├── .gitignore ├── 1. Basic Concepts.ipynb ├── 10. Convolutional Neural Networks II.ipynb ├── 11_Non supervised learning I .ipynb ├── 12. Non supervised learning II.ipynb ├── 13. Reinforcement learning.ipynb ├── 14. Uncertainty_and_Probabilistic_Layers.ipynb ├── 2. Automatic Differentiation.ipynb ├── 3-Tensorflow programming model (Solution).ipynb ├── 3_Tensorflow_programming_model.ipynb ├── 4. Tensorflow first learning models.ipynb ├── 5. Keras.ipynb ├── 6. Recurrent Neural Networks I.ipynb ├── 7. Recurrent Neural Networks II.ipynb ├── 8. Embeddings.ipynb ├── 9. Convolutional Neural Networks.ipynb ├── Assignment 1. Pixel regression.ipynb ├── Assignment 2. Ethics and Sentiment Classification.ipynb ├── Assignment 3. X-ray image classification.ipynb ├── Chollet-advanced-usage-of-recurrent-neural-networks.ipynb ├── DeepLearningMasterIntro2019.pdf ├── Dockerfile ├── LICENSE.txt ├── README.md ├── Uncertainty_and_Probabilistic_Layers.ipynb ├── data ├── Advertising.csv ├── NombresMujerBarcelona.txt ├── adult.data ├── adult.test ├── cat.txt ├── household_power_consumption.zip ├── iris_test.csv ├── iris_training.csv ├── jena_climate_2009_2016.csv.zip ├── monalisa.jpg ├── monalisa.png ├── negative-words.txt ├── otto.zip ├── positive-words.txt ├── t10k-images-idx3-ubyte.gz ├── t10k-labels-idx1-ubyte.gz ├── toponims.txt ├── train-images-idx3-ubyte.gz ├── train-labels-idx1-ubyte.gz ├── train_titanic.csv ├── vocab.txt ├── wiki106.txt.zip └── wordVectors.txt.zip ├── deeplearninginside2.png ├── images ├── FVMNLN.png ├── FVSBN.png ├── NADE.png ├── README.md ├── TanhReal.gif ├── alexnet.png ├── autoencoder.jpg ├── back.png ├── conv.png ├── conv1.png ├── conv2.png ├── deeplearninginside2.png ├── deepq1.png ├── deepq2.png ├── denoised_digits.png ├── densenet.png ├── dgan.png ├── dgan2.png ├── dgan3.png ├── dropout.png ├── exploding.png ├── fasterrcnn.png ├── fastrcnn.png ├── fastrcnn2.png ├── fword2vec-sg.png ├── g1.gif ├── g2.gif ├── gan1.png ├── googlenet.png ├── googlenet2.png ├── gru.png ├── inception.png ├── kar.png ├── loss_functions.png ├── lstm.png ├── maxpool.jpeg ├── merge.png ├── minibatch.png ├── mnistExamples.png ├── multi.png ├── par2vec.png ├── pipeline1.png ├── pipeline2.png ├── pixelrcnn.png ├── pong.jpg ├── rcnn.png ├── res1.png ├── resnet.png ├── result.png ├── ridge2.png ├── seq2seq.png ├── siamese1.png ├── siameseresult.png ├── slot.jpg ├── spectrogram.png ├── split.png ├── steeper.png ├── subsampling.png ├── t1.png ├── t10.png ├── t12.png ├── t2.png ├── t3.png ├── t4.png ├── t5.png ├── t6.png ├── t7.png ├── t8.png ├── t9.png ├── tf-gru.png ├── tf-lstm.png ├── ub.png ├── unrolling.png ├── vae1.png ├── vae2.png ├── vae3.png ├── vae4.png ├── vae5.png ├── vae6.png ├── vae7.png ├── vae_sampling.png ├── vanilla.png ├── vgg16.png ├── w2v1.png ├── weights.jpeg ├── word2vec-cbow.png └── zfnet.png └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | -------------------------------------------------------------------------------- /10. Convolutional Neural Networks II.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "10. Convolutional Neural Networks II.ipynb", 7 | "version": "0.3.2", 8 | "provenance": [], 9 | "include_colab_link": true 10 | }, 11 | "language_info": { 12 | "codemirror_mode": { 13 | "name": "ipython", 14 | "version": 3 15 | }, 16 | "file_extension": ".py", 17 | "mimetype": "text/x-python", 18 | "name": "python", 19 | "nbconvert_exporter": "python", 20 | "pygments_lexer": "ipython3", 21 | "version": "3.5.2" 22 | }, 23 | "kernelspec": { 24 | "name": "python3", 25 | "display_name": "Python 3" 26 | }, 27 | "accelerator": "GPU" 28 | }, 29 | "cells": [ 30 | { 31 | "cell_type": "markdown", 32 | "metadata": { 33 | "id": "view-in-github", 34 | "colab_type": "text" 35 | }, 36 | "source": [ 37 | "\"Open" 38 | ] 39 | }, 40 | { 41 | "metadata": { 42 | "id": "zlMderMf6iaX", 43 | "colab_type": "text" 44 | }, 45 | "cell_type": "markdown", 46 | "source": [ 47 | "# 10. Convolutional Neural Networks II\n", 48 | "## Large Convolutional Networks\n", 49 | "\n", 50 | "There are several architectures in the field of Convolutional Networks that have a name. The most common are:\n", 51 | "\n", 52 | "+ **LeNet**, 1990’s. \n", 53 | "
\n", 54 | "\"\" \n", 55 | "
\n", 56 | "\n", 57 | "\n", 58 | "\n", 59 | "+ **AlexNet**. 2012.\n", 60 | "
\n", 61 | "\"\"\n", 62 | "(Source: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)\n", 63 | "
\n", 64 | "\n", 65 | "> AlexNet has about 60 million parameters!\n", 66 | "\n", 67 | "\n", 68 | "+ **ZF Net**. The ILSVRC 2013 winner was a Convolutional Network from Matthew Zeiler and Rob Fergus. It became known as the ZFNet (short for Zeiler & Fergus Net). It was an improvement on AlexNet by tweaking the architecture hyperparameters, in particular by expanding the size of the middle convolutional layers and making the stride and filter size on the first layer smaller.\n", 69 | "
\n", 70 | "\"\" \n", 71 | "(Source: https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf)\n", 72 | "
\n", 73 | "\n", 74 | "\n", 75 | "+ **VGGNet**. The runner-up in ILSVRC 2014 was the network from Karen Simonyan and Andrew Zisserman that became known as the VGGNet. Its main contribution was in showing that the depth of the network is a critical component for good performance. Their final best network contains 16 CONV/FC layers and, appealingly, features an **extremely homogeneous architecture that only performs 3x3 convolutions and 2x2 pooling from the beginning to the end**. A downside of the VGGNet is that it is more expensive to evaluate and uses a lot more memory and parameters. Most of these parameters are in the first fully connected layer, and it was since found that these FC layers can be removed with no performance downgrade, significantly reducing the number of necessary parameters.\n", 76 | "\n", 77 | "\n", 78 | "
\n", 79 | "\"\" \n", 80 | "(Source: https://blog.heuritech.com/2016/02/29/a-brief-report-of-the-heuritech-deep-learning-meetup-5/)\n", 81 | "
" 82 | ] 83 | }, 84 | { 85 | "metadata": { 86 | "id": "xZnRS5306iaa", 87 | "colab_type": "code", 88 | "outputId": "fe4da8fb-84f5-4ad5-fd59-0b6c1284232e", 89 | "colab": { 90 | "base_uri": "https://localhost:8080/", 91 | "height": 1129 92 | } 93 | }, 94 | "cell_type": "code", 95 | "source": [ 96 | "# Small VGG-like convnet in Keras\n", 97 | "\n", 98 | "import numpy as np\n", 99 | "import keras\n", 100 | "from keras.models import Sequential\n", 101 | "from keras.layers import Dense, Dropout, Flatten\n", 102 | "from keras.layers import Conv2D, MaxPooling2D\n", 103 | "from keras.optimizers import SGD\n", 104 | "\n", 105 | "# Generate dummy data\n", 106 | "\n", 107 | "def to_categorical(y, num_classes=None):\n", 108 | " \"\"\"\n", 109 | " Converts a class vector (integers) to binary class matrix.\n", 110 | " \"\"\"\n", 111 | " y = np.array(y, dtype='int').ravel()\n", 112 | " if not num_classes:\n", 113 | " num_classes = np.max(y) + 1\n", 114 | " n = y.shape[0]\n", 115 | " categorical = np.zeros((n, num_classes))\n", 116 | " categorical[np.arange(n), y] = 1\n", 117 | " return categorical\n", 118 | "\n", 119 | "x_train = np.random.random((100, 100, 100, 3))\n", 120 | "y_train = to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)\n", 121 | "x_test = np.random.random((20, 100, 100, 3))\n", 122 | "y_test = to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)\n", 123 | "\n", 124 | "model = Sequential()\n", 125 | "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))\n", 126 | "model.add(Conv2D(32, (3, 3), activation=\"relu\"))\n", 127 | "model.add(MaxPooling2D(pool_size=(2, 2)))\n", 128 | "model.add(Dropout(0.25))\n", 129 | "\n", 130 | "model.add(Conv2D(32, (3, 3), activation=\"relu\"))\n", 131 | "model.add(Conv2D(32, (3, 3), activation=\"relu\"))\n", 132 | "model.add(MaxPooling2D(pool_size=(2, 2)))\n", 133 | "model.add(Dropout(0.25))\n", 134 | "\n", 135 | "model.add(Flatten())\n", 136 | "model.add(Dense(256, activation='relu'))\n", 137 | "model.add(Dropout(0.5))\n", 138 | "model.add(Dense(10, activation='softmax'))\n", 139 | "\n", 140 | "sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n", 141 | "model.compile(loss='categorical_crossentropy', optimizer=sgd)\n", 142 | "print(model.summary())\n", 143 | "\n", 144 | "model.fit(x_train, y_train, batch_size=32, epochs=10)\n", 145 | "score = model.evaluate(x_test, y_test, batch_size=32)" 146 | ], 147 | "execution_count": 0, 148 | "outputs": [ 149 | { 150 | "output_type": "stream", 151 | "text": [ 152 | "Using TensorFlow backend.\n" 153 | ], 154 | "name": "stderr" 155 | }, 156 | { 157 | "output_type": "stream", 158 | "text": [ 159 | "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", 160 | "Instructions for updating:\n", 161 | "Colocations handled automatically by placer.\n", 162 | "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n", 163 | "Instructions for updating:\n", 164 | "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n", 165 | "_________________________________________________________________\n", 166 | "Layer (type) Output Shape Param # \n", 167 | "=================================================================\n", 168 | "conv2d_1 (Conv2D) (None, 98, 98, 32) 896 \n", 169 | "_________________________________________________________________\n", 170 | "conv2d_2 (Conv2D) (None, 96, 96, 32) 9248 \n", 171 | "_________________________________________________________________\n", 172 | "max_pooling2d_1 (MaxPooling2 (None, 48, 48, 32) 0 \n", 173 | "_________________________________________________________________\n", 174 | "dropout_1 (Dropout) (None, 48, 48, 32) 0 \n", 175 | "_________________________________________________________________\n", 176 | "conv2d_3 (Conv2D) (None, 46, 46, 32) 9248 \n", 177 | "_________________________________________________________________\n", 178 | "conv2d_4 (Conv2D) (None, 44, 44, 32) 9248 \n", 179 | "_________________________________________________________________\n", 180 | "max_pooling2d_2 (MaxPooling2 (None, 22, 22, 32) 0 \n", 181 | "_________________________________________________________________\n", 182 | "dropout_2 (Dropout) (None, 22, 22, 32) 0 \n", 183 | "_________________________________________________________________\n", 184 | "flatten_1 (Flatten) (None, 15488) 0 \n", 185 | "_________________________________________________________________\n", 186 | "dense_1 (Dense) (None, 256) 3965184 \n", 187 | "_________________________________________________________________\n", 188 | "dropout_3 (Dropout) (None, 256) 0 \n", 189 | "_________________________________________________________________\n", 190 | "dense_2 (Dense) (None, 10) 2570 \n", 191 | "=================================================================\n", 192 | "Total params: 3,996,394\n", 193 | "Trainable params: 3,996,394\n", 194 | "Non-trainable params: 0\n", 195 | "_________________________________________________________________\n", 196 | "None\n", 197 | "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", 198 | "Instructions for updating:\n", 199 | "Use tf.cast instead.\n", 200 | "Epoch 1/10\n", 201 | "100/100 [==============================] - 6s 57ms/step - loss: 2.3112\n", 202 | "Epoch 2/10\n", 203 | "100/100 [==============================] - 0s 747us/step - loss: 2.3700\n", 204 | "Epoch 3/10\n", 205 | "100/100 [==============================] - 0s 746us/step - loss: 2.3099\n", 206 | "Epoch 4/10\n", 207 | "100/100 [==============================] - 0s 719us/step - loss: 2.2850\n", 208 | "Epoch 5/10\n", 209 | "100/100 [==============================] - 0s 725us/step - loss: 2.2990\n", 210 | "Epoch 6/10\n", 211 | "100/100 [==============================] - 0s 740us/step - loss: 2.3016\n", 212 | "Epoch 7/10\n", 213 | "100/100 [==============================] - 0s 778us/step - loss: 2.3113\n", 214 | "Epoch 8/10\n", 215 | "100/100 [==============================] - 0s 792us/step - loss: 2.2885\n", 216 | "Epoch 9/10\n", 217 | "100/100 [==============================] - 0s 758us/step - loss: 2.2925\n", 218 | "Epoch 10/10\n", 219 | "100/100 [==============================] - 0s 867us/step - loss: 2.2957\n", 220 | "20/20 [==============================] - 0s 6ms/step\n" 221 | ], 222 | "name": "stdout" 223 | } 224 | ] 225 | }, 226 | { 227 | "metadata": { 228 | "id": "WMiG37Tg6iah", 229 | "colab_type": "code", 230 | "outputId": "33c25635-a733-4069-a217-fae2215c046e", 231 | "colab": { 232 | "base_uri": "https://localhost:8080/", 233 | "height": 35 234 | } 235 | }, 236 | "cell_type": "code", 237 | "source": [ 238 | "# how to compute the numer of trainable and non trainable weights in a model\n", 239 | "\n", 240 | "from keras import backend as K\n", 241 | "import numpy\n", 242 | "\n", 243 | "trainable_count = int(numpy.sum([K.count_params(p) for p in set(model.trainable_weights)]))\n", 244 | "\n", 245 | "non_trainable_count = int(numpy.sum([K.count_params(p) for p in set(model.non_trainable_weights)]))\n", 246 | "\n", 247 | "print(trainable_count,non_trainable_count)" 248 | ], 249 | "execution_count": 0, 250 | "outputs": [ 251 | { 252 | "output_type": "stream", 253 | "text": [ 254 | "3996394 0\n" 255 | ], 256 | "name": "stdout" 257 | } 258 | ] 259 | }, 260 | { 261 | "metadata": { 262 | "id": "D3R-Gg0c6iak", 263 | "colab_type": "code", 264 | "outputId": "22fb793c-cd46-433f-afc4-c0e605890fd3", 265 | "colab": { 266 | "base_uri": "https://localhost:8080/", 267 | "height": 35 268 | } 269 | }, 270 | "cell_type": "code", 271 | "source": [ 272 | "# how to compute the memory allocated by the activations of a model\n", 273 | "\n", 274 | "batch = 1\n", 275 | "shapes_count = int(numpy.sum([numpy.prod(numpy.array([s if isinstance(s, int) \n", 276 | " else 1 for s in l.output_shape])) \n", 277 | " for l in model.layers]))\n", 278 | "memory = shapes_count * 4 * batch\n", 279 | "\n", 280 | "print(memory)" 281 | ], 282 | "execution_count": 0, 283 | "outputs": [ 284 | { 285 | "output_type": "stream", 286 | "text": [ 287 | "3643436\n" 288 | ], 289 | "name": "stdout" 290 | } 291 | ] 292 | }, 293 | { 294 | "metadata": { 295 | "id": "31DIGKll6iap", 296 | "colab_type": "text" 297 | }, 298 | "cell_type": "markdown", 299 | "source": [ 300 | "**Exercise**\n", 301 | "\n", 302 | "+ Why do we have 896 parameters in the ``convolution2d_1`` layer of the previous example?\n", 303 | "\n", 304 | "+ Compute the number of parameters of the original VGG16 (all CONV layers are 3x3).\n", 305 | "> The VGG16 architecture is: INPUT: [224x224x3] $\\rightarrow$ CONV3-64: [224x224x64] $\\rightarrow$ CONV3-64: [224x224x64] $\\rightarrow$ POOL2: [112x112x64] $\\rightarrow$ CONV3-128: [112x112x128] $\\rightarrow$ CONV3-128: [112x112x128] $\\rightarrow$ POOL2: [56x56x128] $\\rightarrow$ CONV3-256: [56x56x256] $\\rightarrow$ CONV3-256: [56x56x256] $\\rightarrow$ CONV3-256: [56x56x256] $\\rightarrow$ POOL2: [28x28x256] $\\rightarrow$ CONV3-512: [28x28x512] $\\rightarrow$ CONV3-512: [28x28x512] $\\rightarrow$ CONV3-512: [28x28x512] $\\rightarrow$ POOL2: [14x14x512] $\\rightarrow$ CONV3-512: [14x14x512] $\\rightarrow$ CONV3-512: [14x14x512] $\\rightarrow$ CONV3-512: [14x14x512] $\\rightarrow$ POOL2: [7x7x512] $\\rightarrow$ FC: [1x1x4096] $\\rightarrow$ FC: [1x1x4096] $\\rightarrow$ FC: [1x1x1000].\n", 306 | "\n", 307 | "+ The largest bottleneck to be aware of when constructing ConvNet architectures is the memory bottleneck. What is the necessary memory size (supposing that we need 4 bytes for each element) to store intermediate data?\n", 308 | "\n" 309 | ] 310 | }, 311 | { 312 | "metadata": { 313 | "id": "oyrajG_-6iaq", 314 | "colab_type": "code", 315 | "colab": {} 316 | }, 317 | "cell_type": "code", 318 | "source": [ 319 | "# your code here\n" 320 | ], 321 | "execution_count": 0, 322 | "outputs": [] 323 | }, 324 | { 325 | "metadata": { 326 | "id": "aYqr5XV66ias", 327 | "colab_type": "text" 328 | }, 329 | "cell_type": "markdown", 330 | "source": [ 331 | "## More Large Convolutional Networks\n", 332 | "\n", 333 | "\n", 334 | "+ **GoogLeNet**. The ILSVRC 2014 winner was a Convolutional Network from Szegedy et al. from Google. Its main contribution was the development of an **Inception Module** that dramatically reduced the number of parameters in the network (4M, compared to VGG with 138,357,544). Additionally, this paper uses Average Pooling instead of Fully Connected layers at the top of the ConvNet, eliminating a large amount of parameters that do not seem to matter much. There are also several followup versions to the GoogLeNet, most recently Inception-v4.\n", 335 | "\n", 336 | "\n", 337 | "
