 223 | model_2.cuda() 224 | optimizer = torch.optim.Adam(model_2.parameters(), lr=1e-4) 225 | epoch_losses_2 = fit(model_2, optimizer, X_train_tensor, Y_train_tensor, 226 | X_test_tensor, Y_test_tensor, n_epochs=20) 227 | 228 | 229 | # In[9]: 230 | 231 | 232 | best_epoch = np.argmin([x[1] for x in epoch_losses_2]) + 1 233 | print("Best Loss: {:.4f} Acc: {:.2f}% Best Epoch: {}".format( 234 | epoch_losses_2[best_epoch-1][1], 235 | epoch_losses_2[best_epoch-1][4] * 100, 236 | best_epoch 237 | )) 238 | 239 | 240 | # ## Naive Dropout (w/o Embedding Dropout) 241 | 242 | # In[10]: 243 | 244 | 245 | model_3 = Model(NB_WORDS, wdrop=0, odrop=0.2, edrop=0, idrop=0.2, standard_dropout=True) 246 | model_3.cuda() 247 | optimizer = torch.optim.Adam(model_3.parameters(), lr=1e-4) 248 | epoch_losses_3 = fit(model_3, optimizer, X_train_tensor, Y_train_tensor, 249 | X_test_tensor, Y_test_tensor, n_epochs=20) 250 | 251 | 252 | # In[11]: 253 | 254 | 255 | best_epoch = np.argmin([x[2] for x in epoch_losses_3]) + 1 256 | print("Best Loss: {:.4f} Acc: {:.2f}% Best Epoch: {}".format( 257 | epoch_losses_3[best_epoch-1][2], 258 | epoch_losses_3[best_epoch-1][5] * 100, 259 | best_epoch 260 | )) 261 | 262 | 263 | # ## Variational LSTM 264 | 265 | # In[12]: 266 | 267 | 268 | model_4 = Model(NB_WORDS, wdrop=0.02, odrop=0.1, edrop=0.2, idrop=0.1, variational=True) 269 | model_4.cuda() 270 | optimizer = torch.optim.Adam(model_4.parameters(), lr=1e-4) 271 | epoch_losses_4 = fit(model_4, optimizer, X_train_tensor, Y_train_tensor, 272 | X_test_tensor, Y_test_tensor, n_epochs=20) 273 | 274 | 275 | # In[13]: 276 | 277 | 278 | best_epoch = np.argmin([x[2] for x in epoch_losses_4]) + 1 279 | print("Best Loss: {:.4f} Acc: {:.2f}% Best Epoch: {}".format( 280 | epoch_losses_4[best_epoch-1][2], 281 | epoch_losses_4[best_epoch-1][5] * 100, 282 | best_epoch 283 | )) 284 | 285 | 286 | # ## Variational LSTM w/o Recurrent Dropout 287 | 288 | # In[14]: 289 | 290 | 291 | model_5 = Model(NB_WORDS, wdrop=0., odrop=0.1, edrop=0.2, idrop=0.1) 292 | model_5.cuda() 293 | optimizer = torch.optim.Adam(model_5.parameters(), lr=1e-4) 294 | epoch_losses_5= fit(model_5, optimizer, X_train_tensor, Y_train_tensor, 295 | X_test_tensor, Y_test_tensor, n_epochs=20) 296 | 297 | 298 | # In[17]: 299 | 300 | 301 | best_epoch = np.argmin([x[2] for x in epoch_losses_5]) + 1 302 | print("Best Loss: {:.4f} Acc: {:.2f}% Best Epoch: {}".format( 303 | epoch_losses_5[best_epoch-1][2], 304 | epoch_losses_5[best_epoch-1][5] * 100, 305 | best_epoch 306 | )) 307 | 308 | 309 | # ## Visualizations 310 | 311 | # In[27]: 312 | 313 | 314 | bins = np.arange(-0.03, 0.01, 0.002) 315 | plt.figure(figsize=(12, 4)) 316 | plt.subplot(1, 2, 1) 317 | plt.title("Accuracy Comparison - Training Set") 318 | plt.plot(np.arange(len(epoch_losses_1)), [x[3] * 100 for x in epoch_losses_1], label="weight dropped") 319 | plt.plot(np.arange(len(epoch_losses_2)), [x[3] * 100 for x in epoch_losses_2], "g-", label="no dropout") 320 | plt.plot(np.arange(len(epoch_losses_3)), [x[3] * 100 for x in epoch_losses_3], "y-", label="naive dropout") 321 | plt.plot(np.arange(len(epoch_losses_4)), [x[3] * 100 for x in epoch_losses_4], "m-", label="variational") 322 | plt.plot(np.arange(len(epoch_losses_5)), [x[3] * 100 for x in epoch_losses_5], "c-", label="v w/o r-drop") 323 | plt.legend(loc='best') 324 | plt.xlabel("epochs") 325 | plt.ylabel("Accuracy (%)") 326 | plt.subplot(1, 2, 2) 327 | plt.title("(MC - Approx) Histogram") 328 | plt.hist([x[2] - x[1] for x in epoch_losses_1], bins=bins, alpha=0.3, label="w-dropped") 329 | plt.hist([x[2] - x[1] for x in epoch_losses_3], bins=bins, alpha=0.3, label="naive") 