{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"&nbsp;\n&nbsp;\n&nbsp;\n\n# **IMPORTACIONES**","metadata":{}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nimport tensorflow as tf\nimport tensorflow.keras as keras\nfrom tensorflow.keras import layers\n\nfrom tensorflow.keras.layers.experimental import preprocessing\nfrom tensorflow.keras.preprocessing import image_dataset_from_directory\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\nimport matplotlib.pyplot as plt\nimport cv2\n\nfrom sklearn.model_selection import train_test_split\n\nfrom tensorflow.keras.callbacks import EarlyStopping","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"&nbsp;\n&nbsp;\n&nbsp;\n\n# **DATASET DE ENTRENAMIENTO**","metadata":{}},{"cell_type":"markdown","source":"### Carga del dataset de entrenamiento","metadata":{}},{"cell_type":"code","source":"mainpath_train = '../input/landmark-recognition-2021/train'\ntraindf = pd.read_csv('../input/landmark-recognition-2021/train.csv')\n\n#Añadir a train.csv la columna con la dirección de cada imagen para su posterior lectura\ntraindf['img_path'] = traindf['id'].apply(lambda r: os.path.join(mainpath_train, r[0], r[1], r[2], r + '.jpg'))\n\nlandmark_unique = traindf['landmark_id'].unique()    #Clases totales del dataset (monumentos diferentes)\nn_landmark = len(landmark_unique)                    #Es el número de neuronas de la última capa de la red neuronal convolucional\n\ntraindf.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Información numérica del dataset de entrenamiento","metadata":{}},{"cell_type":"code","source":"print('Datos del dataset de entrenamiento \\n')\nprint('Número de imágenes en el dataset a clasificar: ', traindf.shape[0])\nprint('Número de monumentos (clases) diferentes: ', n_landmark)\nprint('Repeticiones de elementos por clase: Mínimo', min(traindf['landmark_id'].value_counts()),\n      'y Máximo',max(traindf['landmark_id'].value_counts()))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Contenido del dataset de entrenamiento: primeras imágenes","metadata":{}},{"cell_type":"code","source":"# Mostrar los primeros monumentos del dataset\n\ntam = 256\n\nplt.figure(figsize=(25,7))\n\nfor i in range(12):\n    random_img = plt.imread(traindf['img_path'][i])\n    random_img = cv2.resize(random_img,(tam,tam))      #Para que tengan el mismo tamaño (= número de píxeles = neuronas de entrada)\n    plt.subplot(2 , 6, i+1)\n    plt.xticks([])\n    plt.yticks([])\n    plt.xlabel('Clase: '+ str(traindf['landmark_id'][i]))\n    plt.imshow(random_img)\n    \nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Contenido del dataset de entrenamiento: imágenes aleatorias","metadata":{}},{"cell_type":"code","source":"# Mostrar algunos monumentos aleatorios del dataset\n\nplt.figure(figsize=(25,7))\n\nfor i in range(12):\n    random_row = np.random.randint(0, traindf.shape[0])\n    random_img = plt.imread(traindf['img_path'][random_row])\n    random_img = cv2.resize(random_img,(tam,tam))\n    plt.subplot(2 , 6, i+1)\n    plt.xticks([])\n    plt.yticks([])\n    plt.xlabel('Clase: '+ str(traindf['landmark_id'][random_row]))\n    plt.imshow(random_img)\n    \nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Selección de la cantidad de datos a emplear del dataset de entrenamiento","metadata":{}},{"cell_type":"code","source":"#Muestra del dataframe original (para agilizar las pruebas)\n\nN_DATOS = 1000\n\ntraindf_s = traindf.iloc[:N_DATOS,:]\n\n#unique_classes = len(traindf_s['landmark_id'].unique())          #Número de monumentos distintos dentro de la muestra que se empleará\n\ntraindf = None     #Eliminar el dataframe que no se usará más para liberar memoria\n\ntraindf_s","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Redimensionamiento de las imágenes del dataset de entrenamiento. Almacenamiento de las imágenes en una variable y de las etiquetas en otra","metadata":{}},{"cell_type":"code","source":"IMG_SIZE = 256\n\ndatos_entrenamiento = []\n\ndef img_resize(img_path):             #Función para redimensionar