\n", 338 | "\"\" \n", 339 | "GoogLeNet Architecture. Source: https://arxiv.org/pdf/1409.4842v1.pdf\n", 340 | "
\n", 341 | "\n", 342 | "Blue Box: Convolution | Red Box: Pooling | Yelow Box: Softmax | Green Box: Normalization\n", 343 | "\n", 344 | "
\n", 345 | "\"\" \n", 346 | "Inception Layer. Source: https://arxiv.org/pdf/1409.4842v1.pdf\n", 347 | "
\n", 348 | "\n", 349 | "
\n", 350 | "\"\" \n", 351 | "GoogLeNet parameters and ops. Source: https://arxiv.org/pdf/1409.4842v1.pdf\n", 352 | "
\n", 353 | "\n", 354 | "> What is the role of 1x1 convolutions?\n", 355 | "\n", 356 | "+ **ResNet**. Residual Network developed by Kaiming He et al. was the winner of ILSVRC 2015. It features special **skip connections** and a heavy use of batch normalization. A Residual Network, or ResNet is a neural network architecture which solves the problem of vanishing gradients in the simplest way possible. If there is trouble sending the gradient signal backwards, why not provide the network with a shortcut at each layer to make things happen more smoothly? The architecture is also missing fully connected layers at the end of the network. \n", 357 | "\n", 358 | "
\n", 359 | "\"\" \n", 360 | "(Source: https://arxiv.org/pdf/1512.03385.pdf)\n", 361 | "
\n", 362 | "\n", 363 | "
\n", 364 | "\"\" \n", 365 | " \n", 366 | "(Source: https://arxiv.org/pdf/1512.03385.pdf)\n", 367 | "
" 368 | ] 369 | }, 370 | { 371 | "metadata": { 372 | "id": "XOHYi9jh6iat", 373 | "colab_type": "text" 374 | }, 375 | "cell_type": "markdown", 376 | "source": [ 377 | "## Deeper is better?\n", 378 | "\n", 379 | "When it comes to neural network design, the trend in the past few years has pointed in one direction: deeper. \n", 380 | "\n", 381 | "Whereas the state of the art only a few years ago consisted of networks which were roughly twelve layers deep, it is now not surprising to come across networks which are hundreds of layers deep. \n", 382 | "\n", 383 | "This move hasn’t just consisted of greater depth for depths sake. For many applications, the most prominent of which being object classification, the deeper the neural network, the better the performance.\n", 384 | "\n", 385 | "So the problem is to design a network in which the gradient can more easily reach all the layers of a network which might be dozens, or even hundreds of layers deep. This is the goal behind some of state of the art architectures: ResNets, HighwayNets, and DenseNets.\n", 386 | "\n", 387 | "**HighwayNets** builds on the ResNet in a pretty intuitive way. The Highway Network preserves the shortcuts introduced in the ResNet, but augments them with a learnable parameter to determine to what extent each layer should be a skip connection or a nonlinear connection. Layers in a Highway Network are defined as follows:\n", 388 | "\n", 389 | " $$ y = H(x, W_H) \\cdot T(x,W_T) + x \\cdot C(x, W_C) $$\n", 390 | " \n", 391 | "In this equation we can see an outline of two kinds of layers discussed: $y = H(x,W_H)$ mirrors the traditional layer, and $y = H(x,W_H) + x$ mirrors our residual unit. \n", 392 | "\n", 393 | "The traditional layer can be implemented as:\n", 394 | "\n", 395 | "```python\n", 396 | "def dense(x, input_size, output_size, activation):\n", 397 | " W = tf.Variable(tf.truncated_normal([input_size, output_size], stddev=0.1), name=\"weight\")\n", 398 | " b = tf.Variable(tf.constant(0.1, shape=[output_size]), name=\"bias\")\n", 399 | " y = activation(tf.matmul(x, W) + b)\n", 400 | " return y\n", 401 | "```\n", 402 | "\n", 403 | "What is new is the $T(x,W_t)$, the transform gate function and $C(x,W_C) = 1 - T(x,W_t)$, the carry gate function. What happens is that when the transform gate is 1, we pass through our activation (H) and suppress the carry gate (since it will be 0). When the carry gate is 1, we pass through the unmodified input (x), while the activation is suppressed.\n", 404 | "\n", 405 | "```python\n", 406 | "def highway(x, size, activation, carry_bias=-1.0):\n", 407 | " W_T = tf.Variable(tf.truncated_normal([size, size], stddev=0.1), name=\"weight_transform\")\n", 408 | " b_T = tf.Variable(tf.constant(carry_bias, shape=[size]), name=\"bias_transform\")\n", 409 | "\n", 410 | " W = tf.Variable(tf.truncated_normal([size, size], stddev=0.1), name=\"weight\")\n", 411 | " b = tf.Variable(tf.constant(0.1, shape=[size]), name=\"bias\")\n", 412 | "\n", 413 | " T = tf.sigmoid(tf.matmul(x, W_T) + b_T, name=\"transform_gate\")\n", 414 | " H = activation(tf.matmul(x, W) + b, name=\"activation\")\n", 415 | " C = tf.sub(1.0, T, name=\"carry_gate\")\n", 416 | "\n", 417 | " y = tf.add(tf.mul(H, T), tf.mul(x, C), \"y\")\n", 418 | " return y\n", 419 | "```\n", 420 | "\n", 421 | "With this kind of network you can train models with hundreds of layers.\n", 422 | "\n", 423 | "**DenseNet** takes the insights of the skip connection to the extreme. The idea here is that if connecting a skip connection from the previous layer improves performance, why not connect every layer to every other layer? That way there is always a direct route for the information backwards through the network.\n", 424 | "\n", 425 | "
\n", 426 | "\"\" \n", 427 | "(Source: https://arxiv.org/abs/1608.06993)\n", 428 | "
\n", 429 | "\n", 430 | "Instead of using an addition however, the DenseNet relies on stacking of layers. Mathematically this looks like:\n", 431 | "\n", 432 | "$$ y = f(x, x-1, x-2, \\dots, x-n) $$\n", 433 | "\n", 434 | "This architecture makes intuitive sense in both the feedforward and feed backward settings. In the feed-forward setting, a task may benefit from being able to get low-level feature activations in addition to high level feature activations. In classifying objects for example, a lower layer of the network may determine edges in an image, whereas a higher layer would determine larger-scale features such as presence of faces. There may be cases where being able to use information about edges can help in determining the correct object in a complex scene. In the backwards case, having all the layers connected allows us to quickly send gradients to their respective places in the network easily.\n", 435 | "\n" 436 | ] 437 | }, 438 | { 439 | "metadata": { 440 | "id": "Zg29A7wJ6iau", 441 | "colab_type": "text" 442 | }, 443 | "cell_type": "markdown", 444 | "source": [ 445 | "## Fully Convolutional Networks\n", 446 | "\n", 447 | "(Source: http://cs231n.github.io/convolutional-networks/#convert)\n", 448 | "\n", 449 | "The only difference between **Fully Connected (FC)** and **Convolutional (CONV)** layers is that the neurons in the CONV layer are connected only to a local region in the input, and that many of the neurons in a CONV volume share parameters. \n", 450 | "\n", 451 | "However, the neurons in both layers still compute dot products, so their functional form is identical.\n", 452 | "\n", 453 | "Then, it is easy to see that for any CONV layer there is an FC layer that implements the same forward function. The weight matrix would be a large matrix that is mostly zero except for at certain blocks (due to local connectivity) where the weights in many of the blocks are equal (due to parameter sharing).\n", 454 | "\n", 455 | "
\n", 456 | "\"\" \n", 457 | "
\n", 458 | "\n", 459 | "\n", 460 | "Conversely, any FC layer can be converted to a CONV layer. \n", 461 | "\n", 462 | "Let $F$ be the receptive field size of the CONV layer neurons and $K$ the depth (number of bands) of the CONV layer.\n", 463 | "\n", 464 | "For example, an FC layer with $K=4096$ that is looking at some input volume of size $7×7×512$ (this is a tensor with size $(7×7×512, 4096$) can be equivalently expressed as $4096$ CONV layers with $F=7,K=512$ (this are $4096$ $(7,7,512)$ matrices). \n", 465 | "\n", 466 | "This can be very useful, bacause now we can apply the network to arbitrary large images!" 467 | ] 468 | }, 469 | { 470 | "metadata": { 471 | "id": "B3HZ8p-g6iau", 472 | "colab_type": "text" 473 | }, 474 | "cell_type": "markdown", 475 | "source": [ 476 | "## Object Detection and Segmentation\n", 477 | "(Source: https://blog.athelas.com/a-brief-history-of-cnns-in-image-segmentation-from-r-cnn-to-mask-r-cnn-34ea83205de4)\n", 478 | "\n", 479 | "In classification, there’s generally an image with a single object as the focus and the task is to say what that image is. But when we look at the world around us, we see complicated sights with multiple overlapping objects, and different backgrounds and we not only classify these different objects but also identify their boundaries, differences, and relations to one another!\n", 480 | "\n", 481 | "To what extent do CNN generalize to object detection? Object detection is the task of finding the different objects in an image and classifying them.\n", 482 | "\n", 483 | "### R-CNN\n", 484 | "\n", 485 | "A team, comprised of Ross Girshick (a name we’ll see again), Jeff Donahue, and Trevor Darrel found that this problem can be solved with AlexNet by testing on the PASCAL VOC Challenge, a popular object detection challenge akin to ImageNet.\n", 486 | "\n", 487 | "The goal of R-CNN is to take in an image, and correctly identify where the main objects (via a bounding box) in the image.\n", 488 | "\n", 489 | ">Inputs: Image\n", 490 | "\n", 491 | ">Outputs: Bounding boxes + labels for each object in the image.\n", 492 | "\n", 493 | "But how do we find out where these bounding boxes are? R-CNN proposes a bunch of boxes in the image and see if any of them actually correspond to an object.\n", 494 | "\n", 495 | "R-CNN creates these bounding boxes, or region proposals, using a process called Selective Search (see http://www.cs.cornell.edu/courses/cs7670/2014sp/slides/VisionSeminar14.pdf). \n", 496 | "\n", 497 | "At a high level, Selective Search looks at the image through windows of different sizes, and for each size tries to group together adjacent pixels by texture, color, or intensity to identify objects.\n", 498 | "\n", 499 | "Once the proposals are created, R-CNN warps the region to a standard square size and passes it through to a modified version of AlexNet.\n", 500 | "\n", 501 | "On the final layer of the CNN, R-CNN adds a Support Vector Machine (SVM) that simply classifies whether this is an object, and if so what object. \n", 502 | "\n", 503 | "
\n", 504 | "\"\" \n", 505 | "
\n", 506 | "\n", 507 | "Now, having found the object in the box, can we tighten the box to fit the true dimensions of the object? We can, and this is the final step of R-CNN. R-CNN runs a simple linear regression on the region proposal to generate tighter bounding box coordinates to get our final result. Here are the inputs and outputs of this regression model:\n", 508 | "\n", 509 | "> Inputs: sub-regions of the image corresponding to objects.\n", 510 | "\n", 511 | "> Outputs: New bounding box coordinates for the object in the sub-region.\n", 512 | "\n", 513 | "\n", 514 | "\n", 515 | "### Fast R-CNN\n", 516 | "\n", 517 | "R-CNN works really well, but is really quite slow for a few simple reasons:\n", 518 | "+ It requires a forward pass of the CNN (AlexNet) for every single region proposal for every single image (that’s around 2000 forward passes per image!).\n", 519 | "+ It has to train three different models separately - the CNN to generate image features, the classifier that predicts the class, and the regression model to tighten the bounding boxes. This makes the pipeline extremely hard to train.\n", 520 | "\n", 521 | "In 2015, Ross Girshick, the first author of R-CNN, solved both these problems, leading to Fast R-CNN. \n", 522 | "\n", 523 | "For the forward pass of the CNN, Girshick realized that for each image, a lot of proposed regions for the image invariably overlapped causing us to run the same CNN computation again and again (~2000 times!). His insight was simple — Why not run the CNN just once per image and then find a way to share that computation across the ~2000 proposals?\n", 524 | "\n", 525 | "This is exactly what Fast R-CNN does using a technique known as **RoIPool** (Region of Interest Pooling). At its core, RoIPool shares the forward pass of a CNN for an image across its subregions. In the image below, notice how the CNN features for each region are obtained by selecting a corresponding region from the CNN’s feature map. Then, the features in each region are pooled (usually using max pooling). So all it takes us is one pass of the original image as opposed to ~2000!\n", 526 | "\n", 527 | "
\n", 528 | "\"\" \n", 529 | "(Source: Stanford’s CS231N slides by Fei Fei Li, Andrei Karpathy, and Justin Johnson)\n", 530 | "
\n", 531 | "\n", 532 | "\n", 533 | "The second insight of Fast R-CNN is to jointly train the CNN, classifier, and bounding box regressor in a single model. Where earlier we had different models to extract image features (CNN), classify (SVM), and tighten bounding boxes (regressor), Fast R-CNN instead used a single network to compute all three.\n", 534 | "\n", 535 | "
\n", 536 | "\"\" \n", 537 | "(Source: https://www.slideshare.net/simplyinsimple/detection-52781995)\n", 538 | "
\n", 539 | "\n", 540 | "### Faster R-CNN\n", 541 | "\n", 542 | "Even with all these advancements, there was still one remaining bottleneck in the Fast R-CNN process — the region proposer. As we saw, the very first step to detecting the locations of objects is generating a bunch of potential bounding boxes or regions of interest to test. In Fast R-CNN, these proposals were created using Selective Search, a fairly slow process that was found to be the bottleneck of the overall process.\n", 543 | "\n", 544 | "In the middle 2015, a team at Microsoft Research composed of Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun, found a way to make the region proposal step almost cost free through an architecture they (creatively) named Faster R-CNN.\n", 545 | "\n", 546 | "The insight of Faster R-CNN was that region proposals depended on features of the image that were already calculated with the forward pass of the CNN (first step of classification). So why not reuse those same CNN results for region proposals instead of running a separate selective search algorithm?\n", 547 | "\n", 548 | "
