diff --git a/requirements b/requirements index 7174011..7910569 100644 --- a/requirements +++ b/requirements @@ -1,4 +1,8 @@ -matplotlib -pandas +keras +jax[cuda12] +pydot + +matplotlib +pandas tqdm -scikit-learn +scikit-learn diff --git a/src/app.py b/src/app.py index 6c4ceb7..ec4039f 100644 --- a/src/app.py +++ b/src/app.py @@ -114,13 +114,13 @@ if __name__ == "__main__": #rand = 347617386 # LoR for electrical_grid #rand = 834535453 # LoR for heart #rand = 1793295160 # MLP for iris - #rand = 629702080 # MLP for frogs + #rand = 772284034 # MLP for frogs #rand = 1038336550 # KMe for frogs_no_target np.random.seed(rand) print(f"Using seed: {rand}") - ds, ml, sk = power_plant() + ds, ml, sk = frogs() epochs, _, _ = ml.learn(1000, verbose=True) ml.display_results() diff --git a/src/deep/dl.py b/src/deep/dl.py new file mode 100644 index 0000000..2cb7476 --- /dev/null +++ b/src/deep/dl.py @@ -0,0 +1,37 @@ +# *********************************************************** +# Esercizio 1.A +# L'esercizio prevede la costruzione di un generatore di caption di immagini. +# Utilizzando il tool preferito, costruire una CNN in grado di costruire +# un suitable embedding di un immagine, il quale verrà passato ad una RNN +# per la generazione del testo della caption. +# Per il training utilizzare il Flickr Dataset. +# https://www.kaggle.com/datasets/hsankesara/flickr-image-dataset + +# # Esercizio 1.B +# L'esercizio consiste nell'applicare una delle tecniche viste a lezione +# per l'object detection tramite bounding box per costruire un riconoscitore +# di oggetti all'interno di una immagine presa con una fotocamera. +# Il dataset da utilizzare è Object365, un dataset di HQ (high-quality) +# immagini con bounding boxes di oggetti. +# Contiene 365 oggetti, 600k immagini, e 10 milioni di bounding boxes. +# Se necessario eseguire un subsampling per ridurre le dimensioni del dataset. +# from ultralytics import YOLO +# model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training) +# model.train(data='Objects365.yaml', epochs=100, imgsz=640) + + +# *********************************************************** +# Esercizio 2.A +# Realizzare un sistema di pulizia immagini (image denoising), +# basato su una opportuna architettura ad Autoencoder, +# che prendendo in input un dataset di immagini soggette a "rumore" +# ne restituisca una versione "pulita". +# Utilizzare il dataset Fashion-MNIST (inserire un opportuno filtro di noise per ottenere l'input) +# https://www.kaggle.com/datasets/zalando-research/fashionmnist + +# Esercizio 2.B +# Realizzare un sistema di text classification per frasi (sequenze testuali) +# che possono essere considerate "positive" o "negative"(sentiment analysis). +# Il sistema deve basarsi sull'uso di architetture transformer. +# Suggerimento: vedere modelli offerti da HuggingFace. +# Alcuni dataset suggeriti su kaggle: https://www.kaggle.com/datasets?search=sentiment+analysis diff --git a/src/deep/esempi/Autoencoder.ipynb b/src/deep/esempi/Autoencoder.ipynb new file mode 100644 index 0000000..2cb1dfe --- /dev/null +++ b/src/deep/esempi/Autoencoder.ipynb @@ -0,0 +1,142 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Created on Tue Feb 22 10:32:06 2022\n", + "\n", + "@author: Luigi Portinale\n", + "\"\"\"\n", + "## load the libraries \n", + "from keras.api.layers import Dense, Input\n", + "from sklearn.model_selection import train_test_split\n", + "from keras.api.callbacks import EarlyStopping\n", + "import matplotlib.pyplot as plt\n", + "from keras.api.models import Model\n", + "\n", + "### read dataset \n", + "from keras.api.datasets import fashion_mnist as dataset\n", + "(train_x, train_y), (_, _) = dataset.load_data()\n", + "\n", + "## normalize and reshape the predictors \n", + "train_x = train_x / 255\n", + "\n", + "## create train and validation datasets\n", + "train_x, val_x, train_y, val_y = train_test_split(train_x, train_y, test_size=0.2)\n", + "\n", + "## reshape the inputs\n", + "train_x = train_x.reshape(-1, 784)\n", + "val_x = val_x.reshape(-1, 784)\n", + "\n", + "#Create Autoencoder architecture\n", + "## input layer\n", + "input_layer = Input(shape=(784,))\n", + "\n", + "## encoding architecture\n", + "encode_layer1 = Dense(1500, activation='relu')(input_layer)\n", + "encode_layer2 = Dense(1000, activation='relu')(encode_layer1)\n", + "encode_layer3 = Dense(500, activation='relu')(encode_layer2)\n", + "\n", + "## latent view\n", + "latent_view = Dense(10, activation='relu')(encode_layer3)\n", + "\n", + "## decoding architecture\n", + "decode_layer1 = Dense(500, activation='relu')(latent_view)\n", + "decode_layer2 = Dense(1000, activation='relu')(decode_layer1)\n", + "decode_layer3 = Dense(1500, activation='sigmoid')(decode_layer2)\n", + "\n", + "## output layer\n", + "output_layer = Dense(784)(decode_layer3)\n", + "\n", + "model = Model(input_layer, output_layer)\n", + "\n", + "#Print summary\n", + "model.summary()\n", + "\n", + "#train the model with early stopping callback.\n", + "model.compile(optimizer='adam', loss='mse')\n", + "early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1, mode='auto')\n", + "model.fit(train_x, train_x, epochs=20, batch_size=500, validation_data=(val_x, val_x), callbacks=[early_stopping])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m375/375\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# predictions\n", + "totals = 10\n", + "preds = model.predict(val_x)\n", + "\n", + "# Actual images\n", + "f, ax = plt.subplots(1,totals)\n", + "f.set_size_inches(80, 40)\n", + "for i in range(totals):\n", + " ax[i].imshow(val_x[i].reshape(28, 28))\n", + "plt.show()\n", + "\n", + "# Output images\n", + "f, ax = plt.subplots(1,totals)\n", + "f.set_size_inches(80, 40)\n", + "for i in range(totals):\n", + " ax[i].imshow(preds[i].reshape(28, 28))\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/deep/esempi/LSTMSeq_CLass.ipynb b/src/deep/esempi/LSTMSeq_CLass.ipynb new file mode 100644 index 0000000..d4fbdfb --- /dev/null +++ b/src/deep/esempi/LSTMSeq_CLass.ipynb @@ -0,0 +1,191 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential_1\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_1\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ embedding (Embedding)           │ (None, 500, 32)        │       160,000 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ lstm (LSTM)                     │ (None, 100)            │        53,200 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (Dense)                   │ (None, 1)              │           101 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m160,000\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ lstm (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m) │ \u001b[38;5;34m53,200\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m101\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Total params: 213,301 (833.21 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m213,301\u001b[0m (833.21 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Trainable params: 213,301 (833.21 KB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m213,301\u001b[0m (833.21 KB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
 Non-trainable params: 0 (0.00 B)\n",
+       "
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\u001b[1m35s\u001b[0m 44ms/step - accuracy: 0.8677 - loss: 0.3171\n", + "Accuracy: 87.01%\n" + ] + } + ], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Created on Wed Feb 23 10:29:02 2022\n", + "\n", + "@author: Luigi Portinale\n", + "\"\"\"\n", + "#The Large Movie Review Dataset (often referred to as the IMDB dataset) \n", + "#contains 25,000 highly-polar movie reviews (good or bad) for training \n", + "#and the same amount again for testing. \n", + "#The problem is to determine whether a given movie review has \n", + "#a positive or negative sentiment.