330 | plt.hist([x[2] - x[1] for x in epoch_losses_4], bins=bins, alpha=0.3, label="varational w-drop") 331 | plt.legend(loc='best') 332 | plt.xlabel("Difference in Raw MSE") 333 | plt.ylabel("Count") 334 | plt.xticks(fontsize=8, rotation=0) 335 | 336 | 337 | # In[33]: 338 | 339 | 340 | plt.figure(figsize=(12, 4)) 341 | plt.subplot(1, 2, 1) 342 | plt.title("Log Loss Comparison - Validation Set") 343 | plt.plot(np.arange(len(epoch_losses_1)), [x[2] * 100 for x in epoch_losses_1], "b-", label="weight dropped(mc)") 344 | plt.plot(np.arange(len(epoch_losses_2)), [x[2] * 100 for x in epoch_losses_2], "g-", label="no dropout") 345 | plt.plot(np.arange(len(epoch_losses_3)), [x[2] * 100 for x in epoch_losses_3], "y-", label="naive dropout(mc)") 346 | plt.plot(np.arange(len(epoch_losses_4)), [x[2] * 100 for x in epoch_losses_4], "m-", label="variational") 347 | plt.plot(np.arange(len(epoch_losses_5)), [x[2] * 100 for x in epoch_losses_5], "c-", label="v w/o r-drop") 348 | plt.legend(loc='best') 349 | plt.xlabel("epochs") 350 | plt.ylabel("Log Loss") 351 | plt.subplot(1, 2, 2) 352 | plt.title("Accuracy Comparison - Validation Set") 353 | plt.plot(np.arange(len(epoch_losses_1)), [x[5] for x in epoch_losses_1], "b-", label="weight dropped(mc)") 354 | plt.plot(np.arange(len(epoch_losses_2)), [x[5] for x in epoch_losses_2], "g-", label="no dropout") 355 | plt.plot(np.arange(len(epoch_losses_3)), [x[5] for x in epoch_losses_3], "y-", label="naive dropout(mc)") 356 | plt.plot(np.arange(len(epoch_losses_4)), [x[5] for x in epoch_losses_4], "m-", label="variational") 357 | plt.plot(np.arange(len(epoch_losses_5)), [x[5] for x in epoch_losses_5], "c-", label="v w/o r-drop") 358 | plt.legend(loc='best') 359 | plt.xlabel("epochs") 360 | plt.ylabel("Accuracy (%)") 361 | 362 | 363 | # In[ ]: 364 | 365 | 366 | 367 | 368 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.py[cod] 2 | 3 | # C extensions 4 | *.so 5 | 6 | # Packages 7 | *.egg 8 | *.egg-info 9 | dist 10 | build 11 | eggs 12 | .eggs 13 | parts 14 | bin 15 | var 16 | sdist 17 | wheelhouse 18 | develop-eggs 19 | .installed.cfg 20 | lib 21 | lib64 22 | venv*/ 23 | pyvenv*/ 24 | 25 | # Installer logs 26 | pip-log.txt 27 | 28 | # Unit test / coverage reports 29 | .coverage 30 | .tox 31 | .coverage.* 32 | nosetests.xml 33 | coverage.xml 34 | htmlcov 35 | 36 | # Translations 37 | *.mo 38 | 39 | # Mr Developer 40 | .mr.developer.cfg 41 | .project 42 | .pydevproject 43 | .idea 44 | *.iml 45 | *.komodoproject 46 | 47 | # Complexity 48 | output/*.html 49 | output/*/index.html 50 | 51 | # Sphinx 52 | docs/_build 53 | 54 | .DS_Store 55 | *~ 56 | .*.sw[po] 57 | .build 58 | .ve 59 | .env 60 | .cache 61 | .pytest 62 | .bootstrap 63 | .appveyor.token 64 | *.bak 65 | 66 | *.html 67 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | FROM ceshine/cuda-pytorch:0.2.0 2 | 3 | MAINTAINER CeShine Lee 4 | 5 | RUN pip install --upgrade pip tqdm && \ 6 | pip install tensorflow-gpu==1.3.0 7 | 8 | RUN conda install -y --quiet jupyter mkl-service matplotlib && \ 9 | conda clean -tipsy 10 | 11 | # Jupyter 12 | EXPOSE 8888 13 | CMD jupyter notebook --ip=0.0.0.0 --port=8888 14 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # recurrent-dropout-experiments 2 | 3 | Source code for the Medium post — [[Learning Note] Dropout in Recurrent Networks — Part 3](https://towardsdatascience.com/learning-note-dropout-in-recurrent-networks-part-3-1b161d030cd4). 