\n", 549 | "\"\" \n", 550 | "(Source: https://arxiv.org/abs/1506.01497)\n", 551 | "
\n", 552 | "\n", 553 | "Here are the inputs and outputs of their model:\n", 554 | "\n", 555 | "> Inputs: Images (Notice how region proposals are not needed).\n", 556 | "\n", 557 | "> Outputs: Classifications and bounding box coordinates of objects in the images.\n", 558 | "\n", 559 | "### Mask R-CNN\n", 560 | "\n", 561 | "So far, we’ve seen how we’ve been able to use CNN features in many interesting ways to effectively locate different objects in an image with bounding boxes.\n", 562 | "\n", 563 | "Can we extend such techniques to go one step further and locate exact pixels of each object instead of just bounding boxes? This problem, known as image segmentation, is what Kaiming He and a team of researchers, including Girshick, explored at Facebook AI using an architecture known as Mask R-CNN.\n", 564 | "\n", 565 | "Given that Faster R-CNN works so well for object detection, could we extend it to also carry out pixel level segmentation? \n", 566 | "\n", 567 | "Mask R-CNN does this by adding a branch to Faster R-CNN that outputs a binary mask that says whether or not a given pixel is part of an object. The branch (in white in the above image), as before, is just a Fully Convolutional Network on top of a CNN based feature map. Here are its inputs and outputs:\n", 568 | "\n", 569 | "> Inputs: CNN Feature Map.\n", 570 | "> Outputs: Matrix with 1s on all locations where the pixel belongs to the object and 0s elsewhere (this is known as a binary mask).\n", 571 | "\n", 572 | "
\n", 573 | "\"\" \n", 574 | "(Source: https://arxiv.org/abs/1703.06870)\n", 575 | "
" 576 | ] 577 | }, 578 | { 579 | "metadata": { 580 | "id": "4QlgQgrl6iav", 581 | "colab_type": "text" 582 | }, 583 | "cell_type": "markdown", 584 | "source": [ 585 | "## 1D-Conv for text classification\n", 586 | "\n", 587 | "**IMDB Movie reviews sentiment classification**: Dataset of 25,000 movies reviews from IMDB, labeled by sentiment (positive/negative). Reviews have been preprocessed, and each review is encoded as a sequence of word indexes (integers). For convenience, words are indexed by overall frequency in the dataset, so that for instance the integer \"3\" encodes the 3rd most frequent word in the data. This allows for quick filtering operations such as: \"only consider the top 10,000 most common words, but eliminate the top 20 most common words\".\n", 588 | "\n", 589 | "The seminal research paper on this subject was published by Yoon Kim on 2014. In this paper Yoon Kim has laid the foundations for how to model and process text by convolutional neural networks for the purpose of sentiment analysis. He has shown that by simple one-dimentional convolutional networks, one can develops very simple neural networks that reach 90% accuracy very quickly.\n", 590 | "\n", 591 | "Here is the text of an example review from our dataset:\n", 592 | "\n", 593 | "
\n", 594 | "\"\" \n", 595 | "
" 596 | ] 597 | }, 598 | { 599 | "metadata": { 600 | "id": "pp2zq6BWCoVi", 601 | "colab_type": "code", 602 | "colab": { 603 | "base_uri": "https://localhost:8080/", 604 | "height": 72 605 | }, 606 | "outputId": "a1cc3933-5049-4c7f-d0e2-a6e6bc959a52" 607 | }, 608 | "cell_type": "code", 609 | "source": [ 610 | "!pip install numpy==1.16.2" 611 | ], 612 | "execution_count": 2, 613 | "outputs": [ 614 | { 615 | "output_type": "stream", 616 | "text": [ 617 | "Requirement already satisfied: numpy==1.16.2 in /usr/local/lib/python3.6/dist-packages (1.16.2)\n", 618 | "\n" 619 | ], 620 | "name": "stdout" 621 | } 622 | ] 623 | }, 624 | { 625 | "metadata": { 626 | "id": "6sgz-wbhCuF2", 627 | "colab_type": "code", 628 | "colab": { 629 | "base_uri": "https://localhost:8080/", 630 | "height": 34 631 | }, 632 | "outputId": "7151d57f-31c9-4484-a20e-85f3b83527a5" 633 | }, 634 | "cell_type": "code", 635 | "source": [ 636 | "import numpy as np\n", 637 | "print(np.__version__)" 638 | ], 639 | "execution_count": 4, 640 | "outputs": [ 641 | { 642 | "output_type": "stream", 643 | "text": [ 644 | "1.16.2\n" 645 | ], 646 | "name": "stdout" 647 | } 648 | ] 649 | }, 650 | { 651 | "metadata": { 652 | "id": "bZolXdO46iaw", 653 | "colab_type": "code", 654 | "outputId": "4584bda2-8dd1-44c0-a561-c7d0b66c5226", 655 | "colab": { 656 | "base_uri": "https://localhost:8080/", 657 | "height": 575 658 | } 659 | }, 660 | "cell_type": "code", 661 | "source": [ 662 | "'''\n", 663 | "This example demonstrates the use of Convolution1D for text classification.\n", 664 | "'''\n", 665 | "\n", 666 | "from __future__ import print_function\n", 667 | "import numpy as np\n", 668 | "import tensorflow as tf\n", 669 | "np.random.seed(1337) # for reproducibility\n", 670 | "\n", 671 | "from tensorflow.keras.preprocessing import sequence\n", 672 | "from tensorflow.keras.models import Sequential\n", 673 | "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n", 674 | "from tensorflow.keras.layers import Embedding\n", 675 | "from tensorflow.keras.layers import Conv1D, MaxPooling1D\n", 676 | "from tensorflow.keras.datasets import imdb\n", 677 | "\n", 678 | "\n", 679 | "# set parameters:\n", 680 | "max_features = 5000\n", 681 | "maxlen = 100\n", 682 | "batch_size = 32\n", 683 | "embedding_dims = 100\n", 684 | "nb_filter = 250\n", 685 | "filter_length = 3\n", 686 | "hidden_dims = 250\n", 687 | "nb_epoch = 10\n", 688 | "\n", 689 | "print('Loading data...')\n", 690 | "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=max_features)\n", 691 | "print(len(X_train), ' train sequences \\n')\n", 692 | "print(len(X_test), ' test sequences \\n')\n", 693 | "\n", 694 | "print('Pad sequences (samples x time)')\n", 695 | "X_train = sequence.pad_sequences(X_train, maxlen=maxlen)\n", 696 | "X_test = sequence.pad_sequences(X_test, maxlen=maxlen)\n", 697 | "print('X_train shape:', X_train.shape)\n", 698 | "print('X_test shape:', X_test.shape)\n", 699 | "\n", 700 | "print('Build model...')\n", 701 | "model = Sequential()\n", 702 | "\n", 703 | "# we start off with an efficient embedding layer which maps\n", 704 | "# our vocab indices into embedding_dims dimensions\n", 705 | "model.add(Embedding(max_features, embedding_dims, input_length=maxlen))\n", 706 | "model.add(Dropout(0.25))\n", 707 | "\n", 708 | "# we add a Convolution1D, which will learn nb_filter\n", 709 | "# word group filters of size filter_length:\n", 710 | "model.add(Conv1D(padding=\"valid\", \n", 711 | " kernel_size=3, \n", 712 | " filters=250, \n", 713 | " strides=1, \n", 714 | " activation=\"relu\"))\n", 715 | "# we use standard max pooling (halving the output of the previous layer):\n", 716 | "model.add(MaxPooling1D(pool_size=2))\n", 717 | "\n", 718 | "model.add(Conv1D(padding=\"valid\", \n", 719 | " kernel_size=3, \n", 720 | " filters=250, \n", 721 | " strides=1, \n", 722 | " activation=\"relu\"))\n", 723 | "model.add(MaxPooling1D(pool_size=2))\n", 724 | "\n", 725 | "\n", 726 | "# We flatten the output of the conv layer,\n", 727 | "# so that we can add a vanilla dense layer:\n", 728 | "model.add(Flatten())\n", 729 | "\n", 730 | "# We add a vanilla hidden layer:\n", 731 | "model.add(Dense(hidden_dims))\n", 732 | "model.add(Dropout(0.25))\n", 733 | "model.add(Activation('relu'))\n", 734 | "\n", 735 | "# We project onto a single unit output layer, and squash it with a sigmoid:\n", 736 | "model.add(Dense(1))\n", 737 | "model.add(Activation('sigmoid'))\n", 738 | "\n", 739 | "model.compile(loss='binary_crossentropy',\n", 740 | " optimizer='rmsprop',\n", 741 | " metrics=['accuracy'])\n", 742 | "model.fit(X_train, y_train,\n", 743 | " batch_size=batch_size,\n", 744 | " epochs=nb_epoch,\n", 745 | " validation_data=(X_test, y_test))" 746 | ], 747 | "execution_count": 5, 748 | "outputs": [ 749 | { 750 | "output_type": "stream", 751 | "text": [ 752 | "Loading data...\n", 753 | "25000 train sequences \n", 754 | "\n", 755 | "25000 test sequences \n", 756 | "\n", 757 | "Pad sequences (samples x time)\n", 758 | "X_train shape: (25000, 100)\n", 759 | "X_test shape: (25000, 100)\n", 760 | "Build model...\n", 761 | "Train on 25000 samples, validate on 25000 samples\n", 762 | "Epoch 1/10\n", 763 | "25000/25000 [==============================] - 10s 384us/sample - loss: 0.4533 - acc: 0.7702 - val_loss: 0.3769 - val_acc: 0.8333\n", 764 | "Epoch 2/10\n", 765 | "25000/25000 [==============================] - 6s 222us/sample - loss: 0.3199 - acc: 0.8620 - val_loss: 0.3270 - val_acc: 0.8564\n", 766 | "Epoch 3/10\n", 767 | "25000/25000 [==============================] - 6s 223us/sample - loss: 0.2845 - acc: 0.8836 - val_loss: 0.3240 - val_acc: 0.8582\n", 768 | "Epoch 4/10\n", 769 | "25000/25000 [==============================] - 6s 236us/sample - loss: 0.2559 - acc: 0.8992 - val_loss: 0.3555 - val_acc: 0.8459\n", 770 | "Epoch 5/10\n", 771 | "25000/25000 [==============================] - 6s 243us/sample - loss: 0.2326 - acc: 0.9072 - val_loss: 0.4883 - val_acc: 0.8101\n", 772 | "Epoch 6/10\n", 773 | "25000/25000 [==============================] - 6s 223us/sample - loss: 0.2097 - acc: 0.9195 - val_loss: 0.5515 - val_acc: 0.7976\n", 774 | "Epoch 7/10\n", 775 | "25000/25000 [==============================] - 6s 224us/sample - loss: 0.1858 - acc: 0.9300 - val_loss: 0.4102 - val_acc: 0.8372\n", 776 | "Epoch 8/10\n", 777 | "25000/25000 [==============================] - 6s 221us/sample - loss: 0.1644 - acc: 0.9378 - val_loss: 0.3814 - val_acc: 0.8463\n", 778 | "Epoch 9/10\n", 779 | "25000/25000 [==============================] - 6s 223us/sample - loss: 0.1430 - acc: 0.9480 - val_loss: 0.5229 - val_acc: 0.8366\n", 780 | "Epoch 10/10\n", 781 | "25000/25000 [==============================] - 6s 220us/sample - loss: 0.1291 - acc: 0.9537 - val_loss: 0.4734 - val_acc: 0.8504\n" 782 | ], 783 | "name": "stdout" 784 | }, 785 | { 786 | "output_type": "execute_result", 787 | "data": { 788 | "text/plain": [ 789 | "" 790 | ] 791 | }, 792 | "metadata": { 793 | "tags": [] 794 | }, 795 | "execution_count": 5 796 | } 797 | ] 798 | } 799 | ] 800 | } -------------------------------------------------------------------------------- /3-Tensorflow programming model (Solution).ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "nbpresent": { 7 | "id": "58f4f8ea-e41a-469a-a078-bb1e1725a1d3" 8 | } 9 | }, 10 | "source": [ 11 | "### Exercise\n", 12 | "\n", 13 | "Let's built and visualize and complex model:\n", 14 | "\n", 15 | "+ Our inputs will be placeholders.\n", 16 | "+ The model will take in a single vector of any lenght.\n", 17 | "+ The graph will be segmented in name scopes.\n", 18 | "+ We will accumulate the total value of all outputs over time.\n", 19 | "+ At each run, we are going to save the output of the graph, the accumulated total of all outputs, and the average value of all outputs to disk for use in `tensorboard`.\n", 20 | "\n", 21 | "\n", 22 | "![alt text](images/t6.png)\n" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 1, 28 | "metadata": { 29 | "ExecuteTime": { 30 | "end_time": "2019-03-05T16:27:52.921775Z", 31 | "start_time": "2019-03-05T16:27:50.195592Z" 32 | }, 33 | "code_folding": [], 34 | "nbpresent": { 35 | "id": "a8218e4e-4edc-48b5-a6a3-3af3169dee3f" 36 | } 37 | }, 38 | "outputs": [ 39 | { 40 | "name": "stdout", 41 | "output_type": "stream", 42 | "text": [ 43 | "WARNING:tensorflow:From /Users/jordivitria/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", 44 | "Instructions for updating:\n", 45 | "Colocations handled automatically by placer.\n", 46 | "WARNING:tensorflow:From :46: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", 47 | "Instructions for updating:\n", 48 | "Deprecated in favor of operator or tf.math.divide.\n" 49 | ] 50 | } 51 | ], 52 | "source": [ 53 | "import tensorflow as tf\n", 54 | "import numpy as np\n", 55 | "\n", 56 | "tf.reset_default_graph()\n", 57 | "\n", 58 | "# Explicitly create a Graph object\n", 59 | "graph = tf.Graph()\n", 60 | "\n", 61 | "with graph.as_default():\n", 62 | " \n", 63 | " with tf.name_scope(\"variables\"):\n", 64 | " # Variable to keep track of how many times the graph has been run\n", 65 | " global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name=\"global_step\")\n", 66 | "\n", 67 | " # Variable that keeps track of the sum of all output values over time:\n", 68 | " total_output = tf.Variable(0.0, dtype=tf.float32, trainable=False, name=\"total_output\")\n", 69 | "\n", 70 | " with tf.name_scope(\"transformation\"):\n", 71 | "\n", 72 | " # Separate input layer\n", 73 | " with tf.name_scope(\"input\"):\n", 74 | " # Create input placeholder- takes in a Vector\n", 75 | " a = tf.placeholder(tf.float32, shape=[None], name=\"input_placeholder_a\")\n", 76 | "\n", 77 | " # Separate middle layer\n", 78 | " with tf.name_scope(\"intermediate_layer\"):\n", 79 | " b = tf.reduce_sum(a, name=\"sum_b\")\n", 80 | " c = tf.reduce_prod(a, name=\"product_c\")\n", 81 | "\n", 82 | " # Separate output layer\n", 83 | " with tf.name_scope(\"output\"):\n", 84 | " output = tf.add(b, c, name=\"output\")\n", 85 | " \n", 86 | " \n", 87 | " with tf.name_scope(\"update\"):\n", 88 | " # Increments the total_output Variable by the latest output\n", 89 | " update_total = total_output.assign_add(output)\n", 90 | "\n", 91 | " # Increments the above `global_step` Variable, should be run whenever the graph is run\n", 92 | " increment_step = global_step.assign_add(1)\n", 93 | "\n", 94 | " \n", 95 | " # Summary Operations\n", 96 | " with tf.name_scope(\"summaries\"):\n", 97 | " # Calculating average (avg = total/steps)\n", 98 | " avg = tf.div(update_total, tf.cast(increment_step, tf.float32), name=\"average\")\n", 99 | "\n", 100 | " # Creates summaries for output node\n", 101 | " tf.summary.scalar(\"output_summary\", output)\n", 102 | " tf.summary.scalar(\"total_summary\", update_total)\n", 103 | " tf.summary.scalar(\"average_summary\", avg)\n", 104 | " \n", 105 | " # Global Variables and Operations\n", 106 | " with tf.name_scope(\"global_ops\"):\n", 107 | " # Initialization Op\n", 108 | " init = tf.global_variables_initializer()\n", 109 | " # Merge all summaries[…]\n", 110 | " merged_summaries = tf.summary.merge_all()\n", 111 | " # Start a Session, using the explicitly created Graph\n", 112 | "sess = tf.Session(graph=graph)\n", 113 | "\n", 114 | "# Open a SummaryWriter to save summaries\n", 115 | "writer = tf.summary.FileWriter('./improved_graph', graph)\n", 116 | "\n", 117 | "# Initialize Variables\n", 118 | "sess.run(init)" 119 | ] 120 | }, 121 | { 122 | "cell_type": "markdown", 123 | "metadata": { 124 | "nbpresent": { 125 | "id": "76d05e86-509b-494a-8364-953c74c1aadb" 126 | } 127 | }, 128 | "source": [ 129 | "Let's write a function to run the graph several times:" 130 | ] 131 | }, 132 | { 133 | "cell_type": "code", 134 | "execution_count": 2, 135 | "metadata": { 136 | "ExecuteTime": { 137 | "end_time": "2019-03-05T16:27:56.374145Z", 138 | "start_time": "2019-03-05T16:27:56.370352Z" 139 | }, 140 | "nbpresent": { 141 | "id": "ac2057e4-ca62-4985-af03-04ecaf7c41b5" 142 | } 143 | }, 144 | "outputs": [], 145 | "source": [ 146 | "def run_graph(input_tensor):\n", 147 | " \"\"\"\n", 148 | " Helper function; runs the graph with given input tensor and saves summaries\n", 149 | " \"\"\"\n", 150 | " feed_dict = {a: input_tensor}\n", 151 | " out, step, summary = sess.run([output, increment_step, merged_summaries], \n", 152 | " feed_dict=feed_dict)\n", 153 | " writer.add_summary(summary, global_step=step)" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": 3, 159 | "metadata": { 160 | "ExecuteTime": { 161 | "end_time": "2019-03-05T16:27:57.426704Z", 162 | "start_time": "2019-03-05T16:27:57.393916Z" 163 | }, 164 | "nbpresent": { 165 | "id": "47fcb7a3-7d43-4dfc-9b03-2fc208b165e6" 166 | } 167 | }, 168 | "outputs": [], 169 | "source": [ 170 | "# Run the graph with various inputs\n", 171 | "run_graph([2,8])\n", 172 | "run_graph([3,1,3,3])\n", 173 | "run_graph([8])\n", 174 | "run_graph([1,2,3])\n", 175 | "run_graph([11,4])\n", 176 | "run_graph([4,1])\n", 177 | "run_graph([7,3,1])\n", 178 | "run_graph([6,3])\n", 179 | "run_graph([0,2])\n", 180 | "run_graph([4,5,6])\n", 181 | "\n", 182 | "# Write the summaries to disk\n", 183 | "writer.flush()\n", 184 | "\n", 185 | "# Close the SummaryWriter\n", 186 | "writer.close()\n", 187 | "\n", 188 | "# Close the session\n", 189 | "sess.close()" 190 | ] 191 | }, 192 | { 193 | "cell_type": "markdown", 194 | "metadata": { 195 | "nbpresent": { 196 | "id": "4adbbc73-2fc9-4f83-8c54-081b32a3375e" 197 | } 198 | }, 199 | "source": [ 200 | "To start TensorBoard after running 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"toc_position": {}, 968 | "toc_section_display": true, 969 | "toc_window_display": false 970 | } 971 | }, 972 | "nbformat": 4, 973 | "nbformat_minor": 1 974 | } 975 | -------------------------------------------------------------------------------- /DeepLearningMasterIntro2019.