las imágenes\n    img = plt.imread(img_path)\n    img_redim = cv2.resize(img,(IMG_SIZE,IMG_SIZE))\n    return img_redim\n\n#X = []   #Imágenes\n#y = []   #Clases\ndatos_entrenamiento=[]\n\nfor i in range(traindf_s.shape[0]):                        #Relleno de una lista con imágenes redimensionadas y otra con sus clases\n    redim = img_resize(traindf_s['img_path'][i])\n    aux = np.array(traindf_s['landmark_id'][i])\n    #X.append(img_resize(traindf_s['img_path'][i])) \n    #y.append(np.array(traindf_s['landmark_id'][i]))      #Cada etiqueta se convierte en numpy.ndarray (por compatibilidad con las imágenes en X, que son ndarrays)\n    datos_entrenamiento.append([redim,tf.convert_to_tensor(aux, dtype=tf.int64, dtype_hint=None, name=None)])    #Tensor\n        \n#df_train = pd.DataFrame(datos_entrenamiento, columns = ['img','landmark_id'])        #Convertimos lista a pd.DataFrame\n                          \n#print('X: ', type(X))\n#print('Elementos de X: ', type(X[0]))\n#print('\\ny: ', type(y))\n#print('Elementos de y: ', type(y[0]))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"datos_entrenamiento[0]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Conversión de lista a array necesaria\n\nX=[]\ny=[]\n\nfor img,clase in datos_entrenamiento:\n    X.append(img)\n    y.append(clase)\n\nprint('X: ', type(X))\nprint('Elementos de X: ', type(X[0]))\nprint('\\ny: ', type(y))\nprint('Elementos de y: ', type(y[0]))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Normalización de las imágenes (Comprobado: aumenta la velocidad del programa x6)","metadata":{}},{"cell_type":"code","source":"X = np.array(X).astype(float)/255       #Normalizamos las imágenes para que los píxeles estén entre [0,1] en vez de [0,255] --------> Mayor velocidad de ejecución\nX.shape","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X[0]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y=np.array(y)\ny","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Obtención de los datos de validación a partir del dataset de entrenamiento","metadata":{}},{"cell_type":"code","source":"#Separación de los datos en datos para entrenamiento y datos para validación\n\nX_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.3, random_state=0)  #Mismo tipo que X e y (ndarray formado por ndarrays o int64, respectivamente)\n\nprint('Imágenes en X: ',len(X), ' y etiquetas en y: ', len(y))\nprint('Entrenamiento. Imágenes en X_train: ',len(X_train), 'y etiquetas en y_train: ',len(y_train))\nprint('Validación. Imágenes en X_val: ',len(X_val), 'y etiquetas en y_val: ',len(y_val))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"&nbsp;\n&nbsp;\n&nbsp;\n\n# **DATASET DE TESTEO**","metadata":{}},{"cell_type":"markdown","source":"### Carga del dataset de testeo","metadata":{}},{"cell_type":"markdown","source":"'''\nmainpath_test = '../input/landmark-recognition-2021/test'\n\nds_test = image_dataset_from_directory(\n    mainpath_test,\n    labels=None,       #Puesto que las etiquetas son desconocidas (para confirmar el acierto de la predicción se imprimirá otra imagen de la clase supuesta)\n    label_mode='int',              #Porque se utilizará la función de pérdida loss='sparse_categorical_crossentropy'\n    image_size=[IMG_SIZE, IMG_SIZE],      #Redimensionamiento al mismo tamaño que las imágenes de entrenamiento\n    interpolation='bilinear',\n    batch_size=1,\n    shuffle=True,      #Para que salgan en orden aleatorio\n)\n'''","metadata":{"execution":{"iopub.status.busy":"2022-04-18T22:17:45.070842Z","iopub.execute_input":"2022-04-18T22:17:45.071302Z","iopub.status.idle":"2022-04-18T22:17:45.079974Z","shell.execute_reply.started":"2022-04-18T22:17:45.071237Z","shell.execute_reply":"2022-04-18T22:17:45.078903Z"}}},{"cell_type":"markdown","source":"'''\ndef convert_to_int(image):\n    image = tf.image.convert_image_dtype(image, dtype=tf.int64)\n    return