4 | 5 | Other posts in the series: 6 | 7 | * [[Learning Note] Dropout in Recurrent Networks — Part 1](https://becominghuman.ai/learning-note-dropout-in-recurrent-networks-part-1-57a9c19a2307) 8 | * [[Learning Note] Dropout in Recurrent Networks — Part 2](https://towardsdatascience.com/learning-note-dropout-in-recurrent-networks-part-2-f209222481f8) 9 | -------------------------------------------------------------------------------- /data/scaledata.README.1.0.txt: -------------------------------------------------------------------------------- 1 | 2 | ======= 3 | 4 | Introduction 5 | 6 | This README v1.0 (June, 2005) for the v0.9 and v1.0 scale datasets comes 7 | from the URL 8 | http://www.cs.cornell.edu/people/pabo/movie-review-data . 9 | 10 | ======= 11 | 12 | Citation Info 13 | 14 | This data was first used in Bo Pang and Lillian Lee, 15 | ``Seeing stars: Exploiting class relationships for sentiment categorization 16 | with respect to rating scales.'', Proceedings of the ACL, 2005. 17 | 18 | @InProceedings{Pang+Lee:05a, 19 | author = {Bo Pang and Lillian Lee}, 20 | title = {Seeing stars: Exploiting class relationships for sentiment 21 | categorization with respect to rating scales}, 22 | booktitle = {Proceedings of the ACL}, 23 | year = 2005 24 | } 25 | 26 | ======= 27 | 28 | Data Format Summary 29 | 30 | There are two tar files, roughly corresponding to (1) the reviews 31 | after pre-processing, including subjectivity extraction (i.e., the 32 | data we used in our experiments) and (2) the reviews after very light 33 | pre-processing (provided in case these prove convenient to others; to 34 | date we have not experimented directly with them). 35 | 36 | 37 | (1) scale_data.tar.gz (scale dataset v1.0): contains this readme and 38 | data files that were used in the experiments described in Pang/Lee 39 | ACL 2005. 40 | 41 | Specifically: 42 | 43 | Each sub-directory $author contains data extracted from reviews written 44 | by some single author; altogether, there are four author sub-directories. 45 | 46 | In each such sub-directory, each line in the file subj.$author 47 | corresponds to the subjective extract of one review. The 48 | corresponding line in the file id.$author specifies the source html 49 | file for the review from which the extract was created; these source 50 | files can be found in polarity_html.zip, available from 51 | http://www.cs.cornell.edu/people/pabo/movie-review-data ("Pool of 52 | 27886 unprocessed html files"). 53 | 54 | We automatically tokenized and applied pattern matching technique to 55 | remove explicit rating indications from the reviews. Subjective 56 | sentences were automatically identified using the system described in 57 | our 2004 ACL paper 58 | (http://www.cs.cornell.edu/home/llee/papers/cutsent.home.html). We 59 | did not apply any feature selection algorithms in our experiments; 60 | we simply used all unigrams as features, and used feature 61 | presence/absence to create feature vectors. 62 | 63 | The class label for each extract is given in the corresponding line 64 | of the file label.3class.$author (for the {0,1,2} three-category 65 | classification task) or label.4class.$author (for the {0,1,2,3} 66 | four-categority classification task). 67 | 68 | For those who wish to experiment with more fine-grained labels, we 69 | also provide normalized ratings (in the range [0-1] with stepsize 70 | 0.1 or smaller, depending on the smallest unit used by the author) 71 | in the file rating.$author. 72 | 73 | EXAMPLE: consider the information corresponding to the extract 74 | represented by the first line of Steve+Rhodes/subj.Steve+Rhodes: 75 | 76 | % paste Steve+Rhodes/label.3class.Steve+Rhodes \ 77 | Steve+Rhodes/label.4class.Steve+Rhodes Steve+Rhodes/id.Steve+Rhodes \ 78 | Steve+Rhodes/rating.Steve+Rhodes | head -1 79 | 0 0 11790 0.1 80 | 81 | The class labels for both the three-class and four-class tasks are 0. 