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DataScienceUB/DeepLearningMaster2019/e4106e183a2c171ef62b1c3bcdc702667af62b0b/DeepLearningMasterIntro2019.pdf -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | 2 | FROM gcr.io/tensorflow/tensorflow:1.5.0-py3 3 | #Install packages 4 | RUN DEBIAN_FRONTEND=noninteractive apt-get update 5 | RUN DEBIAN_FRONTEND=noninteractive apt-get -qqy install wget python3-pip git 6 | RUN DEBIAN_FRONTEND=noninteractive pip3 install --upgrade pip 7 | RUN DEBIAN_FRONTEND=noninteractive pip3 install tqdm seaborn keras edward autograd pymc3 gym gensim 8 | 9 | #Remove examples 10 | RUN rm -Rf * -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Jordi Vitrià 4 | Permission is hereby granted, free of charge, to any person obtaining a copy 5 | of this software and associated documentation files (the "Software"), to deal 6 | in the Software without restriction, including without limitation the rights 7 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 8 | copies of the Software, and to permit persons to whom the Software is 9 | furnished to do so, subject to the following conditions: 10 | 11 | The above copyright notice and this permission notice shall be included in all 12 | copies or substantial portions of the Software. 13 | 14 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 15 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 16 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 17 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 18 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 19 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 20 | SOFTWARE. 21 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 |

4 | This is an outdated repository corresponding to the 2018/2019 UB MSc Course. If you are looking for the 2019/2020 course, go to https://github.com/DataScienceUB/DeepLearningMaster20192020 5 |

6 | 7 |

8 | About screen 9 |

10 | 11 | ## DeepLearningMaster Repository 12 | 13 | This repository contains notebooks used in DEEP LEARNING COURSE of the [MASTER IN FUNDAMENTAL PRINCIPLES OF DATA SCIENCE](http://www.ub.edu/datascience/master/) of the Universitat de Barcelona. 14 | 15 | ## Course Description 16 | 17 | Deep learning is one of the fastest growing areas of machine learning and a hot topic in both academia and industry. This course will cover the basics of deep learning by using a hands-on approach. 18 | 19 | ## Course Instructor 20 | 21 | [Jordi Vitrià](http://www.ub.edu/cvub/jordivitria/) 22 | 23 | ## Class Time and Location 24 | 25 | + 2ond Semester (February - May, 2019) 26 | + Lecture: Tuesday 15:00h-17:00h 27 | + Location: Aula B1, Facultat de Matemàtiques i Informàtica, Universitat de Barcelona. [Map](https://www.google.es/maps/place/Gran+Via+de+les+Corts+Catalanes,+585,+08007+Barcelona/@41.3865736,2.1619408,17z/data=!3m1!4b1!4m5!3m4!1s0x12a4a28cbeee3689:0x4b4a8ba716765923!8m2!3d41.3865736!4d2.1641295?hl=ca). 28 | 29 | ## Prerequisites 30 | 31 | + Proficiency in Python (3.6): All class assignments will be in Python (using ``tensorflow`` and ``keras``). 32 | + Calculus, Linear Algebra, Optimization: You should be comfortable taking derivatives and understanding matrix vector operations and notation. 33 | + Basic Probability and Statistics. 34 | + Machine Learning. 35 | 36 | If you are not used to Git, you can complete this free online git [course](https://try.github.io/levels/1/challenges/1) 37 | 38 | ## Grading 39 | 40 | + Assignment #1: 30%. Submission deadline (UB Campus Virtual): **March 19th, 2019.** 41 | + Assignment #2: 30%. Submission deadline (UB Campus Virtual): **April 20th, 2019.** 42 | + Assignment #3: 40%. Submission deadline (UB Campus Virtual): **June 2nd, 2019** 43 | 44 | Study groups are allowed but we expect students to understand and complete their own assignments and to hand in one assignment per student. 45 | 46 | ## Course Agenda 47 |
    48 |
  1. Introduction to Deep Learning and its applications. Using the Jupyter notebook & Docker. Software stack. 49 |
  2. Basic concepts: learning from data. 50 |
  3. Automated differentiation & Backpropagation, Training a Neural Network from Scratch. 51 |
  4. Tensorflow programming model. Dense Neural Networks. 52 |
  5. Keras. 53 |
  6. Recurrent Neural Netwoks I. 54 |
  7. Recurrent Neural Netwoks II. 55 |
  8. Embeddings. 56 |
  9. Convolutional Neural Networks I. 57 |
  10. Convolutional Neural Networks for Large Scale Learning. 58 |
  11. Unsupervised Learning I. 59 |
  12. Unsupervised Learning II. 60 |
  13. Deep Reinforcement Learning. 61 |
62 | 63 | ## Course Software Installation: Working in Colab 64 | 65 | You can develop deep learning applications with Google Colaboratory (Colab) -on the free Tesla K80 GPU- using Keras and Tensorflow. Colab is a Google internal research tool for data science. They have released the tool sometime earlier to the general public with a goal of dissemination of machine learning education and research. This is a free service that may not always be available, and requires extra steps to ensure your work is saved. Be sure to read the docs on the Colab web site to ensure you understand the limitations of the system. 66 | 67 | For accessing Colab, first of all you should sign in to you Google account if you are not signed in by default. You must do this step before opening Colab, otherwise the notebooks will not work. 68 | 69 | Next, head on to the Colab Welcome Page (https://colab.research.google.com) and click on ‘Github’. In the ‘Enter a GitHub URL or search by organization or user’ line enter ‘https://github.com/DataScienceUB/DeepLearningMaster2019’. You will see all the courses notebooks listed there. Click on the one you are interested in using. 70 | 71 | You should see your notebook displayed now. Before running anything, you need to tell Colab that you are interested in using a GPU. You can do this by clicking on the ‘Runtime’ tab and selecting ‘Change runtime type’. A pop-up window will open up with a drop-down menu. Select ‘GPU’ from the menu and click ‘Save’. 72 | 73 | When you run the first cell, you will face a pop-up saying ‘Warning: This notebook was not authored by Google’; you should leave the default tick in the ‘Reset all runtimes before running’ check box and click on ‘Run Anyway’. 74 | 75 | If you opened a notebook from Github, you will need to save your work to Google Drive. You can do this by clicking on ‘File’ and then ‘Save’. 76 | 77 | Click on ‘SAVE A COPY IN DRIVE’. This will open up a new tab with the same file, only this time located in your Drive. If you want to continue working after saving, use the file in the new tab. Your notebook will be saved in a folder called Colab Notebooks in your Google Drive by default. 78 | 79 | If you run a script which creates/ downloads files, the files will NOT persist after the allocated instance is shutdown. To save files, you need to permit your Colaboratory instance to read and write files to your Google Drive. Add the following code snippet at the beginning of every notebook. 80 | 81 | ```python 82 | from google.colab import drive 83 | drive.mount('/content/gdrive', force_remount=True) 84 | root_dir = "/content/gdrive/My Drive/" 85 | base_dir = root_dir + 'masterUB/' 86 | ``` 87 | 88 | Now, you may access your Google Drive as a file sytem using standard python commands to both read and write files. 89 | 90 | You can find more information in these blogs: 91 | + https://medium.com/deep-learning-turkey/google-colab-free-gpu-tutorial-e113627b9f5d 92 | + https://medium.com/tensorflow/colab-an-easy-way-to-learn-and-use-tensorflow-d74d1686e309 93 | 94 | ## Course Software Installation: Working with Docker 95 | 96 | You can run the course software using a **Docker container**. 97 | 98 | > A gentle introduction to docker: [How Docker Can Help You Become A More Effective Data Scientist](https://towardsdatascience.com/how-docker-can-help-you-become-a-more-effective-data-scientist-7fc048ef91d5) 99 | 100 | There’s full documentation on installing Docker at ``docker.com``, but in a few words, the steps are: 101 | 102 | + Go to ``docs.docker.com`` in your browser. 103 | + Step one of the instructions sends you to download Docker. 104 | + Run that downloaded file to install Docker. 105 | + At the end of the install process a whale in the top status bar indicates that Docker is running, and accessible from a terminal. 106 | + Click the whale to get ``Preferences``, and other options. 107 | + Open a command-line terminal, and run some Docker commands to verify that Docker is working as expected. 108 | Some good commands to try are ``docker version`` to check that you have the latest release installed, and ``docker ps`` and ``docker run hello-world`` to verify that Docker is running. 109 | + By default, Docker is set to use 2 processors. You can increase processing power for the app by setting this to a higher number in ``Preferences``, or lower it to use fewer computing resources. 110 | + Memory - By default, Docker is set to use 2 GB runtime memory, allocated from the total available memory on your computer. You can increase the RAM on the app to get faster performance by setting this number higher (for example to 3) or lower (to 1) if you want Docker to use less memory. 111 | 112 | Once Docker is installed, you can download the **image of this course** and download this git repository: 113 | 114 | + In a terminal, go to your course folder and run (This operation requires a good internet connection; it will take some minutes): ``docker pull datascienceub/deepub`` 115 | + MacOS & Linux: Run the ``deepub`` image on your system: ``docker run -it -p 8888:8888 -p 6006:6006 -v /$(pwd):/notebooks datascienceub/deepub`` 116 | + Windows: Run the ``deepub`` image on your system: ``docker run -it -p 8888:8888 -p 6006:6006 -v C:/your_course_folder_path:/notebooks datascienceub/deepub`` 117 | + Once these steps have been done, you can check the installation by starting your web browser and introducing the referred URL. 118 | + Finally, to have the contents of this repository in your computer, open terminal from your browser and execute this instruction: ``git clone https://github.com/DataScienceUB/DeepLearningMaster2019``. 119 | 120 | To run this image: 121 | 122 | + Windows: In a terminal, go to your course folder and run the ``deepub`` image on your system: ``docker run -it -p 8888:8888 -p 6006:6006 -v C:/your_course_folder_path:/notebooks datascienceub/deepub``. 123 | + MacOS & Linux: In a terminal, go to your course folder and run the ``deepub`` image on your system: ``docker run -it -p 8888:8888 -p 6006:6006 -v /$(pwd):/notebooks datascienceub/deepub`` 124 | + Start your web browser and introduce the corresponding URL. 125 | 126 | Next times, if there are new contents in the repository, you can bring your local copy of the repository up to date: 127 | 128 | + Open a new Jupyter notebook and execute this instruction in a code cell: 129 | ``!git pull https://github.com/DataScienceUB/DeepLearningMaster2019`` 130 | 131 | -------------------------------------------------------------------------------- /data/Advertising.csv: -------------------------------------------------------------------------------- 1 | "","TV","Radio","Newspaper","Sales" 2 | "1",230.1,37.8,69.2,22.1 3 | "2",44.5,39.3,45.1,10.4 4 | "3",17.2,45.9,69.3,9.3 5 | "4",151.5,41.3,58.5,18.5 6 | "5",180.8,10.8,58.4,12.9 7 | "6",8.7,48.9,75,7.2 8 | "7",57.5,32.8,23.5,11.8 9 | "8",120.2,19.6,11.6,13.2 10 | "9",8.6,2.1,1,4.8 11 | "10",199.8,2.6,21.2,10.6 12 | "11",66.1,5.8,24.2,8.6 13 | "12",214.7,24,4,17.4 14 | "13",23.8,35.1,65.9,9.2 15 | "14",97.5,7.6,7.2,9.7 16 | "15",204.1,32.9,46,19 17 | "16",195.4,47.7,52.9,22.4 18 | "17",67.8,36.6,114,12.5 19 | "18",281.4,39.6,55.8,24.4 20 | "19",69.2,20.5,18.3,11.3 21 | "20",147.3,23.9,19.1,14.6 22 | "21",218.4,27.7,53.4,18 23 | "22",237.4,5.1,23.5,12.5 24 | "23",13.2,15.9,49.6,5.6 25 | "24",228.3,16.9,26.2,15.5 26 | "25",62.3,12.6,18.3,9.7 27 | "26",262.9,3.5,19.5,12 28 | "27",142.9,29.3,12.6,15 29 | "28",240.1,16.7,22.9,15.9 30 | "29",248.8,27.1,22.9,18.9 31 | "30",70.6,16,40.8,10.5 32 | "31",292.9,28.3,43.2,21.4 33 | "32",112.9,17.4,38.6,11.9 34 | "33",97.2,1.5,30,9.6 35 | "34",265.6,20,0.3,17.4 36 | "35",95.7,1.4,7.4,9.5 37 | "36",290.7,4.1,8.5,12.8 38 | "37",266.9,43.8,5,25.4 39 | "38",74.7,49.4,45.7,14.7 40 | "39",43.1,26.7,35.1,10.1 41 | "40",228,37.7,32,21.5 42 | "41",202.5,22.3,31.6,16.6 43 | "42",177,33.4,38.7,17.1 44 | "43",293.6,27.7,1.8,20.7 45 | "44",206.9,8.4,26.4,12.9 46 | "45",25.1,25.7,43.3,8.5 47 | "46",175.1,22.5,31.5,14.9 48 | "47",89.7,9.9,35.7,10.6 49 | "48",239.9,41.5,18.5,23.2 50 | "49",227.2,15.8,49.9,14.8 51 | "50",66.9,11.7,36.8,9.7 52 | "51",199.8,3.1,34.6,11.4 53 | "52",100.4,9.6,3.6,10.7 54 | "53",216.4,41.7,39.6,22.6 55 | "54",182.6,46.2,58.7,21.2 56 | "55",262.7,28.8,15.9,20.2 57 | "56",198.9,49.4,60,23.7 58 | "57",7.3,28.1,41.4,5.5 59 | "58",136.2,19.2,16.6,13.2 60 | "59",210.8,49.6,37.7,23.8 61 | "60",210.7,29.5,9.3,18.4 62 | "61",53.5,2,21.4,8.1 63 | "62",261.3,42.7,54.7,24.2 64 | "63",239.3,15.5,27.3,15.7 65 | "64",102.7,29.6,8.4,14 66 | "65",131.1,42.8,28.9,18 67 | "66",69,9.3,0.9,9.3 68 | "67",31.5,24.6,2.2,9.5 69 | "68",139.3,14.5,10.2,13.4 70 | "69",237.4,27.5,11,18.9 71 | "70",216.8,43.9,27.2,22.3 72 | "71",199.1,30.6,38.7,18.3 73 | "72",109.8,14.3,31.7,12.4 74 | "73",26.8,33,19.3,8.8 75 | "74",129.4,5.7,31.3,11 76 | "75",213.4,24.6,13.1,17 77 | "76",16.9,43.7,89.4,8.7 78 | "77",27.5,1.6,20.7,6.9 79 | "78",120.5,28.5,14.2,14.2 