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Commit 5cb68421 authored by Perez Visaires, Jon's avatar Perez Visaires, Jon
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Notebooks actualizados.

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Scripts/Notebooks/NumbersNb.ipynb
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Scripts/Notebooks/SmokeyConvNb.ipynb 0 → 100644
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Scripts/Notebooks/SmokeyDeepNb.ipynb
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Scripts/Notebooks/SmokeyNb.ipynb
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Scripts/Notebooks/Untitled.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Autoencoder"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importamos las bibliotecas necesarias"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import os\n",
"import shutil\n",
"import sys\n",
"import math\n",
"import random\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import scipy.misc\n",
"import matplotlib.pyplot as plt\n",
"sys.path.append(\"../tools\") # Herramientas propias de MantaFlow\n",
"import uniio # Biblioteca para la lectura de ficheros .uni"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inicializamos las seed para funciones random. Al ser inicializadas al mismo número, el resultado no cambiará en cada ejecución."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(13)\n",
"tf.set_random_seed(13)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ruta a los datos de simulación, donde también se guardan los resultados."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"base_path = \"../data\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Carga de datos de simulación"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cargamos los datos desde los ficheros .uni en arrays de numpy."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"densities = []"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"for sim in range(1000, 2000):\n",
" if os.path.exists(\"%s/simSimple_%04d\" % (base_path, sim)): # Comprueba la existencia de las carpetas (cada una 100 frames de datos)\n",
" for i in range(0, 100):\n",
" filename = \"%s/simSimple_%04d/density_%04d.uni\" # Nombre de cada frame (densidad)\n",
" uni_path = filename % (base_path, sim, i) # 100 frames por sim, rellena parametros de la ruta\n",
" header, content = uniio.readUni(uni_path) # Devuelve una array np [Z, Y, X, C]\n",
" h = header[\"dimX\"]\n",
" w = header[\"dimY\"]\n",
" arr = content[:, ::-1, :, :] # Cambia el orden de Y\n",
" arr = np.reshape(arr, [w, h, 1]) # Deshecha Z\n",
" densities.append(arr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Necesitamos al menos 2 simulaciones para trabajar de manera adecuada."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"load_num = len(densities)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"if load_num <200:\n",
" print(\"Error - usa al menos dos simulaciones completas\")\n",
" exit(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convertimos la lista \"densities\" en una array de Numpy."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1100, 64, 64, 1)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"densities = np.reshape(densities, (len(densities), 64, 64, 1))\n",
"densities.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creación de set de validación"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creamos el set de validación de entre los datos de simulación generados, al menos una simulación completa o el 10% de los datos (el que sea mayor de los dos)."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Read uni files, total data (1100, 64, 64, 1)\n",
"Split into 990 training and 110 validation samples\n"
]
}
],
"source": [
"print(\"Read uni files, total data \" + format(densities.shape))\n",
"\n",
"vali_size = max(100, int(load_num * 0.1)) # Al menos una simu completa\n",
"vali_data = densities[load_num - vali_size : load_num, :]\n",
"densities = densities[0 : load_num - vali_size, :]\n",
"\n",
"print(\"Split into %d training and %d validation samples\" % (densities.shape[0], vali_data.shape[0]))\n",
"\n",
"load_num = densities.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(990, 64, 64, 1)\n",
"(110, 64, 64, 1)\n"
]
}
],
"source": [
"densities = np.reshape(densities, (len(densities), 64, 64, 1))\n",
"vali_data = np.reshape(vali_data, (len(vali_data), 64, 64, 1))\n",
"\n",
"print(densities.shape)\n",
"print(vali_data.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creación del modelo Autoencoder mediante Keras (Sequential)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importamos las bibliotecas de Keras"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Conv2D, MaxPooling2D, UpSampling2D"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creacion de las capas del modelo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En la primera capa debemos definir las dimensiones del input esperado."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"l0 = Conv2D(filters = 1, \n",
" kernel_size = (3, 3), \n",
" activation = \"relu\", \n",
" padding = \"same\", \n",
" input_shape = (densities.shape[1], densities.shape[2], densities.shape[3]))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"autoencoder = Sequential([l0])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"autoencoder.compile(optimizer = \"adadelta\", loss = \"binary_crossentropy\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 990 samples, validate on 110 samples\n",
"Epoch 1/10\n",
"990/990 [==============================] - 1s 508us/step - loss: 0.1760 - val_loss: 0.1559\n",
"Epoch 2/10\n",
"990/990 [==============================] - 0s 406us/step - loss: 0.1219 - val_loss: 0.1284\n",
"Epoch 3/10\n",
"990/990 [==============================] - 0s 407us/step - loss: 0.1087 - val_loss: 0.1143\n",
"Epoch 4/10\n",
"990/990 [==============================] - 0s 405us/step - loss: 0.1011 - val_loss: 0.1074\n",
"Epoch 5/10\n",
"990/990 [==============================] - 0s 409us/step - loss: 0.0943 - val_loss: 0.0977\n",
"Epoch 6/10\n",
"990/990 [==============================] - 0s 408us/step - loss: 0.0870 - val_loss: 0.0896\n",
"Epoch 7/10\n",
"990/990 [==============================] - 0s 416us/step - loss: 0.0790 - val_loss: 0.0824\n",
"Epoch 8/10\n",
"990/990 [==============================] - 0s 409us/step - loss: 0.0731 - val_loss: 0.0789\n",
"Epoch 9/10\n",
"990/990 [==============================] - 0s 422us/step - loss: 0.0688 - val_loss: 0.0766\n",
"Epoch 10/10\n",
"990/990 [==============================] - 0s 414us/step - loss: 0.0658 - val_loss: 0.0754\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7fcd0db7bf98>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"autoencoder.fit(densities, densities, \n",
" epochs = 10,\n",
" verbose = 1,\n",
" validation_data = (vali_data, vali_data),\n",
" shuffle = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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