\n", + "\n", + "#Word Embedding\n", + "#We will map each word onto a 32 length real valued vector. \n", + "#We will also limit the total number of words that we are interested \n", + "#in modeling to the 5000 most frequent words, and zero out the rest. \n", + "#Finally, the sequence length (number of words) in each review varies, \n", + "#so we will constrain each review to be 500 words, \n", + "#truncating long reviews and pad the shorter reviews with zero values.\n", + "\n", + "import numpy\n", + "from keras.api.datasets import imdb\n", + "from keras.api.models import Sequential\n", + "from keras.api.layers import Dense\n", + "from keras.api.layers import LSTM\n", + "from keras.api.layers import Embedding\n", + "from keras.api.layers import Input\n", + "from keras.api.preprocessing import sequence\n", + "\n", + "# fix random seed for reproducibility\n", + "numpy.random.seed(7)\n", + "# load the dataset but only keep the top n words, zero the rest\n", + "top_words = 5000\n", + "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)\n", + "# truncate and pad input sequences\n", + "max_review_length = 500\n", + "X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)\n", + "X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)\n", + "\n", + "\n", + "# create the model\n", + "embedding_vector_length = 32\n", + "model = Sequential()\n", + "#create the emebedding of the documents\n", + "model.add(Input(shape=(max_review_length,)))\n", + "model.add(Embedding(top_words, embedding_vector_length))\n", + "\n", + "#add an LSTM with 100 units and drop-out on both the input\n", + "#and the recurrent connections\n", + "model.add(LSTM(100, recurrent_dropout=0.05, dropout=0.01))\n", + "\n", + "#output layer for binary classification\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "\n", + "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", + "print(model.summary())\n", + "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=3, batch_size=64)\n", + "\n", + "# Final evaluation of the model\n", + "scores = model.evaluate(X_test, y_test, verbose=1)\n", + "print(\"Accuracy: %.2f%%\" % (scores[1]*100))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/deep/esempi/SimpleCNN.ipynb b/src/deep/esempi/SimpleCNN.ipynb new file mode 100644 index 0000000..d61c11d --- /dev/null +++ b/src/deep/esempi/SimpleCNN.ipynb @@ -0,0 +1,314 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train: X=(50000, 32, 32, 3), y=(50000, 1)\n", + "Test: X=(10000, 32, 32, 3), y=(10000, 1)\n" + ] + }, + { + "data": { + "image/png": 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Model: \"sequential_4\"\n",
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┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d_24 (Conv2D)              │ (None, 32, 32, 32)     │           896 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_25 (Conv2D)              │ (None, 32, 32, 32)     │         9,248 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_12 (MaxPooling2D) │ (None, 16, 16, 32)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_26 (Conv2D)              │ (None, 16, 16, 64)     │        18,496 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_27 (Conv2D)              │ (None, 16, 16, 64)     │        36,928 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_13 (MaxPooling2D) │ (None, 8, 8, 64)       │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_28 (Conv2D)              │ (None, 8, 8, 128)      │        73,856 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_29 (Conv2D)              │ (None, 8, 8, 128)      │       147,584 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_14 (MaxPooling2D) │ (None, 4, 4, 128)      │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_4 (Flatten)             │ (None, 2048)           │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_8 (Dense)                 │ (None, 