80 | "79",5.4,29.9,9.4,5.3 81 | "80",116,7.7,23.1,11 82 | "81",76.4,26.7,22.3,11.8 83 | "82",239.8,4.1,36.9,12.3 84 | "83",75.3,20.3,32.5,11.3 85 | "84",68.4,44.5,35.6,13.6 86 | "85",213.5,43,33.8,21.7 87 | "86",193.2,18.4,65.7,15.2 88 | "87",76.3,27.5,16,12 89 | "88",110.7,40.6,63.2,16 90 | "89",88.3,25.5,73.4,12.9 91 | "90",109.8,47.8,51.4,16.7 92 | "91",134.3,4.9,9.3,11.2 93 | "92",28.6,1.5,33,7.3 94 | "93",217.7,33.5,59,19.4 95 | "94",250.9,36.5,72.3,22.2 96 | "95",107.4,14,10.9,11.5 97 | "96",163.3,31.6,52.9,16.9 98 | "97",197.6,3.5,5.9,11.7 99 | "98",184.9,21,22,15.5 100 | "99",289.7,42.3,51.2,25.4 101 | "100",135.2,41.7,45.9,17.2 102 | "101",222.4,4.3,49.8,11.7 103 | "102",296.4,36.3,100.9,23.8 104 | "103",280.2,10.1,21.4,14.8 105 | "104",187.9,17.2,17.9,14.7 106 | "105",238.2,34.3,5.3,20.7 107 | "106",137.9,46.4,59,19.2 108 | "107",25,11,29.7,7.2 109 | "108",90.4,0.3,23.2,8.7 110 | "109",13.1,0.4,25.6,5.3 111 | "110",255.4,26.9,5.5,19.8 112 | "111",225.8,8.2,56.5,13.4 113 | "112",241.7,38,23.2,21.8 114 | "113",175.7,15.4,2.4,14.1 115 | "114",209.6,20.6,10.7,15.9 116 | "115",78.2,46.8,34.5,14.6 117 | "116",75.1,35,52.7,12.6 118 | "117",139.2,14.3,25.6,12.2 119 | "118",76.4,0.8,14.8,9.4 120 | "119",125.7,36.9,79.2,15.9 121 | "120",19.4,16,22.3,6.6 122 | "121",141.3,26.8,46.2,15.5 123 | "122",18.8,21.7,50.4,7 124 | "123",224,2.4,15.6,11.6 125 | "124",123.1,34.6,12.4,15.2 126 | "125",229.5,32.3,74.2,19.7 127 | "126",87.2,11.8,25.9,10.6 128 | "127",7.8,38.9,50.6,6.6 129 | "128",80.2,0,9.2,8.8 130 | "129",220.3,49,3.2,24.7 131 | "130",59.6,12,43.1,9.7 132 | "131",0.7,39.6,8.7,1.6 133 | "132",265.2,2.9,43,12.7 134 | "133",8.4,27.2,2.1,5.7 135 | "134",219.8,33.5,45.1,19.6 136 | "135",36.9,38.6,65.6,10.8 137 | "136",48.3,47,8.5,11.6 138 | "137",25.6,39,9.3,9.5 139 | "138",273.7,28.9,59.7,20.8 140 | "139",43,25.9,20.5,9.6 141 | "140",184.9,43.9,1.7,20.7 142 | "141",73.4,17,12.9,10.9 143 | "142",193.7,35.4,75.6,19.2 144 | "143",220.5,33.2,37.9,20.1 145 | "144",104.6,5.7,34.4,10.4 146 | "145",96.2,14.8,38.9,11.4 147 | "146",140.3,1.9,9,10.3 148 | "147",240.1,7.3,8.7,13.2 149 | "148",243.2,49,44.3,25.4 150 | "149",38,40.3,11.9,10.9 151 | "150",44.7,25.8,20.6,10.1 152 | "151",280.7,13.9,37,16.1 153 | "152",121,8.4,48.7,11.6 154 | "153",197.6,23.3,14.2,16.6 155 | "154",171.3,39.7,37.7,19 156 | "155",187.8,21.1,9.5,15.6 157 | "156",4.1,11.6,5.7,3.2 158 | "157",93.9,43.5,50.5,15.3 159 | "158",149.8,1.3,24.3,10.1 160 | "159",11.7,36.9,45.2,7.3 161 | "160",131.7,18.4,34.6,12.9 162 | "161",172.5,18.1,30.7,14.4 163 | "162",85.7,35.8,49.3,13.3 164 | "163",188.4,18.1,25.6,14.9 165 | "164",163.5,36.8,7.4,18 166 | "165",117.2,14.7,5.4,11.9 167 | "166",234.5,3.4,84.8,11.9 168 | "167",17.9,37.6,21.6,8 169 | "168",206.8,5.2,19.4,12.2 170 | "169",215.4,23.6,57.6,17.1 171 | "170",284.3,10.6,6.4,15 172 | "171",50,11.6,18.4,8.4 173 | "172",164.5,20.9,47.4,14.5 174 | "173",19.6,20.1,17,7.6 175 | "174",168.4,7.1,12.8,11.7 176 | "175",222.4,3.4,13.1,11.5 177 | "176",276.9,48.9,41.8,27 178 | "177",248.4,30.2,20.3,20.2 179 | "178",170.2,7.8,35.2,11.7 180 | "179",276.7,2.3,23.7,11.8 181 | "180",165.6,10,17.6,12.6 182 | "181",156.6,2.6,8.3,10.5 183 | "182",218.5,5.4,27.4,12.2 184 | "183",56.2,5.7,29.7,8.7 185 | "184",287.6,43,71.8,26.2 186 | "185",253.8,21.3,30,17.6 187 | "186",205,45.1,19.6,22.6 188 | "187",139.5,2.1,26.6,10.3 189 | "188",191.1,28.7,18.2,17.3 190 | "189",286,13.9,3.7,15.9 191 | "190",18.7,12.1,23.4,6.7 192 | "191",39.5,41.1,5.8,10.8 193 | "192",75.5,10.8,6,9.9 194 | "193",17.2,4.1,31.6,5.9 195 | "194",166.8,42,3.6,19.6 196 | "195",149.7,35.6,6,17.3 197 | "196",38.2,3.7,13.8,7.6 198 | "197",94.2,4.9,8.1,9.7 199 | "198",177,9.3,6.4,12.8 200 | "199",283.6,42,66.2,25.5 201 | "200",232.1,8.6,8.7,13.4 202 | -------------------------------------------------------------------------------- /data/cat.txt: -------------------------------------------------------------------------------- 1 | Wow! Carai! 2 | Really? De veritat? 3 | Thanks. Gràcies! 4 | Goodbye! Adéu! 5 | Hurry up. Afanya't. 6 | Too late. Massa tard. 7 | Thank you. Gràcies! 8 | Can I help? Puc ajudar? 9 | I envy him. L'envejo. 10 | Time flies. El temps vola. 11 | I'm 17, too. Jo també tinc 17 anys. 12 | I'm at home. Estic a casa. 13 | Make a wish. Demana un desig 14 | Money talks. Qui paga, mana. 15 | We love you. T'estimem. 16 | We love you. Us estimem. 17 | Who are you? Qui ets tu? 18 | Who are you? Qui és vostè? 19 | Who are you? Qui ets? 20 | Who are you? Qui sou? 21 | He has a dog. Ell té un gos. 22 | She stood up. Ella es va aixecar. 23 | Hi, everybody. Hola a tots. 24 | I'm desperate. Estic desesperat. 25 | Let me try it. Deixa'm intentar-ho. 26 | You look good. Tens bona cara. 27 | You look good. Fas bona cara. 28 | You look good. Fas bon aspecte. 29 | Are you insane? Estàs boig? 30 | Can I help you? Puc ajudar? 31 | Happy New Year! Bon any nou! 32 | I need a stamp. Necessito un segell. 33 | I saw him jump. El vaig veure saltar. 34 | Leave me alone! Deixa'm en pau! 35 | Who painted it? Qui ho ha pintat? 36 | Her book is red. El seu llibre és roig. 37 | I didn't say it. No ho he dit pas. 38 | I felt the same. Em sentia igual. 39 | I have two cats. Tinc dos gats. 40 | I speak Swedish. Parlo suec. 41 | It's cold today! Avui fa fred! 42 | It's your fault. És culpa teva. 43 | Who are you all? Qui sou tots vosaltres? 44 | Who are you all? Qui sou totes vosaltres? 45 | Here is your bag. Aquí és la teva bossa. 46 | Here is your bag. Ací està la teua bossa. 47 | Here is your bag. Ací tens la teua bossa. 48 | I am now on duty. Ara estic de servei. 49 | I ate the cheese. Em vaig menjar el formatge. 50 | I have a problem. Tinc un problema. 51 | I have a problem. Tinc un maldecap. 52 | I have no family. No tinc família. 53 | I work in a bank. Jo treballo a un banc. 54 | I wrote a letter. Vaig escriure una carta. 55 | I'm already late. Ja faig tard. 56 | I'm not a doctor. Jo no sóc metge. 57 | Let go of my arm. Deixa'm anar el braç. 58 | She lives nearby. Viu aquí prop. 59 | They're my books. Són els meus llibres. 60 | This is your dog. Aquest és el teu gos. 61 | Tom isn't hungry. Tom no té fam. 62 | Tom walked alone. En Tom caminava sol. 63 | What is going on? Què hi ha? 64 | What is going on? Què passa? 65 | Who are you with? Amb qui estàs? 66 | Who are you with? Amb qui esteu? 67 | Answer in English. Contesta en anglès! 68 | He went back home. Ell va tornar a casa. 69 | I have an earache. Tinc otitis. 70 | I have black eyes. Tinc els ulls negres. 71 | I think he did it. Crec que ho va fer ell. 72 | I'm a salesperson. Sóc venedor. 73 | Let me have a try. Deixa'm intentar-ho. 74 | Nobody is perfect. Ningú és perfecte. 75 | She has long hair. Ella té el cabell llarg. 76 | Tom doesn't drink. Tom no beu. 77 | Why is snow white? Per què és blanca la neu? 78 | You need to hurry. T'has d'afanyar. 79 | Are you hungry now? Tens fam, ara? 80 | Are you hungry now? Teniu fam, ara? 81 | Are you hungry now? Ara tens fam? 82 | Come along with me. Acompanya'm. 83 | Do it by all means. Fes-ho com sigui. 84 | Do it by all means. Fes-ho peti qui peti. 85 | Everyone loves him. Tots l'estimen. 86 | He speaks fluently. Parla amb soltura. 87 | I don't have a cat. No tinc cap gat. 88 | I don't want sugar. No vull sucre. 89 | I have lost my key. He perdut la meva clau. 90 | It was a nightmare. Va ser un malson. 91 | Shut up and listen! Calla i escolta! 92 | Shut up and listen. Calla i escolta! 93 | Stay out of my way. Fora del meu camí! 94 | This coat fits you. Aquest abric et queda bé. 95 | Tom is a silly man. En tom és un ximplet. 96 | What's the problem? Quin problema hi ha? 97 | What's your secret? Quin és el teu secret? 98 | You are a good boy. Ets un bon noi. 99 | Europe is in crisis. Europa està en crisi. 100 | Everybody loves him. Tots l'estimen. 101 | He'll return at six. Tornarà a les sis. 102 | He's the oldest son. Ell és el fill gran. 103 | I can see the light. Puc veure la llum. 104 | I want to stay here. Vull quedar-me aquí. 105 | I'll call him later. Li cridaré més tard. 106 | I've got a question. Tinc una pregunta. 107 | She has a white cat. Ella té un gat blanc. 108 | She raised her hand. Ella va aixecar la mà. 109 | She raised her hand. Ella va alçar la mà. 110 | She raised her hand. Ella alçà la mà. 111 | She raised her hand. Va alçar la mà. 112 | The bicycle is mine. La bicicleta és meva. 113 | They were satisfied. Estaven satisfets. 114 | They were satisfied. Estaven cofois. 115 | Where are you going? A on vas? 116 | Your father is tall. Ton pare és alt. 117 | Birds fly in the sky. Els ocells volen pel cel. 118 | Can he speak English? Que parla anglès, ell? 119 | Do you live in Tokyo? Vius a Tokyo? 120 | Fish live in the sea. Els peixos viuen al mar. 121 | He is a good athlete. Ell és un bon atleta. 122 | I dislike big cities. No m'agraden les ciutats grans. 123 | I don't want to work. No vull treballar. 124 | I have two daughters. Tinc dues filles. 125 | I'm glad you're here. M'alegra que estigues ací. 126 | I'm glad you're here. M'alegra que estigueu ací. 127 | It's a piece of cake. Això és bufar i fer ampolles. 128 | It's really annoying. És realment molest. 129 | She sent me a letter. Ella em va enviar una carta. 130 | Sorry for being late. Perdó pel retard. 131 | That's a bright idea. És una idea brillant. 132 | The ground seems wet. El sòl sembla mullat. 133 | Tom always says that. Tom sempre diu això. 134 | Tom doesn't watch TV. En Tom no mira la tele. 135 | Who's coming with me? Qui ve amb mi? 136 | You can come with me. Pots venir amb mi. 137 | Your son is a genius. El vostre fill és un geni. 138 | Do you have two books? Tens dos llibres? 139 | Does he speak English? Que parla anglès, ell? 140 | Does he speak English? Parla anglès? 141 | Does he speak English? Ell parla anglès? 142 | His house was on fire. La seva casa està en flames. 143 | I don't have time now. Ara no tinc temps. 144 | I have to go to sleep. He d'anar a dormir. 145 | I know these students. conec aquests estudiants. 146 | I'd like some aspirin. Voldria una aspirina. 147 | Money opens all doors. Els diners obren totes les portes. 148 | She is a good swimmer. És una bona nedadora. 149 | Thanks for everything. Mercès per tot. 150 | The girl said nothing. La nena no va dir res. 151 | Those are empty words. Són paraules buides. 152 | We have two daughters. Tenim dues filles. 153 | What's wrong with you? Què et passa? 154 | You should eat slower. Has de menjar més a poc a poc. 155 | You're taller than me. Ets més alt que jo. 156 | Come whenever you like. Vingui quan vulgui. 157 | Come whenever you like. Veniu quan vulgueu. 158 | Come whenever you like. Vine quan vulguis. 159 | Does the bus stop here? L'autobús para ací? 160 | I didn't buy this book. No vaig comprar aquest llibre. 161 | I think he has done it. Crec que ell ho ha fet. 162 | Mary married for money. La Mary es va casar per diners. 163 | They will not eat meat. No menjaran carn. 164 | Tom wants to be famous. Tom vol ser famós. 165 | We have to act quickly. Hem d'actuar ràpid. 166 | What is wrong with him? Què li passa? 167 | Ask me something easier. Pregunta'm una cosa més fàcil. 168 | Do you have a cellphone? Tens un mòbil? 169 | He died three years ago. Va morir fa tres anys. 170 | I don't like big cities. No m'agraden les ciutats grans. 171 | I felt like I would die. Sentia que em moriria. 172 | I hope that I can do it. Espere poder-ho fer. 173 | I listened to her story. Vaig escoltar la història d'ella. 174 | I won't see him anymore. No el veuré mai més. 175 | I'll start this evening. Començaré aquest vespre. 176 | I'll start this evening. Començaré esta vesprada. 177 | I'm sure of his success. Estic segur del seu èxit. 178 | I've forgotten his name. He oblidat el seu nom. 179 | Many sailors can't swim. Molts mariners no saben nedar, 180 | My father quit drinking. El meu pare va aturar de beure. 181 | My father quit drinking. Mon pare va deixar de beure. 182 | She helped me willingly. Ella em va ajudar de bon gust. 183 | Tell me when he returns. Avisa'm quan torni. 184 | Tom is thirty years old. Tom té trenta anys. 185 | What a beautiful sunset! Quina posta de sol més bonica! 186 | What a beautiful sunset. Quina posta més maca! 187 | What a beautiful sunset. Quina posta de sol més bonica. 188 | Where are our umbrellas? On estan els nostres paraigües? 189 | Would you draw me a map? Em faríes un mapa? 190 | You will miss the train. Perdràs el tren. 191 | Can your mom drive a car? La teva mama sap conduir? 192 | Can your mom drive a car? Ta mare sap conduir? 193 | Can your mom drive a car? La teua mare sap conduir un cotxe? 194 | Can your mom drive a car? La vostra mare sap conduir? 195 | Do you have a cell phone? Tens un mòbil? 196 | Do you have a smartphone? Tens un mòbil? 197 | Don't ever do that again. No ho facis mai més. 198 | Fire is always dangerous. El foc sempre és perillós. 199 | He fell and hurt his leg. Ell es va caure i es va fer mal a la cama. 200 | He has never played golf. Ell no ha jugat mai al golf. 201 | He looks like his father. Ell s'assembla al seu pare. 202 | He speaks five languages. Ell parla cinc idiomes. 203 | I can't stand that noise. No puc aguantar aquest soroll. 204 | I didn't know what to do. No sabia què fer. 205 | I don't know her address. No sé la seva adreça. 206 | I made my son a new suit. He fet un vestit nou per a mon fill. 207 | I only have eyes for you. Aquí no veig ningú més que tu. 208 | I recognized him at once. El vaig reconèixer de seguida. 209 | I'm talking on the phone. Parlo per telèfon. 210 | I'm talking on the phone. Estic parlant per telèfon. 211 | It doesn't sound natural. No sona natural. 212 | It is nice and cool here. Aquí fa fresca i s'hi està bé. 213 | My mother is always busy. La meva mare sempre està ocupada. 214 | She had a strange hat on. Ella portava un barret estrany. 215 | This is his car, I think. Aquest és el seu cotxe, crec. 216 | Tom is a history teacher. En Tom és un professor d'història. 217 | Tom visited Mary's grave. En Tom va visitar la tomba de la Mary. 218 | Turn down the TV, please. Baixa el volum del televisor. 219 | Turn the TV down, please. Baixa el volum del televisor. 220 | You are everything to me. Tu ets tot per mi. 221 | You don't have to eat it. Vostè no ha de menjar-ho. 222 | I like to listen to music. M'agrada escoltar música. 223 | I made a careless mistake. Vaig cometre una negligència. 224 | I'm busy, so I can't help. Estic ocupat, no puc ajudar-te. 225 | I'm busy, so I can't help. Estic ocupat, no puc ajudar-vos. 226 | I'm standing in the shade. M'estic dret a l'ombra. 227 | It's always been that way. Sempre ha sigut així. 228 | That'll put you in danger. Això et posarà en perill. 229 | We may be late for school. Potser farem tard a l'escola. 230 | What a nice sounding word! Què bé sona aquesta paraula! 231 | Where will you be staying? On t'estaràs? 232 | Where will you be staying? On t'allotjaràs? 233 | Where will you be staying? On et quedaràs? 234 | Where will you be staying? On us allotjareu? 235 | You agree with Tom, right? Estàs d'acord amb Tom, oi? 236 | You agree with Tom, right? Esteu d'acord amb Tom, veritat? 