82 | The original review was written by Steve Rhodes and extracted from 83 | 11790.html (see above for location of original reviews). 84 | The numerical rating converted from the four-star system used by the author 85 | (1/2 star was the smallest unit he employed) is 0.1 (see section 86 | "Label Decision" below for more information on rating normalization). 87 | 88 | 89 | (2) scale_whole_review.tar.gz (scale dataset v0.9): Contains this 90 | README and the review files in their entireties before passing 91 | through tokenization, sentence separation, and subjectivity 92 | extraction. 93 | 94 | Specifically: 95 | 96 | The entire review for each subjective extract in $author/subj.$author 97 | (of scale dataset v1.0) can be identified by the id number specified 98 | in the correponding line of $author/id.$author and located as file 99 | $author/txt.parag/$id.txt 100 | where each line of $id.txt corresponds to one paragraph of the review. 101 | 102 | ======= 103 | 104 | Label Decision 105 | 106 | The numerical ratings were derived from texts in the original html 107 | files. Note that with our particular conversion scheme, 0-to-4 stars 108 | within a four star system translates into 0.1-to-0.9 in our normalized 109 | numerical ratings, whereas 0-to-5 stars within a five star system 110 | translates into 0-to-1. (The reasoning was that in a four-star 111 | system, an author is more likely to assign "endpoint" scores because 112 | the dynamic range of the rating scheme is smaller.) 113 | 114 | The class labels were then derived from the normalized numerical ratings. 115 | * for the three-class task: 116 | 0: rating <= 0.4 117 | 1: 0.4 < rating < 0.7 118 | 2: rating >= 0.7 119 | 120 | * for the four-class task: 121 | 0: rating <=.3 122 | 1: .4 <= rating <=.5 123 | 2: .6 <= rating <= .7 124 | 3: .8 <= rating 125 | 126 | -------------------------------------------------------------------------------- /data/scaledata/Dennis+Schwartz/id.Dennis+Schwartz: -------------------------------------------------------------------------------- 1 | 29420 2 | 17219 3 | 18406 4 | 18648 5 | 20021 6 | 20454 7 | 20473 8 | 20538 9 | 21002 10 | 21739 11 | 21899 12 | 21908 13 | 22251 14 | 23721 15 | 24276 16 | 25097 17 | 25119 18 | 25673 19 | 25799 20 | 26295 21 | 26341 22 | 26402 23 | 26462 24 | 27176 25 | 27366 26 | 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1713 | 3 1714 | 3 1715 | 3 1716 | 3 1717 | 3 1718 | 3 1719 | 3 1720 | 3 1721 | 3 1722 | 3 1723 | 3 1724 | 3 1725 | 3 1726 | 3 1727 | 3 1728 | 3 1729 | 3 1730 | 3 1731 | 3 1732 | 3 1733 | 3 1734 | 3 1735 | 3 1736 | 3 1737 | 3 1738 | 3 1739 | 3 1740 | 3 1741 | 3 1742 | 3 1743 | 3 1744 | 3 1745 | 3 1746 | 3 1747 | 3 1748 | 3 1749 | 3 1750 | 3 1751 | 3 1752 | 3 1753 | 3 1754 | 3 1755 | 3 1756 | 3 1757 | 3 1758 | 3 1759 | 3 1760 | 3 1761 | 3 1762 | 3 1763 | 3 1764 | 3 1765 | 3 1766 | 3 1767 | 3 1768 | 3 1769 | 3 1770 | 3 1771 | -------------------------------------------------------------------------------- /embed_regularize.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | import torch 4 | from torch.autograd import Variable 5 | 6 | def embedded_dropout(embed, words, dropout=0.1, scale=None): 7 | if dropout: 8 | mask = embed.weight.data.new().resize_((embed.weight.size(0), 1)).bernoulli_(1 - dropout).expand_as(embed.weight) / (1 - dropout) 9 | mask = Variable(mask) 10 | masked_embed_weight = mask * embed.weight 11 | else: 12 | masked_embed_weight = embed.weight 13 | if scale: 14 | masked_embed_weight = scale.expand_as(masked_embed_weight) * masked_embed_weight 15 | 16 | padding_idx = embed.padding_idx 17 | if padding_idx is None: 18 | padding_idx = -1 19 | X = embed._backend.Embedding.apply(words, masked_embed_weight, 20 | padding_idx, embed.max_norm, embed.norm_type, 21 | embed.scale_grad_by_freq, embed.sparse 22 | ) 23 | return X 24 | 25 | if __name__ == '__main__': 26 | V = 50 27 | h = 4 28 | bptt = 10 29 | batch_size = 2 30 | 31 | embed = torch.nn.Embedding(V, h) 32 | 33 | words = np.random.random_integers(low=0, high=V-1, size=(batch_size, bptt)) 34 | words = torch.LongTensor(words) 35 | words = Variable(words) 36 | 37 | origX = embed(words) 38 | X = embedded_dropout(embed, words) 39 | 40 | print(origX) 41 | print(X) 42 | -------------------------------------------------------------------------------- /jupyter_notebook_config.py: -------------------------------------------------------------------------------- 1 | # Reference: https://svds.com/jupyter-notebook-best-practices-for-data-science/ 2 | import os 3 | from subprocess import check_call 4 | 5 | def post_save(model, os_path, contents_manager): 6 | """post-save hook for converting notebooks to .py scripts""" 7 | if model['type'] != 'notebook': 8 | return # only do this for notebooks 9 | d, fname = os.path.split(os_path) 10 | check_call(['jupyter', 'nbconvert', '--to', 'script', fname], cwd=d) 11 | check_call(['jupyter', 'nbconvert', '--to', 'html', fname], cwd=d) 12 | 13 | c.FileContentsManager.post_save_hook = post_save 14 | -------------------------------------------------------------------------------- /locked_dropout.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from torch.autograd import Variable 4 | 5 | class LockedDropout(nn.Module): 6 | def __init__(self, batch_first=False): 7 | super().__init__() 8 | self.batch_first = batch_first 9 | 10 | def forward(self, x, dropout=0.5): 11 | if not self.training or not dropout: 12 | return x 13 | if self.batch_first: 14 | m = x.data.new(x.size(0), 1, x.size(2)).bernoulli_(1 - dropout) 15 | else: 16 | m = x.data.new(1, x.size(1), x.size(2)).bernoulli_(1 - dropout) 17 | mask = Variable(m, requires_grad=False) / (1 - dropout) 18 | mask = mask.expand_as(x) 19 | return mask * x 20 | -------------------------------------------------------------------------------- /weight_drop.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.nn import Parameter 3 | from functools import wraps 4 | 5 | class WeightDrop(torch.nn.Module): 6 | def __init__(self, module, weights, dropout=0, variational=False): 7 | super(WeightDrop, self).__init__() 8 | self.module = module 9 | self.weights = weights 10 | self.dropout = dropout 11 | self.variational = variational 12 | self._setup() 13 | 14 | def widget_demagnetizer_y2k_edition(*args, **kwargs): 15 | # We need to replace flatten_parameters with a nothing function 16 | # It must be a function rather than a lambda as otherwise pickling explodes 17 | # We can't write boring code though, so ... WIDGET DEMAGNETIZER Y2K EDITION! 18 | # (╯°□°）╯︵ ┻━┻ 19 | return 20 | 21 | def _setup(self): 22 | # Terrible temporary solution to an issue regarding compacting weights re: CUDNN RNN 23 | if issubclass(type(self.module), torch.nn.RNNBase): 24 | self.module.flatten_parameters = self.widget_demagnetizer_y2k_edition 25 | 26 | for name_w in self.weights: 27 | print('Applying weight drop of {} to {}'.format(self.dropout, name_w)) 28 | w = getattr(self.module, name_w) 29 | del self.module._parameters[name_w] 30 | self.module.register_parameter(name_w + '_raw', Parameter(w.data)) 31 | 32 | def _setweights(self): 33 | for name_w in self.weights: 34 | raw_w = getattr(self.module, name_w + '_raw') 35 | w = None 36 | if