128)            │       262,272 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_9 (Dense)                 │ (None, 10)             │         1,290 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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"\ttest_norm = test_norm / 255.0\n", + "\t# return normalized images\n", + "\treturn train_norm, test_norm\n", + "\n", + "def summarize_diagnostics(history):\n", + "\t# plot loss\n", + "\tpyplot.subplot(211)\n", + "\tpyplot.title('Cross Entropy Loss')\n", + "\tpyplot.plot(history.history['loss'], color='blue', label='train')\n", + "\tpyplot.plot(history.history['val_loss'], color='orange', label='test')\n", + "\t# plot accuracy\n", + "\tpyplot.subplot(212)\n", + "\tpyplot.title('Classification Accuracy')\n", + "\tpyplot.plot(history.history['accuracy'], color='blue', label='train')\n", + "\tpyplot.plot(history.history['val_accuracy'], color='orange', label='test')\n", + "# # save plot to file\n", + "# \tfilename = sys.argv[0].split('/')[-1]\n", + "# \tpyplot.savefig(filename + '_plot.png')\n", + "# \tpyplot.close()\n", + "\n", + "\n", + "# load dataset\n", + "(trainX, trainY), (testX, testY) = cifar10.load_data()\n", + "# summarize loaded dataset\n", + "#each image is 32x32x3 (rgb)\n", + "print('Train: X=%s, y=%s' % (trainX.shape, trainY.shape))\n", + "#50K images training\n", + "print('Test: X=%s, y=%s' % (testX.shape, testY.shape))\n", + "#10K images test\n", + "\n", + "#plot first few images\n", + "for i in range(9):\n", + "\t# define subplot\n", + "\tpyplot.subplot(330 + 1 + i)\n", + "\t# plot raw pixel data\n", + "\tpyplot.imshow(trainX[i])\n", + "\n", + "# show the figure\n", + "pyplot.show()\n", + "\n", + "# one hot encode target values\n", + "trainY = to_categorical(trainY)\n", + "testY = to_categorical(testY)\n", + "\n", + "# prepare pixel data\n", + "trainX, testX = prep_pixels(trainX, testX)\n", + "\n", + "#define the model\n", + "# example of a 3-block vgg style architecture\n", + "model = Sequential()\n", + "model.add(Input(shape=(32, 32, 3)))\n", + "# the feature extraction layers\n", + "model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n", + "# num. param 896\n", + "#we have 32 filters wich are 3x3x3 (input depth is 3 and the filter is 3x3)\n", + "#before applying the ReLu each filter adds the bias so we have 3x3x3+1=28 params\n", + "#we have 32 filters then 28x32=896\n", + "model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n", + "model.add(MaxPooling2D((2, 2)))\n", + "model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n", + "model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n", + "model.add(MaxPooling2D((2, 2)))\n", + "model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n", + "model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n", + "model.add(MaxPooling2D((2, 2)))\n", + "#now the classifier layers\n", + "model.add(Flatten())\n", + "model.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n", + "model.add(Dense(10, activation='softmax'))\n", + "\n", + "# print summary\n", + "model.summary()\n", + "\n", + "# compile model\n", + "#choose an optimizer\n", + "\n", + "opt1 = SGD(learning_rate=0.01, momentum=0.9)\n", + "opt2=Adam()\n", + "opt3=RMSprop(learning_rate=0.0009)\n", + "model.compile(optimizer=opt1, loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.6816 - loss: 0.8976 - val_accuracy: 0.7025 - val_loss: 0.8543\n", + "Epoch 2/5\n", + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.7490 - loss: 0.7154 - val_accuracy: 0.7196 - val_loss: 0.8193\n", + "Epoch 3/5\n", + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.7846 - loss: 0.6141 - val_accuracy: 0.7270 - val_loss: 0.8080\n", + "Epoch 4/5\n", + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.8161 - loss: 0.5266 - val_accuracy: 0.7373 - val_loss: 0.8084\n", + "Epoch 5/5\n", + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.8463 - loss: 0.4421 - val_accuracy: 0.7450 - val_loss: 0.7984\n", + "> 74.500\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# fit model\n", + "history = model.fit(trainX, trainY, epochs=5, batch_size=64, validation_data=(testX, testY), verbose=1)\n", + "# evaluate model\n", + "_, acc = model.evaluate(testX, testY, verbose=0)\n", + "print('> %.3f' % (acc * 100.0))\n", + "\n", + "# learning curves\n", + "summarize_diagnostics(history)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/deep/esempi/SimpleDNN.ipynb b/src/deep/esempi/SimpleDNN.ipynb new file mode 100644 index 0000000..6c5cd19 --- /dev/null +++ b/src/deep/esempi/SimpleDNN.ipynb @@ -0,0 +1,314 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Model: \"sequential\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ layer1 (Dense)                  │ (None, 64)             │        50,240 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ layer2 (Dense)                  │ (None, 64)             │         4,160 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ final (Dense)                   │ (None, 10)             │           650 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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\n" + ], + "text/plain": [ + "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n", + "┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n", + "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n", + "│ layer1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m50,240\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ layer2 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m4,160\u001b[0m │\n", + "├─────────────────────────────────┼────────────────────────┼───────────────┤\n", + "│ final (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m650\u001b[0m │\n", + "└─────────────────────────────────┴────────────────────────┴───────────────┘\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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 Trainable params: 55,050 (215.04 KB)\n",
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val_sparse_categorical_accuracy: 0.9607\n", + "Epoch 4/50\n", + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - loss: 0.0932 - sparse_categorical_accuracy: 0.9721 - val_loss: 0.1009 - val_sparse_categorical_accuracy: 0.9714\n", + "Epoch 5/50\n", + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - loss: 0.0736 - sparse_categorical_accuracy: 0.9776 - val_loss: 0.0994 - val_sparse_categorical_accuracy: 0.9737\n", + "Epoch 6/50\n", + "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - loss: 0.0638 - sparse_categorical_accuracy: 0.9800 - val_loss: 0.1072 - val_sparse_categorical_accuracy: 0.9706\n", + "Epoch 6: early stopping\n" + ] + }, + { + "data": { + "text/plain": [ + "{'loss': [0.34231796860694885,\n", + " 0.15515290200710297,\n", + " 0.11294537782669067,\n", + " 0.09125398844480515,\n", + " 0.07616668939590454,\n", + " 0.06609614938497543],\n", + " 'sparse_categorical_accuracy': [0.9008599519729614,\n", + " 0.9533999562263489,\n", + " 0.9660999774932861,\n", + " 0.9724400043487549,\n", + " 0.9772999882698059,\n", + " 0.9797799587249756],\n", + " 'val_loss': [0.17639364302158356,\n", + " 0.1290387213230133,\n", + " 0.1292431503534317,\n", + " 0.10090412199497223,\n", + " 0.09937908500432968,\n", + " 0.1071673259139061],\n", + " 'val_sparse_categorical_accuracy': [0.9494000673294067,\n", + " 0.9657000303268433,\n", + " 0.9607000350952148,\n", + " 0.9714000225067139,\n", + " 0.9737000465393066,\n", + " 0.9706000685691833]}" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Created on Tue Feb 22 12:13:57 2022\n", + "\n", + "@author: Luigi Portinale\n", + "\"\"\"\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from keras.api import layers, models, datasets, callbacks\n", + "model = models.Sequential()\n", + "\n", + "#create a DNN with an input layer of 784 units\n", + "model.add(layers.Input(shape=(784,)))\n", + "# first