237 | You don't have to do this. No has de fer-ho. 238 | You don't have to do this. Això no ho has de fer. 239 | Do they have any good news? Tens bones notícies? 240 | Do you come here every day? Véns aquí cada dia? 241 | Do you come here every day? Veniu ací tots els dies? 242 | Do you have a mobile phone? Tens un mòbil? 243 | Do you know his birthplace? Saps on va néixer? 244 | I have to buy one tomorrow. He de comprar-ne un demà. 245 | I just want to be near you. Només vull estar prop de tu. 246 | I know he likes jazz music. Sé que li agrada el jazz. 247 | I'd rather do it by myself. Prefereixo fer-lo pel meu compte. 248 | I'm afraid I caught a cold. Em sembla que he agafat un constipat. 249 | It's all you can really do. És tot el que pots fer. 250 | It's always been like that. Sempre ha sigut així. 251 | She's Tom's younger sister. És la germana petita d'en Tom. 252 | She's Tom's younger sister. És la germana menuda de Tom. 253 | The bird's wing was broken. L'ala de l'ocell estava trencada. 254 | The bird's wing was broken. L'ocell tenia una ala trencada. 255 | The bird's wing was broken. El pardal tenia una ala trencada. 256 | There were ten eggs in all. Hi havia deu ous en total. 257 | There's no reason to worry. No hi ha cap motiu per preocupar-se. 258 | Things are not that simple. Les coses no són tan senzilles. 259 | This store sells old books. Aquesta botiga ven llibres vells. 260 | You're not a child anymore. Ja no ets un nen. 261 | Columbus discovered America. Colón va descobrir Amèrica. 262 | Don't you like Chinese food? No t'agrada el menjar xinès? 263 | France is in western Europe. França és a l'Europa Occidental. 264 | He plays baseball every day. Juga al beisbol tots el dies. 265 | He wants a watch like yours. Vol un rellotge com el teu. 266 | He's the one who touched me. Ell és el que em va tocar. 267 | I don't know if he knows it. No sé si ho sap. 268 | I'll be back in ten minutes. Tornaré en deu minuts. 269 | I'm the one who has the key. Jo sóc qui té la clau. 270 | Take off your socks, please. Sisplau, lleva't els mitjons. 271 | Take off your socks, please. Lleva't els calcetins, per favor. 272 | Take off your socks, please. Lleveu-vos els calcetins, per favor. 273 | The evidence was against me. L'evidència estava en contra meua. 274 | The food was great in Italy. El menjar va ser cosa fina a Itàlia. 275 | They work eight hours a day. Treballen vuit hores al dia. 276 | What are you doing tomorrow? Què fas demà? 277 | What is wrong with that guy? Què li passa a aquet paio? 278 | Where will we go afterwards? On anirem després? 279 | Do you know where she's gone? Saps on ha anat ella? 280 | He goes to the office by car. Va al despatx amb cotxe. 281 | He is the manager of a hotel. És el director d'un hotel. 282 | He lost all the money he had. Va perdre tots els diners que tenia. 283 | He plays the piano very well. Ell toca el piano molt bé. 284 | I don't want to go to school. No vull anar a l'escola. 285 | I have something to tell you. T'he de dir una cosa. 286 | I have something to tell you. Us he de dir una cosa. 287 | I have something to tell you. Tinc una cosa a dir-te. 288 | I must have the wrong number. Dec tenir el número equivocat. 289 | I never get tired of talking. No em canso mai de parlar. 290 | I saw him tear up the letter. El vaig veure estripar la carta. 291 | I will get in touch with you. Em posaré en contacte amb tu. 292 | Japan is smaller than Canada. El Japó és més petit que el Canadà. 293 | She sent you her best wishes. Ella t'envia els seus millors desitjos. 294 | That's exactly what happened. Això és exactament el què va passar. 295 | The girl didn't say anything. La nena no va dir res. 296 | The soldier gave water to me. El soldat m'ha donat aigua. 297 | We killed time playing cards. Matàvem el temps jugant a les cartes. 298 | We must control our passions. Hem de controlar les nostres passions. 299 | What you think is irrelevant. El que penses és irellevant. 300 | Do you have medical insurance? Teniu assegurança mèdica? 301 | He comes here every five days. Ve aquí cada cinc dies. 302 | He left the book on the table. Va deixar el llibre sobre la taula. 303 | How many children do you have? Quants fills tens? 304 | I believe the choice is clear. Crec que l'elecció està clara. 305 | I study for 3 hours every day. Jo estudio 3 hores cada dia. 306 | It was cheaper than I thought. És més barat del que em vaig pensar. 307 | Let me know whenever you come. Quan vinguis, fes-m'ho saber. 308 | Most schools are closed today. La majoria d'escoles avui estan tancades. 309 | My dad died before I was born. Mon pare va morir abans del meu naixement. 310 | Nobody equals him in strength. Ningú no li fa ombra. 311 | Nobody equals him in strength. Ningú no li és rival. 312 | Our summer is short, but warm. El nostre estiu és curt, però calorós. 313 | She didn't tell me her secret. Ella no em va dir el seu secret. 314 | She is giving a party tonight. Ella fa una festa aquesta nit. 315 | This is a very strange letter. Aquesta és una carta molt estranya. 316 | Tom wants to change the world. En Tom vol canviar el món. 317 | Tom's arm had to be amputated. Van haver d'amputar el braç al Tom. 318 | Tom's arm had to be amputated. Li van haver d'amputar el braç a Tom. 319 | You agree with Tom, don't you? Estàs d'acord amb Tom, no? 320 | He is a very thoughtful person. És una persona molt considerada. 321 | I don't know when he will come. No sé quan vindrà. 322 | I don't like it when you swear. No m'agrada que digues paraulotes. 323 | I don't like it when you swear. No m'agrada que digueu paraulotes. 324 | I have breakfast every morning. Cada dia esmorzo. 325 | I have not seen him since then. No l'he vist des d'aleshores. 326 | I opened the box. It was empty. Vaig obrir la caixa. Estava buida. 327 | I wish I could buy that guitar. Com voldria poder comprar aquesta guitarra. 328 | I wish I could buy that guitar. M'agradaria poder comprar eixa guitarra. 329 | I wish I could buy that guitar. Tant de bo pogués comprar aquesta guitarra. 330 | I wish I could buy that guitar. Tant de bo poguera comprar eixa guitarra. 331 | I'm very glad to see you again. Estic molt content de tornar-te a veure. 332 | Please circle the right answer. Encercleu la resposta correcta, sisplau. 333 | He had a firm belief in his God. Té una creença ferma en Déu. 334 | He is getting better bit by bit. Ell s'està millorant poc a poc 335 | He is getting better bit by bit. Està millorant poc a poc. 336 | He told me an interesting story. M'ha contat una història interessant. 337 | Helen Keller was deaf and blind. Hellen Keller era sorda i cega. 338 | How much does he earn per month? Quant guanya al mes? 339 | I can repeat it again and again. Puc repetir-ho vint vegades. 340 | I caught the ball with one hand. Vaig agafar la pilota amb una mà. 341 | I heard him sing at the concert. El vaig sentir cantant al concert. 342 | I was not aware of his presence. Jo no era conscient que ell estava al davant. 343 | I wonder if he'll come tomorrow. Em pregunto si vindrà demà. 344 | I'm a professional photographer. Jo sóc fotògraf professional. 345 | Let me know when he will arrive. Ja em diràs quan arriba. 346 | My mother speaks little English. La meva mare parla una mica d'anglès. 347 | She made the same mistake again. Ella va cometre una altra vegada la mateixa errada. 348 | She will have a baby next month. Ella vol tenir un fill el mes vinent. 349 | Thanks a lot for the invitation. Moltes gràcies per la invitació. 350 | The food didn't taste very good. El menjar no tenia gaire bon gust. 351 | The food didn't taste very good. El menjar no feia gaire bon gust. 352 | The sun appeared on the horizon. El Sol apareix a l'horitzont. 353 | The sun appeared on the horizon. El sol aparegué a l'horitzó. 354 | The sun gives us heat and light. El Sol ens dóna calor i llum. 355 | The sun is larger than the moon. El Sol és més gran que la Lluna. 356 | Tom is too young to drive a car. Tom és massa jove per portar un cotxe. 357 | Tom was fired for a good reason. Tom va ser despedit per una causa justa. 358 | You don't have to kick yourself. No et facis mala sang. 359 | You should've told me yesterday. M'ho hauries d'haver dit ahir. 360 | Your opinion is important to me. La teua opinió és important per a mi. 361 | Asians generally have black hair. Els asiàtics normalment tenen el cabell negre, 362 | Do you know who wrote this novel? Saps qui va escriure aquesta novela? 363 | Do you know who wrote this novel? Sabeu qui va escriure aquesta novel·la? 364 | Don't compare me to a movie star. No em comparis amb una estrella de cinema. 365 | He went skiing during the winter. Se'n va anar a esquiar a l'hivern. 366 | I have lived in Tokyo since 1985. He viscut a Tokyo des de 1985. 367 | I saw the moon above the horizon. Veig la lluna sobre l'horitzont. 368 | My brother-in-law is a policeman. El meu cunyat és policia. 369 | My father died before I was born. Mon pare va morir abans del meu naixement. 370 | My father died before I was born. Mon pare va morir abans que jo nasquera. 371 | My father died before I was born. El meu pare va morir abans de néixer jo. 372 | The bus arrived ten minutes late. El bus va arribar deu minuts tard. 373 | The bus arrived ten minutes late. L'autobús arribà deu minuts tard. 374 | The bus arrived ten minutes late. L'autobús va arribar amb deu minuts de retard. 375 | The flood caused a lot of damage. La riada va fer molt de mal. 376 | The flood caused a lot of damage. La inundació va fer molt de mal. 377 | The rumor is true to some extent. Fins a un cert punt, el rumor és cert. 378 | The teacher told me study harder. El professor em va dir que estudiés molt. 379 | Tom and Mary acted like children. En Tom i la Mary es portaven com nens. 380 | Tom couldn't hold back his tears. Tom no va poder contenir les llàgrimes. 381 | Tom couldn't hold back his tears. Tom no podia contenir les llàgrimes. 382 | Tom doesn't go to school anymore. Tom ja no va a l'escola. 383 | Tom is no longer studying French. En Tom ja no estudia francès. 384 | When can we see each other again? On ens podem tornar a veure? 385 | Are we talking about the same Tom? Estem parlant del mateix Tom? 386 | Are we talking about the same Tom? Parlem del mateix Tom? 387 | Everyone hoped that she would win. Tothom esperava que guanyés. 388 | He was willing to work for others. Ell estava disposat a treballar per altres. 389 | I burned my fingers on a hot iron. Em vaig cremar els dits amb un ferro roent. 390 | I burned my fingers on a hot iron. Em vaig cremar els dits amb una planxa calenta. 391 | I have nothing in common with her. No tinc res en comú amb ella. 392 | I spend money as soon as I get it. Em gasto els diners de seguida que en tinc. 393 | I write letters that I never send. Escric cartes que no envio mai. 394 | Is Flight 123 going to be delayed? El vol 123, té retard? 395 | Last night we worked until 10 p.m. Ahir a la nit vàrem treballar fins a les deu. 396 | My mother knows how to make cakes. La meva mare sap com fer pastissos. 397 | Tell me your plans for the future. Explica'm els teus plans per al futur. 398 | Tell me your plans for the future. Conta'm els teus plans de futur. 399 | Tell me your plans for the future. Expliqueu-me els vostres plans per al futur. 400 | Thank you so much for inviting me. Moltes gràcies per la invitació. 401 | The plane took off exactly at six. L'avió s'enlairà a les sis clavades. 402 | Today's meeting has been canceled. La reunió d'avui ha sigut cancelada. 403 | We're sorry for the inconvenience. Ens sap greu la molèstia causada. 404 | Where's the nearest travel agency? On és l'agència de viatges més propera? 405 | Bangkok is Thailand's capital city. Bangkok és la capital de Tailàndia. 406 | Do you want to play tennis with us? Vols jugar a tennis amb nosaltres? 407 | He helped poor people all his life. Ell va ajudar els pobres tota la seva vida. 408 | Her husband is now living in Tokyo. El seu marit viu a Tòkio ara. 409 | I can't remember where I bought it. No puc recordar on el vaig comprar. 410 | I can't remember where I bought it. No recorde on el vaig comprar. 411 | I can't remember where I bought it. No recorde on ho vaig comprar. 412 | I can't remember where I bought it. No recorde on la vaig comprar. 413 | I can't remember where I bought it. No me'n recorde d'on ho vaig comprar. 414 | I can't remember where I bought it. No recordo on el vaig comprar. 415 | I heard a beautiful song yesterday. Ahir vaig sentir una cançó bonica. 416 | I thanked him for what he had done. Li vaig agrair el que va fer. 417 | I'd like to meet your older sister. Voldria trobar-me amb la teva germana gran. 418 | I'd like to meet your older sister. M'agradaria conèixer la teva germana gran. 419 | I'm the one who pays all the bills. Jo sóc qui paga totes les factures. 420 | I'm very slow at making up my mind. Sóc molt lent a l'hora de prendre decisions. 421 | I, too, didn't understand anything. Jo tampoc entenc res. 422 | Is there a post office around here? Hi ha alguna oficina postal per aquí? 423 | Is there a post office around here? Hi ha alguna oficina de correus prop d'ací? 424 | Is there a post office around here? Hi ha per ací alguna oficina de correus? 425 | The door is locked at nine o'clock. La porta es tanca amb clau a les nou. 426 | The lion is the king of the jungle. El lleó és el rei de la selva. 427 | These questions are easy to answer. Aquestes preguntes són fàcils de respondre. 428 | We are sorry for the inconvenience. Ens sap greu la molèstia causada. 429 | We're not going to change anything. No canviarem res. 430 | What little money I had was stolen. Els pocs diners que tenia me'ls van robar. 431 | A lot of jobs are done by computers. Moltes feines les fan els ordinadors. 432 | Do you wonder why no one trusts him? T'estranya que ningú hi confiï? 433 | Don't go to sleep with the light on. No et durmis amb el llum encès. 434 | I can't remember which is my racket. No recorde quina és la meua raqueta. 435 | I don't think we can take that risk. Crec que no podem córrer aquest risc. 436 | I don't think we can take that risk. Crec que no podem córrer eixe risc. 437 | I have nothing to say to any of you. No tinc res a dir-vos a cap de vosaltres. 438 | I was caught in a shower on the way. M'ha enxampat un xàfec pel camí. 439 | I'd like to reserve a table for two. M'agradaria reservar una taula per a dos. 440 | Look that word up in the dictionary. Cerca aquella paraula al diccionari. 441 | My apartment is on the fourth floor. El meu apartament està al quart pis. 442 | Night is when most people go to bed. La nit és quan la majoria de la gent se'n va al llit. 443 | Take this medicine before each meal. Preneu aquest medicament abans de cada àpat. 444 | Tom may talk to Mary if he wants to. En Tom, si vol, pot parlar amb la Mary. 445 | Tom may talk to Mary if he wants to. Tom pot parlar amb Mary, si vol. 446 | When did you come back from Germany? Quan vas tornar d'Alemanya? 447 | Flowers die if they don't have water. Sense aigua les flors es panseixen. 448 | His arrogance is no longer tolerable. La seva arrogància ja no és tolerable. 449 | His courage is worthy of high praise. La seva valentia mereix grans lloances. 450 | I planted an apple tree in my garden. He plantat un pomer al meu jardí. 