self.variational: 37 | mask = torch.autograd.Variable(torch.ones(raw_w.size(0), 1)) 38 | if raw_w.is_cuda: mask = mask.cuda() 39 | mask = torch.nn.functional.dropout(mask, p=self.dropout, training=True) 40 | w = mask.expand_as(raw_w) * raw_w 41 | else: 42 | w = torch.nn.functional.dropout(raw_w, p=self.dropout, training=self.training) 43 | setattr(self.module, name_w, w) 44 | 45 | def forward(self, *args): 46 | self._setweights() 47 | return self.module.forward(*args) 48 | 49 | if __name__ == '__main__': 50 | import torch 51 | from weight_drop import WeightDrop 52 | 53 | # Input is (seq, batch, input) 54 | x = torch.autograd.Variable(torch.randn(2, 1, 10)).cuda() 55 | h0 = None 56 | 57 | ### 58 | 59 | print('Testing WeightDrop') 60 | print('=-=-=-=-=-=-=-=-=-=') 61 | 62 | ### 63 | 64 | print('Testing WeightDrop with Linear') 65 | 66 | lin = WeightDrop(torch.nn.Linear(10, 10), ['weight'], dropout=0.9) 67 | lin.cuda() 68 | run1 = [x.sum() for x in lin(x).data] 69 | run2 = [x.sum() for x in lin(x).data] 70 | 71 | print('All items should be different') 72 | print('Run 1:', run1) 73 | print('Run 2:', run2) 74 | 75 | assert run1[0] != run2[0] 76 | assert run1[1] != run2[1] 77 | 78 | print('---') 79 | 80 | ### 81 | 82 | print('Testing WeightDrop with LSTM') 83 | 84 | wdrnn = WeightDrop(torch.nn.LSTM(10, 10), ['weight_hh_l0'], dropout=0.9) 85 | wdrnn.cuda() 86 | 87 | run1 = [x.sum() for x in wdrnn(x, h0)[0].data] 88 | run2 = [x.sum() for x in wdrnn(x, h0)[0].data] 89 | 90 | print('First timesteps should be equal, all others should differ') 91 | print('Run 1:', run1) 92 | print('Run 2:', run2) 93 | 94 | # First time step, not influenced by hidden to hidden weights, should be equal 95 | assert run1[0] == run2[0] 96 | # Second step should not 97 | assert run1[1] != run2[1] 98 | 99 | print('---') -------------------------------------------------------------------------------- /yaringal_callbacks.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from tensorflow.contrib.keras.python.keras.callbacks import Callback 3 | from tensorflow.contrib.keras.python.keras import backend as K 4 | from tensorflow.contrib.keras.python.keras import models 5 | from tensorflow.contrib.keras.python.keras.engine.training import _standardize_input_data 6 | from sklearn.metrics import log_loss, mean_squared_error 7 | 8 | 9 | class ModelTest(Callback): 10 | ''' Test model at the end of every X epochs. 11 | The model is tested using both MC dropout and the dropout 12 | approximation. Output metrics for various losses are supported. 13 | # Arguments 14 | Xt: model inputs to test. 15 | Yt: model outputs to get accuracy / error (ground truth). 16 | T: number of samples to use in MC dropout. 17 | test_every_X_epochs: test every test_every_X_epochs epochs. 18 | batch_size: number of data points to put in each batch 19 | (often larger than training batch size). 20 | verbose: verbosity mode, 0 or 1. 21 | loss: a string from ['binary', 'categorical', 'euclidean'] 22 | used to calculate the testing metric. 23 | mean_y_train: mean of outputs in regression cases to add back 24 | to model output ('euclidean' loss). 25 | std_y_train: std of outputs in regression cases to add back 26 | to model output ('euclidean' loss). 27 | # References 28 | - [Dropout: A simple way to prevent neural networks from overfitting](http://jmlr.org/papers/v15/srivastava14a.html) 29 | - [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](http://arxiv.org/abs/1506.02142) 30 | ''' 31 | 32 | def __init__(self, Xt, Yt, T=10, test_every_X_epochs=1, batch_size=500, verbose=1, 33 | loss=None, mean_y_train=None, std_y_train=None): 34 | super(ModelTest, self).