hidden layer of 64 units\n", + "model.add(layers.Dense(64, activation=\"relu\", name=\"layer1\"))\n", + "#second hidden layer of 64 units\n", + "model.add(layers.Dense(64, activation=\"relu\", name=\"layer2\"))\n", + "#final layer softmax layer for 10 classes\n", + "model.add(layers.Dense(10, activation=\"softmax\", name=\"final\"))\n", + "\n", + "# print summary\n", + "model.summary()\n", + "\n", + "#print input/output_layer shape\n", + "##first dimension is batch/sample size; None if no committment on that\n", + "model.input_shape\n", + "model.output_shape\n", + "\n", + "## load some data\n", + "(x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()\n", + "\n", + "# Preprocess the data (these are NumPy arrays)\n", + "#each mage is 28x28 = 784 px\n", + "# normalize rgb by dividing by 255\n", + "x_train = x_train.reshape(60000, 784).astype(\"float32\") / 255\n", + "x_test = x_test.reshape(10000, 784).astype(\"float32\") / 255\n", + "\n", + "y_train = y_train.astype(\"float32\")\n", + "y_test = y_test.astype(\"float32\")\n", + "\n", + "# Reserve 10,000 samples for validation\n", + "x_val = x_train[-10000:]\n", + "y_val = y_train[-10000:]\n", + "x_train = x_train[:-10000]\n", + "y_train = y_train[:-10000]\n", + "\n", + "# compile model\n", + "model.compile(\n", + " optimizer=\"rmsprop\",\n", + " loss=\"sparse_categorical_crossentropy\",\n", + " metrics=[\"sparse_categorical_accuracy\"],\n", + ")\n", + "\n", + "#Training\n", + "callbacks=[\n", + " callbacks.EarlyStopping(\n", + " # Stop training when `val_loss` is no longer improving\n", + " monitor=\"val_loss\",\n", + " # \"no longer improving\" being defined as \"no better than 1e-2 less\"\n", + " min_delta=1e-2,\n", + " # \"no longer improving\" being further defined as \"for at least 2 epochs\"\n", + " patience=2,\n", + " verbose=1,\n", + " )\n", + "]\n", + "print(\"Fit model on training data\")\n", + "#NB training stops either after 50 epochs or for early stopping before 50th epoch\n", + "history = model.fit(\n", + " x_train,\n", + " y_train,\n", + " batch_size=64, #mini-batch size --> 782 batches\n", + " epochs=50,\n", + " callbacks=callbacks,\n", + " # We pass some validation for\n", + " # monitoring validation loss and metrics\n", + " # at the end of each epoch\n", + " validation_data=(x_val, y_val),\n", + ")\n", + "\n", + "history.history" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluate on test data\n", + "\u001b[1m79/79\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.1219 - sparse_categorical_accuracy: 0.9653\n", + "test loss, test acc: [0.10738389194011688, 0.9700000286102295]\n", + "Generate predictions for 10 samples\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 307ms/step\n", + "predictions shape: (10, 10)\n", + "7\n", + "2\n", + "1\n", + "0\n", + "4\n", + "1\n", + "4\n", + "9\n", + "5\n", + "9\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Evaluate the model on the test data using `evaluate`\n", + "print(\"Evaluate on test data\")\n", + "results = model.evaluate(x_test, y_test, batch_size=128)\n", + "print(\"test loss, test acc:\", results)\n", + "\n", + "# Generate predictions (probabilities -- the output of the last layer)\n", + "# on new data using `predict`\n", + "print(\"Generate predictions for 10 samples\")\n", + "predictions = model.predict(x_test[:10])\n", + "print(\"predictions shape:\", predictions.shape)\n", + "\n", + "#Show the 3 predictions \n", + "images=x_test[:10]\n", + "\n", + "f, ax = plt.subplots(1,10)\n", + "f.set_size_inches(28, 28)\n", + "\n", + "for i in range(10):\n", + " #print class\n", + " print(np.argmax(predictions[i]))\n", + "\n", + " #Visualize actual image\n", + " ax[i].imshow(images[i].reshape(28, 28)*255)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/deep/esempi/WordEmbedding.ipynb b/src/deep/esempi/WordEmbedding.ipynb new file mode 100644 index 0000000..c36f464 --- /dev/null +++ b/src/deep/esempi/WordEmbedding.ipynb @@ -0,0 +1,100 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# -*- coding: utf-8 -*-\n", + "\"\"\"\n", + "Created on Wed Feb 23 10:53:10 2022\n", + "\n", + "@author: Luigi Portinale\n", + "\"\"\"\n", + "from numpy import array\n", + "from keras.api.preprocessing.sequence import pad_sequences\n", + "from keras.api.models import Sequential\n", + "from keras.api.layers import Dense\n", + "from keras.api.layers import Flatten\n", + "from keras.api.layers import Embedding\n", + "from keras.api.layers import Input\n", + "#from keras.utils.vis_utils import plot_model\n", + "\n", + "# define documents\n", + "docs = ['Well done!',\n", + "\t\t'Good work',\n", + "\t\t'Great effort',\n", + "\t\t'nice work',\n", + "\t\t'Excellent!',\n", + "\t\t'Weak',\n", + "\t\t'Poor effort!',\n", + "\t\t'not good',\n", + "\t\t'poor work',\n", + "\t\t'Could have done better.']\n", + "# define class labels (1: positive; 0: negative)\n", + "labels = array([1,1,1,1,1,0,0,0,0,0])\n", + "\n", + "def custom_one_hot(doc, vocab_size):\n", + " words = doc.split()\n", + " encoded = [hash(word) % vocab_size for word in words]\n", + " return encoded\n", + "\n", + "# integer encode the documents\n", + "#keras tensorflow one_hot(d,s) is a hash function returning an integer \n", + "#for each word d; it uses a vocabulary size s that should be the number\n", + "#of possible words to hash. If this is greater than the actual vocabulary\n", + "#size then the probability of collisions is reduced\n", + "vocab_size = 50\n", + "encoded_docs = [custom_one_hot(d, vocab_size) for d in docs]\n", + "print(\"The encoded documents:\")\n", + "print(encoded_docs)\n", + "# pad documents to a max length of 4 words\n", + "max_length = 4\n", + "padded_docs = pad_sequences(encoded_docs, maxlen=max_length, padding='post')\n", + "# each document is then a vector of 4 integers!\n", + "print(\"The padded documents:\")\n", + "print(padded_docs)\n", + "\n", + "\n", + "# define the model\n", + "model = Sequential()\n", + "\n", + "#The Embedding has a vocabulary of 50 and an input length of 4. \n", + "#We will choose a small embedding space of 8 dimensions.\n", + "#Embedding needs: the vocabulary size, \n", + "#the embedding output dimension (each word will be embedded in a vector\n", + "#with this dimension) that is the output size of the layer and \n", + "#finally the length of each document that\n", + "#will be provided in input (input size of the layer)\n", + "model.add(Input(shape=(max_length,)))\n", + "model.add(Embedding(vocab_size, 8))\n", + "# each document in input at the above layer will have a vector\n", + "#of 8 integers for every word in the document\n", + "\n", + "#now we flatten the output of the embeddings\n", + "model.add(Flatten())\n", + "#then we use an outout layer for binary classification\n", + "model.add(Dense(1, activation='sigmoid'))\n", + "\n", + "#plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n", + "# compile the model\n", + "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n", + "# summarize the model\n", + "print(model.summary())\n", + "# fit the model\n", + "model.fit(padded_docs, labels, epochs=100, verbose=1)\n", + "# evaluate the model\n", + "loss, accuracy = model.evaluate(padded_docs, labels, verbose=1)\n", + "print('Accuracy: %f' % (accuracy*100))" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/learning/supervised.py b/src/learning/supervised.py index 1e7541c..81cece2 100644 --- a/src/learning/supervised.py +++ b/src/learning/supervised.py @@ -85,8 +85,9 @@ class MultiLayerPerceptron(MLAlgorithm): def _h0(self, x:np.ndarray) -> np.ndarray: self.activations = [x] - for layer in self.layers: - x = lrelu(with_bias(x).dot(layer)) + for i, layer in enumerate(self.layers): + x = with_bias(x).dot(layer) + if i + 1 < len(self.layers): x = lrelu(x) self.activations.append(x) # saving activation result return softmax(x)