451 | I really must have my watch repaired. He de dur el rellotge a arreglar. 452 | I'm sick. Will you send for a doctor? Estic malalt. Oi que avisaràs un metge? 453 | I'm sure of winning the championship. Estic segur de guanyar el campionat. 454 | It seems that he was a great athlete. Sembla que va ser un gran atleta. 455 | It's easier to have fun than to work. És més fàcil divertir-se que treballar. 456 | Please write to me from time to time. Escriu-me de tant en tant, sí? 457 | What are you going to eat for dinner? Que soparàs avui? 458 | What do you want to talk to me about? De què vols parlar amb mi? 459 | What languages are spoken in America? Quins idiomes es parlen a Amèrica? 460 | What's your opinion of Japanese food? Quina és la teva opinió sobre el menjar japonès? 461 | Everyone was listening very carefully. Tots estaven escoltant atentament. 462 | He is three years younger than Father. Ell és tres anys més jove que el pare. 463 | I don't know what has happened to him. No sé què li ha passat. 464 | I was the one who knocked on the door. Vaig ser jo qui va trucar a la porta. 465 | I'll make an exception just this once. Faré una excepció només per aquesta vegada. 466 | I'm the one who takes out the garbage. Jo sóc qui treu les escombraries. 467 | In general, men are taller than women. En general, els homes són més alts que les dones. 468 | Japan imports a large quantity of oil. El Japó importa una gran quantitat de petroli. 469 | Mary's doctor advised her to exercise. El metge de la Mary li va aconsellar que fes exercici. 470 | Please correct me if I make a mistake. Si us plau, corregeix-me si m'equivoco. 471 | Will the work be finished by tomorrow? Estarà enllestida la feina per a demà? 472 | "Is she reading a book?" "Yes, she is." "Està llegint un llibre?" "Sí." 473 | "Is she reading a book?" "Yes, she is." "Està ella llegint un llibre?" "Sí." 474 | All my friends like playing videogames. A tots els meus amics els agraden els videojocs. 475 | As long as there's life, there is hope. Mentre hi ha vida, hi ha esperança. 476 | Blue lines on the map designate rivers. Les línies blaves al mapa designen rius. 477 | How much time do you spend on Facebook? Quant de temps passes a Facebook? 478 | I don't know whether it is true or not. No sé si és veritat o no. 479 | I don't think Tom was talking about me. No crec que Tom estigués parlant de mi. 480 | I have cookies for breakfast every day. Cada dia menjo galetes per esmorzar. 481 | I would like to visit New York someday. Un dia m'agradaria visitar New York. 482 | I've been waiting for this day to come. He estat esperant que arribi aquest dia. 483 | In Japan there are four seasons a year. Al Japó hi ha quatre estacions cada any. 484 | Mathematics is important in daily life. Les matemàtiques són importants a la vida diària. 485 | The Japanese economy developed rapidly. L'economia japonesa es va desenvolupar depressa. 486 | The class was divided into four groups. La classe es va dividir en quatre grups. 487 | The earth is much larger than the moon. La Terra és molt més gran que la Lluna. 488 | They arrived late because of the storm. Ells van arribar tard a causa de la tempesta. 489 | They say golf is very popular in Japan. Diuen que el golf és molt popular al Japó. 490 | This is the best book I have ever read. És el millor llibre que he llegit mai. 491 | Tom is interested in French literature. En Tom està interessat en la literatura francesa. 492 | Tom is making great progress in French. En Tom està progressant molt amb el francès. 493 | He fought against racial discrimination. Va lluitar contra la discriminació racial. 494 | I know that there was a big church here. Sé que aquí hi havia una església gran. 495 | I noticed that she sat in the front row. Vaig notar que ella va seure a la fila del davant. 496 | I was in the shower when the phone rang. Estava en la dutxa quan ha sonat el telèfon. 497 | President Clinton denied the accusation. El president Clinton va negar l'acusació. 498 | The men are wearing short sleeve shirts. Els homes porten camises de màniga curta. 499 | What do these dots represent on the map? Què signifiquen aquests punts al mapa? 500 | Will you please stop talking about food? Podries deixar de parlar de menjar? 501 | German is the best language in the world. L'alemany és la millor llengua del món. 502 | How many people are there in your family? Quants són a la seva família? 503 | I asked him many questions about ecology. Li vaig fer moltes preguntes sobre ecologia. 504 | I don't have the strength to keep trying. No tinc la força per continuar triant. 505 | I started learning English six years ago. Fa sis anys que vaig començar a aprendre anglès. 506 | I will ask him where he went last Sunday. Li preguntaré on va anar el diumenge. 507 | I'm surprised that he accepted the offer. Em sorprèn que acceptés l'oferiment. 508 | It is difficult to speak three languages. És difícil parlar tres llengues. 509 | There are many beautiful parks in London. A Londres hi han molts parcs bonics. 510 | Tom does everything he can to save money. En Tom fa tot el que pot per estalviar. 511 | Tom goes to Boston every once in a while. En Tom va a Boston de tant en tant. 512 | I am sure of his winning the tennis match. Estic segur de la seva victòria al tennis. 513 | I don't know the reason why he went there. No sé el motiu pel qual va anar-hi. 514 | I'd like to know when you can send it out. M'agadaria saber quan ho pot enviar. 515 | Nothing happens unless you make it happen. No passa res si tu no fas que passi. 516 | This is the best book that I've ever read. És el millor llibre que he llegit mai. 517 | As we go up higher, the air becomes cooler. Com més amunt anem, més fresc és l'aire. 518 | Do you support or oppose the death penalty? Estàs a favor o en contra de la pena de mort? 519 | English is not easy, but it is interesting. L'anglès no és fàcil, però és interessant. 520 | I don't have anything to say to any of you. No tinc res a dir-vos a cap de vosaltres. 521 | I don't know for certain when he will come. No sé del cert quan vindrà. 522 | I eat a boiled egg for breakfast every day. Cada dia em menjo un ou dur per esmorzar. 523 | I have been studying French four years now. Fa quatre anys que estudio francès. 524 | I told you to be here on time this morning. Et vaig dir que havies de ser aquí puntual aquest matí. 525 | I'm fed up with him always preaching to me. Estic tip que em sermonegi constantment. 526 | I'm fed up with him always preaching to me. Estic tip dels seus sermons constants. 527 | I'm getting off the train at the next stop. Em baixo del tren a la pròxima estació. 528 | It seems those two are made for each other. Sembla que aquell parell estan fets l'un per l'altre. 529 | This is the place where my father was born. Aquest és el lloc on va nèixer el meu pare. 530 | When will it be convenient for you to come? Quan li convendria venir? 531 | Give him this message the moment he arrives. Dóna-li aquest missatge quan arribi. 532 | I demanded that he pay the bill immediately. Li vaig demanar de pagar la factura immediatament. 533 | I feel like telling him what I think of him. Tinc ganes de dir-li què penso d'ell. 534 | I really need to take care of some business. He de tenir cura d'alguns negocis. 535 | I refused to eat until my parents came home. No vaig voler menjar fins que els meus pares no tornessin a casa. 536 | Japan imports great quantities of crude oil. El Japó importa una gran quantitat de petroli. 537 | She makes him do his homework before dinner. Ella l'obliga a fer els deures abans de sopar. 538 | They fell into the conversation immediately. Van passar al tema a l'instant. 539 | You should pay more attention to what I say. Deuries prestar més atenció a allò que dic. 540 | Both of them are unpredictable and impatient. Tots dos són impredictibles i impacients. 541 | Her explanation of the problem made no sense. La seva explicació del problema no tenia ni cap ni peus. 542 | I am going to do it whether you agree or not. Ho faré, estigueu o no d'acord amb mi. 543 | I didn't know you were that kind of a person. No sabia que eres així. 544 | I will take you to the zoo one of these days. Un dia d'aquests et portaré al zoo. 545 | My son has gone to America to study medicine. El meu fill ha anat a Amèrica a estudiar medicina. 546 | She says she brushes her teeth every morning. Ella diu que es raspatlla les dents tots els dematins. 547 | We need to invest in clean, renewable energy. Hem d'invertir en energia neta i renovable. 548 | He is one of the candidates running for mayor. És un dels candidats que es presenta per alcalde. 549 | I haven't got the nerve to ask you for a loan. No tinc valor per demanar-te un préstec. 550 | I'm getting off the train at the next station. Em baixo del tren a la pròxima estació. 551 | It is said that golf is very popular in Japan. Es diu que el golf és molt popular al Japó. 552 | It seems I'm going to be up all night tonight. Sembla que avui estaré despert tota la nit. 553 | Please wash your hands properly before eating. Siusplau renteu-vos les mans com cal abans de menjar. 554 | The urban population of America is increasing. La població urbana a Amèrica està creixent. 555 | I thought she was angry and would just go away. Vaig pensar que s'havia enfadat i que se n'aniria. 556 | It doesn't matter whether he comes late or not. No hi fa res si ve tard o no. 557 | She buys what she wants regardless of the cost. Compra el que vol sense fixar-se en el que val. 558 | She's curious to find out who sent the flowers. Ella té curiositat per saber qui va enviar les flors. 559 | Unfortunately, my birthday is only once a year. Malauradament, el meu aniversario només succeeix una vegada a l'any. 560 | What would it cost to have this chair repaired? Quant costaria arreglar aquesta cadira? 561 | Drink some coffee. It tastes very good, I think. Pren una mica de cafè. Té molt bon gust, crec. 562 | He and his sisters are currently living in Tokyo. En aquest moment, ell i les seves germanes viuen a Tòquio. 563 | He never fails to write to his mother every week. No passa una setmana que no li escrigui a la seva mare. 564 | I have a friend whose father is a famous pianist. Tinc un amic el pare del qual és un pianista famós. 565 | I'm not interested in going to the baseball game. No tinc cap interès a anar al partit de beisbol. 566 | I'm sorry, but I can't find the book you lent me. Em sap greu, però no trobo el llibre que em vas deixar. 567 | If only I knew, I would tell you all that I knew. Si ho sabés, et diria tot el que sé. 568 | She tried to squeeze the juice out of the orange. Va provar d'escórrer la taronja. 569 | This story is far more interesting than that one. Aquesta història és molt més interessant que aquella. 570 | I took it for granted that he would pass the exam. Dono per descomptat que aprovarà l'examen. 571 | They insisted on my making use of the opportunity. Em varen insistir per a que aprofitès aquella oportunitat. 572 | Do you know which deity this temple is dedicated to? Sabeu a quina divinitat està dedicat aquest temple? 573 | Why don't we see if Tom wants to play cards with us? Perquè no mirem si en Tom vol jugar a les cartes amb niosaltres? 574 | I was glad to see that he finally came to his senses. Vaig estar content de veure que al final va posar-hi seny. 575 | It's difficult for me to express myself in Esperanto. Per mi és difícil expressar-me en esperanto. 576 | I want to live in a quiet city where the air is clean. Vull viure a una ciutat tranquila amb l'aire pur. 577 | If you don't want to be alone, I can keep you company. Si no vols estar sol, puc fer-te companyia. 578 | He will take over the business when his father retires. Ell continuarà el negoci quan son pare es jubili. 579 | My mother likes tulips very much and so does my sister. A ma mare li agraden molt les tulipes i a ma germana també. 580 | This cola has lost its fizz and doesn't taste any good. Aquesta cola s'ha esbravat i no té bon gust. 581 | Tom is accustomed to calling up girls on the telephone. En Tom acostuma a trucar noies. 582 | Cuzco is one of the most interesting places in the world. Cuzco és un dels indrets més interessants del món. 583 | I stayed in bed one more day just to be on the safe side. Em vaig quedar un dia més al llit per si de cas. 584 | Tom will likely be discharged from the hospital tomorrow. Demà donaran d'alta de l'hospital en Tom. 585 | "How are you feeling this morning?" "Pretty good, thanks." "Com et sents aquest matí?" "Bastant bé, gràcies." 586 | People of my generation all think the same way about this. Tota la gent de la meva generació pensen igual sobre això. 587 | The only useful answers are those that raise new questions. Les úniques respostes útils són les que creen noves preguntes. 588 | It takes us thirty minutes to walk from here to the station. D'aquí a l'estació triguem mitja hora a peu. 589 | The secret of longevity is to choose your parents carefully. El secret de la longevitat és triar amb compte els pares. 590 | It takes about 10 minutes to get to the train station by foot. Tens uns 10 minuts d'aquí a l'estació a peu. 591 | This medicine must not be placed within the reach of children. Aquest medicament no s'ha de deixar a la ma dels nins. 592 | You told her that you had finished the work three days before. Li vas dir que havies enllestit la feina feia tres dies. 593 | His father died, and to make matters worse, his mother fell ill. Son pare es va morir, i per acabar-ho d'adobar, sa mare es va posar malalta. 594 | Try to understand it in Spanish, without translating to English. Tracta d'entendre-ho amb espanyol, sense traduïr-lo amb anglès. 595 | We lost our way, and what was worse, we were caught in a shower. Ens vam perdre i, encara pitjor, ens va enxampar un xàfec. 596 | She's worried since she hasn't heard from her son for many months. Està amoïnada perquè fa mesos que no té notícia del seu fill. 597 | I suspected that he was telling a lie, but that didn't surprise me. Sospitava que m'estava dient una mentida, però això no em va sorprendre. 598 | My daughter won't find it easy to get accustomed to the new school. La meva filla no trobarà fàcil per acostumar-se a la nova escola. 599 | The bullet penetrated his chest, leaving him in critical condition. La bala va penetrar al seu pit i el va deixar en estat crític. 600 | I wanted to buy the book, but I found I had no more than 200 yen with me. Volia comprar el llibre, però vaig adonar-me que no duia més de 200 iens. 601 | For the first time in more than 6 years, the unemployment rate is below 6%. Per primera vegada en més de 6 anys, la taxa d'atur està per davall del 6%. 602 | We would have bought the plane tickets if the price had been a little lower. Hauríem comprat els bitllets d'avió si el preu fos un pèl més baix. 603 | My friend has had three jobs in a year; he never sticks to anything for long. El meu amic ha treballat a tres llocs diferents en un any; res no li dura gaire. 604 | You can't park in a handicapped parking space unless you have a special permit. No pots aparcar a una plaça d'aparcament per discapacitats si no tens un permís especial. 