__init__() 35 | self.Xt = Xt 36 | self.Yt = np.array(Yt) 37 | self.T = T 38 | self.test_every_X_epochs = test_every_X_epochs 39 | self.batch_size = batch_size 40 | self.verbose = verbose 41 | self.loss = loss 42 | self.mean_y_train = mean_y_train 43 | self.std_y_train = std_y_train 44 | self._predict_stochastic = None 45 | self.history = [] 46 | 47 | def predict_stochastic(self, X, batch_size=128, verbose=0): 48 | '''Generate output predictions for the input samples 49 | batch by batch, using stochastic forward passes. If 50 | dropout is used at training, during prediction network 51 | units will be dropped at random as well. This procedure 52 | can be used for MC dropout (see [ModelTest callbacks](callbacks.md)). 53 | # Arguments 54 | X: the input data, as a numpy array. 55 | batch_size: integer. 56 | verbose: verbosity mode, 0 or 1. 57 | # Returns 58 | A numpy array of predictions. 59 | # References 60 | - [Dropout: A simple way to prevent neural networks from overfitting](http://jmlr.org/papers/v15/srivastava14a.html) 61 | - [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](http://arxiv.org/abs/1506.02142) 62 | ''' 63 | # https://stackoverflow.com/questions/44351054/keras-forward-pass-with-dropout 64 | X = _standardize_input_data( 65 | X, self.model.model._feed_input_names, self.model.model._feed_input_shapes, 66 | check_batch_axis=False 67 | ) 68 | if self._predict_stochastic is None: # we only get self.model after init 69 | self._predict_stochastic = K.function( 70 | [self.model.layers[0].input, K.learning_phase()], [self.model.layers[-1].output]) 71 | return self.model._predict_loop( 72 | self._predict_stochastic, X + [1.], batch_size, verbose)[:, 0] 73 | 74 | def on_epoch_end(self, epoch, logs={}): 75 | if epoch % self.test_every_X_epochs != 0: 76 | return 77 | model_output = self.model.predict(self.Xt, batch_size=self.batch_size, 78 | verbose=self.verbose)[:, 0] 79 | MC_model_output = [] 80 | for _ in range(self.T): 81 | MC_model_output += [self.predict_stochastic(self.Xt, 82 | batch_size=self.batch_size, 83 | verbose=self.verbose)] 84 | 85 | # assert not np.array_equal(MC_model_output[0], MC_model_output[1]) 86 | # assert not np.array_equal(MC_model_output[0], model_output) 87 | # tmp = self.model.predict(self.Xt, batch_size=self.batch_size, 88 | # verbose=self.verbose) 89 | # assert np.array_equal(tmp, model_output) 90 | 91 | MC_model_output = np.array(MC_model_output) 92 | MC_model_output_mean = np.mean(MC_model_output, 0) 93 | # print(MC_model_output_mean.shape) 94 | # print(self.Yt.shape) 95 | 96 | if self.loss == 'binary': 97 | standard_acc = np.mean(self.Yt == np.round(model_output.flatten())) 98 | MC_acc = np.mean(self.Yt == np.round( 99 | MC_model_output_mean.flatten())) 100 | standard_loss = log_loss(self.Yt, model_output) 101 | MC_loss = log_loss(self.Yt, MC_model_output_mean) 102 | print("Standard logloss/acc at epoch %05d: %0.4f/%.2f%%" % 103 | (epoch, float(standard_loss), standard_acc * 100)) 104 | print("MC logloss/acc at epoch %05d: %0.4f/%.2f%%" % 105 | (epoch, float(MC_loss), MC_acc * 100)) 106 | self.history.append( 107 | [float(standard_loss), float(MC_loss), standard_acc, MC_acc]) 108 | elif self.loss == 'categorical': 109 | standard_acc = np.mean( 110 | np.argmax(self.Yt, axis=-1) == np.argmax(model_output, axis=-1)) 111 | MC_acc = np.mean(np.argmax(self.Yt, axis=-1) == 112 | np.argmax(MC_model_output_mean, axis=-1)) 113 | print("Standard accuracy at epoch %05d: %0.5f" % 114 | (epoch, float(standard_acc))) 115 | print("MC accuracy at epoch %05d: %0.5f" % (epoch, float(MC_acc))) 116 | elif self.loss == 'euclidean': 117 | # print("Mean:", np.mean(model_output), "Max:", np.max(model_output)) 118 | # print("Mean:", np.mean(self.Yt), "Max:", np.max(self.Yt)) 119 | Yt_inverse_transformed = self.Yt * self.std_y_train + self.mean_y_train 120 | standard_err_raw = mean_squared_error(self.Yt, model_output) 121 | model_output = model_output * self.std_y_train + self.mean_y_train 122 | standard_err = mean_squared_error( 123 | Yt_inverse_transformed, model_output) 124 | MC_err_raw = mean_squared_error(self.Yt, MC_model_output_mean) 125 | MC_model_output_mean = MC_model_output_mean * \ 126 | self.std_y_train + self.mean_y_train 127 | MC_err = mean_squared_error( 128 | Yt_inverse_transformed, MC_model_output_mean) 129 | print("Standard error at epoch %03d: %0.4f/%0.4f" % 130 | (epoch, float(standard_err_raw), float(standard_err)**0.5)) 131 | print("MC error at epoch %03d: %0.4f/%0.4f" % 132 | (epoch, float(MC_err_raw), float(MC_err)**0.5)) 133 | self.history.append([float(standard_err), float(MC_err)]) 134 | else: 135 | raise Exception('No loss: ' + self.loss) 136 | -------------------------------------------------------------------------------- /yaringal_dataset.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from tensorflow.contrib.keras.python.keras.preprocessing import sequence 3 | from tensorflow.contrib.keras.python.keras.preprocessing import text 4 | 5 | 6 | class loader(object): 7 | def __init__(self, init_seed, maxlen, nb_words, skip_top, test_split): 8 | self.start_char = 1 9 | self.oov_char = 2 10 | self.index_from = 3 11 | 12 | files = ["Dennis+Schwartz", "James+Berardinelli", 13 | "Scott+Renshaw", "Steve+Rhodes"] 14 | texts, ratings = [], [] 15 | for file in files: 16 | with open("data/scaledata/" + file + "/subj." + file, "r") as f: 17 | texts += list(f) 18 | with open("data/scaledata/" + file + "/rating." + file, "r") as f: 19 | ratings += list(f) 20 | tokenizer = text.Tokenizer(filters='') 21 | tokenizer.fit_on_texts(texts) 22 | X = tokenizer.texts_to_sequences(texts) 23 | Y = [float(rating) for rating in ratings] 24 | 25 | # Shuffle data: 26 | np.random.seed(init_seed) 27 | np.random.shuffle(X) 28 | np.random.seed(init_seed) 29 | np.random.shuffle(Y) 30 | 31 | # Parse data 32 | X = [[self.start_char] + [w + self.index_from for w in x] for x in X] 33 | 34 | new_X = [] 35 | new_Y = [] 36 | for x, y in zip(X, Y): 37 | for i in range(0, len(x), maxlen): 38 | new_X.append(x[i:i + maxlen]) 39 | new_Y.append(y) 40 | X = np.array(new_X) 41 | Y = np.array(new_Y) 42 | # by convention, use 2 as OOV word 43 | # reserve 'index_from' (=3 by default) characters: 0 (padding), 1 (start), 2 (OOV) 44 | X = [[self.oov_char if (w >= nb_words or w < skip_top) 45 | else w for w in x] for x in X] 46 | 47 | self.X_train = X[:int(len(X) * (1 - test_split))] 48 | self.Y_train = Y[:int(len(X) * (1 - test_split))] 49 | self.mean_y_train = np.mean(self.Y_train) 50 | self.std_y_train = np.std(self.Y_train) 51 | self.Y_train = (self.Y_train - self.mean_y_train) / self.std_y_train 52 | 53 | self.X_test = X[int(len(X) * (1 - test_split)):] 54 | self.Y_test = Y[int(len(X) * (1 - test_split)):] 55 | self.Y_test = (self.Y_test - self.mean_y_train) / self.std_y_train 56 | 57 | print(len(self.X_train), 'train sequences') 58 | print(len(self.X_test), 'test sequences') 59 | 60 | print("Pad sequences (samples x time)") 61 | self.X_train = sequence.pad_sequences(self.X_train, maxlen=maxlen) 62 | self.X_test = sequence.pad_sequences(self.X_test, maxlen=maxlen) 63 | print('X_train shape:', self.X_train.shape) 64 | print('X_test shape:', self.X_test.shape) 65 | --------------------------------------------------------------------------------