605 | Drinking lots of water is good for you, sure, but one can't drink that much water at once. Beure molta aigua és bo per tu, segur, però no es pot beure tanta aigua de cop. 606 | We're gonna make sure that no one is taking advantage of the American people for their own short-term gain. Ens assegurarem que ningú s'estiga aprofitant del poble americà per al seu propi interès a curt termini. 607 | -------------------------------------------------------------------------------- /data/household_power_consumption.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DataScienceUB/DeepLearningMaster2019/e4106e183a2c171ef62b1c3bcdc702667af62b0b/data/household_power_consumption.zip -------------------------------------------------------------------------------- /data/iris_test.csv: -------------------------------------------------------------------------------- 1 | 30,4,setosa,versicolor,virginica 2 | 5.9,3.0,4.2,1.5,1 3 | 6.9,3.1,5.4,2.1,2 4 | 5.1,3.3,1.7,0.5,0 5 | 6.0,3.4,4.5,1.6,1 6 | 5.5,2.5,4.0,1.3,1 7 | 6.2,2.9,4.3,1.3,1 8 | 5.5,4.2,1.4,0.2,0 9 | 6.3,2.8,5.1,1.5,2 10 | 5.6,3.0,4.1,1.3,1 11 | 6.7,2.5,5.8,1.8,2 12 | 7.1,3.0,5.9,2.1,2 13 | 4.3,3.0,1.1,0.1,0 14 | 5.6,2.8,4.9,2.0,2 15 | 5.5,2.3,4.0,1.3,1 16 | 6.0,2.2,4.0,1.0,1 17 | 5.1,3.5,1.4,0.2,0 18 | 5.7,2.6,3.5,1.0,1 19 | 4.8,3.4,1.9,0.2,0 20 | 5.1,3.4,1.5,0.2,0 21 | 5.7,2.5,5.0,2.0,2 22 | 5.4,3.4,1.7,0.2,0 23 | 5.6,3.0,4.5,1.5,1 24 | 6.3,2.9,5.6,1.8,2 25 | 6.3,2.5,4.9,1.5,1 26 | 5.8,2.7,3.9,1.2,1 27 | 6.1,3.0,4.6,1.4,1 28 | 5.2,4.1,1.5,0.1,0 29 | 6.7,3.1,4.7,1.5,1 30 | 6.7,3.3,5.7,2.5,2 31 | 6.4,2.9,4.3,1.3,1 32 | -------------------------------------------------------------------------------- /data/iris_training.csv: -------------------------------------------------------------------------------- 1 | 120,4,setosa,versicolor,virginica 2 | 6.4,2.8,5.6,2.2,2 3 | 5.0,2.3,3.3,1.0,1 4 | 4.9,2.5,4.5,1.7,2 5 | 4.9,3.1,1.5,0.1,0 6 | 5.7,3.8,1.7,0.3,0 7 | 4.4,3.2,1.3,0.2,0 8 | 5.4,3.4,1.5,0.4,0 9 | 6.9,3.1,5.1,2.3,2 10 | 6.7,3.1,4.4,1.4,1 11 | 5.1,3.7,1.5,0.4,0 12 | 5.2,2.7,3.9,1.4,1 13 | 6.9,3.1,4.9,1.5,1 14 | 5.8,4.0,1.2,0.2,0 15 | 5.4,3.9,1.7,0.4,0 16 | 7.7,3.8,6.7,2.2,2 17 | 6.3,3.3,4.7,1.6,1 18 | 6.8,3.2,5.9,2.3,2 19 | 7.6,3.0,6.6,2.1,2 20 | 6.4,3.2,5.3,2.3,2 21 | 5.7,4.4,1.5,0.4,0 22 | 6.7,3.3,5.7,2.1,2 23 | 6.4,2.8,5.6,2.1,2 24 | 5.4,3.9,1.3,0.4,0 25 | 6.1,2.6,5.6,1.4,2 26 | 7.2,3.0,5.8,1.6,2 27 | 5.2,3.5,1.5,0.2,0 28 | 5.8,2.6,4.0,1.2,1 29 | 5.9,3.0,5.1,1.8,2 30 | 5.4,3.0,4.5,1.5,1 31 | 6.7,3.0,5.0,1.7,1 32 | 6.3,2.3,4.4,1.3,1 33 | 5.1,2.5,3.0,1.1,1 34 | 6.4,3.2,4.5,1.5,1 35 | 6.8,3.0,5.5,2.1,2 36 | 6.2,2.8,4.8,1.8,2 37 | 6.9,3.2,5.7,2.3,2 38 | 6.5,3.2,5.1,2.0,2 39 | 5.8,2.8,5.1,2.4,2 40 | 5.1,3.8,1.5,0.3,0 41 | 4.8,3.0,1.4,0.3,0 42 | 7.9,3.8,6.4,2.0,2 43 | 5.8,2.7,5.1,1.9,2 44 | 6.7,3.0,5.2,2.3,2 45 | 5.1,3.8,1.9,0.4,0 46 | 4.7,3.2,1.6,0.2,0 47 | 6.0,2.2,5.0,1.5,2 48 | 4.8,3.4,1.6,0.2,0 49 | 7.7,2.6,6.9,2.3,2 50 | 4.6,3.6,1.0,0.2,0 51 | 7.2,3.2,6.0,1.8,2 52 | 5.0,3.3,1.4,0.2,0 53 | 6.6,3.0,4.4,1.4,1 54 | 6.1,2.8,4.0,1.3,1 55 | 5.0,3.2,1.2,0.2,0 56 | 7.0,3.2,4.7,1.4,1 57 | 6.0,3.0,4.8,1.8,2 58 | 7.4,2.8,6.1,1.9,2 59 | 5.8,2.7,5.1,1.9,2 60 | 6.2,3.4,5.4,2.3,2 61 | 5.0,2.0,3.5,1.0,1 62 | 5.6,2.5,3.9,1.1,1 63 | 6.7,3.1,5.6,2.4,2 64 | 6.3,2.5,5.0,1.9,2 65 | 6.4,3.1,5.5,1.8,2 66 | 6.2,2.2,4.5,1.5,1 67 | 7.3,2.9,6.3,1.8,2 68 | 4.4,3.0,1.3,0.2,0 69 | 7.2,3.6,6.1,2.5,2 70 | 6.5,3.0,5.5,1.8,2 71 | 5.0,3.4,1.5,0.2,0 72 | 4.7,3.2,1.3,0.2,0 73 | 6.6,2.9,4.6,1.3,1 74 | 5.5,3.5,1.3,0.2,0 75 | 7.7,3.0,6.1,2.3,2 76 | 6.1,3.0,4.9,1.8,2 77 | 4.9,3.1,1.5,0.1,0 78 | 5.5,2.4,3.8,1.1,1 79 | 5.7,2.9,4.2,1.3,1 80 | 6.0,2.9,4.5,1.5,1 81 | 6.4,2.7,5.3,1.9,2 82 | 5.4,3.7,1.5,0.2,0 83 | 6.1,2.9,4.7,1.4,1 84 | 6.5,2.8,4.6,1.5,1 85 | 5.6,2.7,4.2,1.3,1 86 | 6.3,3.4,5.6,2.4,2 87 | 4.9,3.1,1.5,0.1,0 88 | 6.8,2.8,4.8,1.4,1 89 | 5.7,2.8,4.5,1.3,1 90 | 6.0,2.7,5.1,1.6,1 91 | 5.0,3.5,1.3,0.3,0 92 | 6.5,3.0,5.2,2.0,2 93 | 6.1,2.8,4.7,1.2,1 94 | 5.1,3.5,1.4,0.3,0 95 | 4.6,3.1,1.5,0.2,0 96 | 6.5,3.0,5.8,2.2,2 97 | 4.6,3.4,1.4,0.3,0 98 | 4.6,3.2,1.4,0.2,0 99 | 7.7,2.8,6.7,2.0,2 100 | 5.9,3.2,4.8,1.8,1 101 | 5.1,3.8,1.6,0.2,0 102 | 4.9,3.0,1.4,0.2,0 103 | 4.9,2.4,3.3,1.0,1 104 | 4.5,2.3,1.3,0.3,0 105 | 5.8,2.7,4.1,1.0,1 106 | 5.0,3.4,1.6,0.4,0 107 | 5.2,3.4,1.4,0.2,0 108 | 5.3,3.7,1.5,0.2,0 109 | 5.0,3.6,1.4,0.2,0 110 | 5.6,2.9,3.6,1.3,1 111 | 4.8,3.1,1.6,0.2,0 112 | 6.3,2.7,4.9,1.8,2 113 | 5.7,2.8,4.1,1.3,1 114 | 5.0,3.0,1.6,0.2,0 115 | 6.3,3.3,6.0,2.5,2 116 | 5.0,3.5,1.6,0.6,0 117 | 5.5,2.6,4.4,1.2,1 118 | 5.7,3.0,4.2,1.2,1 119 | 4.4,2.9,1.4,0.2,0 120 | 4.8,3.0,1.4,0.1,0 121 | 5.5,2.4,3.7,1.0,1 122 | -------------------------------------------------------------------------------- /data/jena_climate_2009_2016.csv.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DataScienceUB/DeepLearningMaster2019/e4106e183a2c171ef62b1c3bcdc702667af62b0b/data/jena_climate_2009_2016.csv.zip -------------------------------------------------------------------------------- /data/monalisa.jpg: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/DataScienceUB/DeepLearningMaster2019/e4106e183a2c171ef62b1c3bcdc702667af62b0b/data/otto.zip -------------------------------------------------------------------------------- /data/positive-words.txt: -------------------------------------------------------------------------------- 1 | ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; 2 | ; 3 | ; Opinion Lexicon: Positive 4 | ; 5 | ; This file contains a list of POSITIVE opinion words (or sentiment words). 6 | ; 7 | ; This file and the papers can all be downloaded from 8 | ; http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html 9 | ; 10 | ; If you use this list, please cite the following paper: 11 | ; 12 | ; Minqing Hu and Bing Liu. "Mining and Summarizing Customer Reviews." 13 | ; Proceedings of the ACM SIGKDD International Conference on Knowledge 14 | ; Discovery and Data Mining (KDD-2004), Aug 22-25, 2004, Seattle, 15 | ; Washington, USA, 16 | ; Notes: 17 | ; 1. The appearance of an opinion word in a sentence does not necessarily 18 | ; mean that the sentence expresses a positive or negative opinion. 19 | ; See the paper below: 20 | ; 21 | ; Bing Liu. "Sentiment Analysis and Subjectivity." An chapter in 22 | ; Handbook of Natural Language Processing, Second Edition, 23 | ; (editors: N. Indurkhya and F. J. Damerau), 2010. 24 | ; 25 | ; 2. You will notice many misspelled words in the list. They are not 26 | ; mistakes. They are included as these misspelled words appear 27 | ; frequently in social media content. 28 | ; 29 | ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; 30 | 31 | a+ 32 | abound 33 | abounds 34 | abundance 35 | abundant 36 | accessable 37 | accessible 38 | acclaim 39 | acclaimed 40 | acclamation 41 | accolade 42 | accolades 43 | accommodative 44 | accomodative 45 | accomplish 46 | accomplished 47 | accomplishment 48 | accomplishments 49 | accurate 50 | accurately 51 | achievable 52 | achievement 53 | achievements 54 | achievible 55 | acumen 56 | adaptable 57 | adaptive 58 | adequate 59 | adjustable 60 | admirable 61 | admirably 62 | admiration 63 | admire 64 | admirer 65 | admiring 66 | admiringly 67 | adorable 68 | adore 69 | adored 70 | adorer 71 | adoring 72 | adoringly 73 | adroit 74 | adroitly 75 | adulate 76 | adulation 77 | adulatory 78 | advanced 79 | advantage 80 | advantageous 81 | advantageously 82 | advantages 83 | adventuresome 84 | adventurous 85 | advocate 86 | advocated 87 | advocates 88 | affability 89 | affable 90 | affably 91 | affectation 92 | affection 93 | affectionate 94 | affinity 95 | affirm 96 | affirmation 97 | affirmative 98 | affluence 99 | affluent 100 | afford 101 | affordable 102 | affordably 103 | afordable 104 | agile 105 | agilely 106 | agility 107 | agreeable 108 | agreeableness 109 | agreeably 110 | all-around 111 | alluring 112 | alluringly 113 | altruistic 114 | altruistically 115 | amaze 116 | amazed 117 | amazement 118 | amazes 119 | amazing 120 | amazingly 121 | ambitious 122 | ambitiously 123 | ameliorate 124 | amenable 125 | amenity 126 | amiability 127 | amiabily 128 | amiable 129 | amicability 130 | amicable 131 | amicably 132 | amity 133 | ample 134 | amply 135 | amuse 136 | amusing 137 | amusingly 138 | angel 139 | angelic 140 | apotheosis 141 | appeal 142 | appealing 143 | applaud 144 | appreciable 145 | appreciate 146 | appreciated 147 | appreciates 148 | appreciative 149 | appreciatively 150 | appropriate 151 | approval 152 | approve 153 | ardent 154 | ardently 155 | ardor 156 | articulate 157 | aspiration 158 | aspirations 159 | aspire 160 | assurance 161 | assurances 162 | assure 163 | assuredly 164 | assuring 165 | astonish 166 | astonished 167 | astonishing 168 | astonishingly 169 | astonishment 170 | astound 171 | astounded 172 | astounding 173 | astoundingly 174 | astutely 175 | attentive 176 | attraction 177 | attractive 178 | attractively 179 | attune 180 | audible 181 | audibly 182 | auspicious 183 | authentic 184 | authoritative 185 | autonomous 186 | available 187 | aver 188 | avid 189 | avidly 190 | award 191 | awarded 192 | awards 193 | awe 194 | awed 195 | awesome 196 | awesomely 197 | awesomeness 198 | awestruck 199 | awsome 200 | backbone 201 | balanced 202 | bargain 203 | beauteous 204 | beautiful 205 | beautifullly 206 | beautifully 207 | beautify 208 | beauty 209 | beckon 210 | beckoned 211 | beckoning 212 | beckons 213 | believable 214 | believeable 215 | beloved 216 | benefactor 217 | beneficent 218 | beneficial 219 | beneficially 220 | beneficiary 221 | benefit 222 | benefits 223 | benevolence 224 | benevolent 225 | benifits 226 | best 227 | best-known 228 | best-performing 229 | best-selling 230 | better 231 | better-known 232 | better-than-expected 233 | beutifully 234 | blameless 235 | bless 236 | blessing 237 | bliss 238 | blissful 239 | blissfully 240 | blithe 241 | blockbuster 242 | bloom 243 | blossom 244 | bolster 245 | bonny 246 | bonus 247 | bonuses 248 | boom 249 | booming 250 | boost 251 | boundless 252 | bountiful 253 | brainiest 254 | brainy 255 | brand-new 256 | brave 257 | bravery 258 | bravo 259 | breakthrough 260 | breakthroughs 261 | breathlessness 262 | breathtaking 263 | breathtakingly 264 | breeze 265 | bright 266 | brighten 267 | brighter 268 | brightest 269 | brilliance 270 | brilliances 271 | brilliant 272 | brilliantly 273 | brisk 274 | brotherly 275 | bullish 276 | buoyant 277 | cajole 278 | calm 279 | calming 280 | calmness 281 | capability 282 | capable 283 | capably 284 | captivate 285 | captivating 286 | carefree 287 | cashback 288 | cashbacks 289 | catchy 290 | celebrate 291 | celebrated 292 | celebration 293 | celebratory 294 | champ 295 | champion 296 | charisma 297 | charismatic 298 | charitable 299 | charm 300 | charming 301 | charmingly 302 | chaste 303 | cheaper 304 | cheapest 305 | cheer 306 | cheerful 307 | cheery 308 | cherish 309 | cherished 310 | cherub 311 | chic 312 | chivalrous 313 | chivalry 314 | civility 315 | civilize 316 | clarity 317 | classic 318 | classy 319 | clean 320 | cleaner 321 | cleanest 322 | cleanliness 323 | cleanly 324 | clear 325 | clear-cut 326 | cleared 327 | clearer 328 | clearly 329 | clears 330 | clever 331 | cleverly 332 | cohere 333 | coherence 334 | coherent 335 | cohesive 336 | colorful 337 | comely 338 | comfort 339 | comfortable 340 | comfortably 341 | comforting 342 | comfy 343 | commend 344 | commendable 345 | commendably 346 | commitment 347 | commodious 348 | compact 349 | compactly 350 | compassion 351 | compassionate 352 | compatible 353 | competitive 354 | complement 355 | complementary 356 | complemented 357 | complements 358 | compliant 359 | compliment 360 | complimentary 361 | comprehensive 362 | conciliate 363 | conciliatory 364 | concise 365 | confidence 366 | confident 367 | congenial 368 | congratulate 369 | congratulation 370 | congratulations 371 | congratulatory 372 | conscientious 373 | considerate 374 | consistent 375 | consistently 376 | constructive 377 | consummate 378 | contentment 379 | continuity 380 | contrasty 381 | contribution 382 | convenience 383 | convenient 384 | conveniently 385 | convience 386 | convienient 387 | convient 388 | convincing 389 | convincingly 390 | cool 391 | coolest 392 | cooperative 393 | cooperatively 394 | cornerstone 395 | correct 396 | correctly 397 | cost-effective 398 | cost-saving 399 | counter-attack 400 | counter-attacks 401 | 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-------------------------------------------------------------------------------- 1 | # Deep Learning from Scratch 2 | This course is organized by the Data Science Group @ UB 3 | -------------------------------------------------------------------------------- /images/TanhReal.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DataScienceUB/DeepLearningMaster2019/e4106e183a2c171ef62b1c3bcdc702667af62b0b/images/TanhReal.gif -------------------------------------------------------------------------------- /images/alexnet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/DataScienceUB/DeepLearningMaster2019/e4106e183a2c171ef62b1c3bcdc702667af62b0b/images/alexnet.png -------------------------------------------------------------------------------- /images/autoencoder.jpg: -------------------------------------------------------------------------------- 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boto==2.48.0 4 | boto3==1.5.6 5 | botocore==1.8.20 6 | bz2file==0.98 7 | certifi==2017.11.5 8 | chardet==3.0.4 9 | cycler==0.10.0 10 | decorator==4.1.2 11 | docutils==0.14 12 | edward==1.3.4 13 | entrypoints==0.2.3 14 | enum34==1.1.6 15 | future==0.16.0 16 | futures==3.1.1 17 | gensim==3.2.0 18 | gym==0.9.4 19 | h5py==2.7.1 20 | html5lib==0.9999999 21 | idna==2.6 22 | ipykernel==4.6.1 23 | ipython==6.2.1 24 | ipython-genutils==0.2.0 25 | ipywidgets==7.0.3 26 | jedi==0.11.0 27 | Jinja2==2.9.6 28 | jmespath==0.9.3 29 | joblib==0.11 30 | jsonschema==2.6.0 31 | jupyter==1.0.0 32 | jupyter-client==5.1.0 33 | jupyter-console==5.2.0 34 | jupyter-core==4.3.0 35 | Keras==2.1.2 36 | Markdown==2.6.9 37 | MarkupSafe==1.0 38 | matplotlib==2.1.0 39 | mistune==0.7.4 40 | nbconvert==5.3.1 41 | nbformat==4.4.0 42 | notebook==5.2.0 43 | numpy==1.13.3 44 | olefile==0.44 45 | pandas==0.20.3 46 | pandocfilters==1.4.2 47 | parso==0.1.0 48 | patsy==0.4.1 49 | pexpect==4.2.1 50 | pickleshare==0.7.4 51 | Pillow==4.3.0 52 | prompt-toolkit==1.0.15 53 | protobuf==3.4.0 54 | ptyprocess==0.5.2 55 | pycurl==7.43.0 56 | pyglet==1.3.0 57 | Pygments==2.2.0 58 | pygobject==3.20.0 59 | pymc3==3.2 60 | pyparsing==2.2.0 61 | python-apt==1.1.0b1 62 | python-dateutil==2.6.1 63 | pytz==2017.2 64 | PyYAML==3.12 65 | pyzmq==16.0.2 66 | qtconsole==4.3.1 67 | requests==2.18.4 68 | s3transfer==0.1.12 69 | scikit-learn==0.19.0 70 | scipy==0.19.1 71 | seaborn==0.8.1 72 | simplegeneric==0.8.1 73 | six==1.11.0 74 | sklearn==0.0 75 | smart-open==1.5.6 76 | tensorflow==1.4.0rc1 77 | tensorflow-tensorboard==0.4.0rc1 78 | terminado==0.6 79 | testpath==0.3.1 80 | Theano==1.0.1 81 | tornado==4.5.2 82 | tqdm==4.19.5 83 | traitlets==4.3.2 84 | urllib3==1.22 85 | wcwidth==0.1.7 86 | webencodings==0.5.1 87 | Werkzeug==0.12.2 88 | widgetsnbextension==3.0.6 89 | --------------------------------------------------------------------------------