image\n\nds_test.map()\ntype(ds_test)\n'''","metadata":{"execution":{"iopub.status.busy":"2022-04-18T22:17:45.081805Z","iopub.execute_input":"2022-04-18T22:17:45.082206Z","iopub.status.idle":"2022-04-18T22:17:45.090863Z","shell.execute_reply.started":"2022-04-18T22:17:45.082166Z","shell.execute_reply":"2022-04-18T22:17:45.0901Z"}}},{"cell_type":"markdown","source":"&nbsp;\n&nbsp;\n&nbsp;\n\n# **HIPERPARÁMETROS DEL MODELO**","metadata":{}},{"cell_type":"code","source":"#Parámetros de la red neuronal convolucional\n\nkernelSize = (3,3)            #Tamaño de la plantilla de convolución\npaddingType = 'same'          #Cómo se procede en los bordes de las imágenes ('same' o 'valid')\nactivationF = 'relu'          #Función de activación\npoolSize = (2,2)              #Tamaño de la plantilla de maximum pooling\nstridesSize = (2,2)           #Desplazamiento de plantilla durante maximum pooling\ndropoutRate = 0.5             #Porcentaje de neuronas que se desactivan con la capa Dropout\n#batchSize (tomará valores en un bucle)   #Cantidad de datos con los que se entrena en cada época (tomará valores en un bucle)\nepochsSize= 100                 #Número de épocas en las que se entrena","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"&nbsp;\n&nbsp;\n&nbsp;\n\n# **FUNCIÓN PARA CREACIÓN, COMPILACIÓN Y ENTRENAMIENTO DEL MODELO**","metadata":{}},{"cell_type":"code","source":"def crear_modelo(kernelSize, paddingType, activationF, IMG_SIZE, poolSize, stridesSize, dropoutRate, X_train, y_train, X_val, y_val, batchSize, epochsSize):\n    \n    #Creación del modelo\n\n    model = keras.Sequential([\n        \n        #DATA AUGMENTATION\n        preprocessing.RandomFlip('horizontal_and_vertical'),\n        preprocessing.RandomZoom(height_factor=(-0.2, 0.0)),\n        preprocessing.RandomContrast(0.5),\n        ##preprocessing.RandomTranslation(height_factor= (-0.2, 0.2), width_factor=(-0.2, 0.2)),       #Empeoran los resultados\n        ##preprocessing.RandomRotation(factor= (-0.3, 0.3)),\n        \n        \n        #BASE DEL MODELO\n\n        #Bloque convolucional 1  \n        layers.Conv2D(filters=32, kernel_size=kernelSize, strides=1, padding=paddingType, activation=activationF, input_shape=[IMG_SIZE, IMG_SIZE, 3]),\n        layers.MaxPool2D(pool_size=poolSize, strides=stridesSize, padding=paddingType),\n\n        #Bloque convolucional 2  \n        layers.Conv2D(filters=64, kernel_size=kernelSize, strides=1, padding=paddingType, activation=activationF),\n        layers.MaxPool2D(pool_size=poolSize, strides=stridesSize, padding=paddingType),\n\n        #Bloque convolucional 3  \n        layers.Conv2D(filters=128, kernel_size=kernelSize, strides=1, padding=paddingType, activation=activationF),\n        layers.MaxPool2D(pool_size=poolSize, strides=stridesSize, padding=paddingType),\n\n        #Bloque convolucional 4  \n        layers.Conv2D(filters=256, kernel_size=kernelSize, strides=1, padding=paddingType, activation=activationF),\n        layers.MaxPool2D(pool_size=poolSize, strides=stridesSize, padding=paddingType),\n\n        #CABEZA DEL MODELO\n\n        layers.Flatten(),\n        layers.BatchNormalization(),\n        layers.Dense(units = 2048, activation=activationF),\n        layers.Dropout(rate=dropoutRate),\n\n        layers.BatchNormalization(),\n        layers.Dense(units = n_landmark, activation='softmax'),         #Número de neuronas = Número de clases en los datos       \n    ])\n    \n    #Compilación del modelo   \n    \n    model.compile(\n        optimizer=tf.keras.optimizers.Adam(epsilon=0.01),\n        loss='sparse_categorical_crossentropy',\n        metrics=['sparse_categorical_accuracy']\n    )\n    \n    early_stopping = EarlyStopping(\n        min_delta=0.001, # minimium amount of change to count as an improvement\n        patience=15, # how many epochs to wait before stopping\n        restore_best_weights=True,\n    )\n    \n    #Entrenamiento del modelo\n    \n    history = model.fit(\n        X_train, y_train,\n        validation_data= (X_val, y_val),\n        batch_size=batchSize,\n        epochs=epochsSize,\n        #callbacks=[early_stopping],\n        verbose=1,            # 0: silencio     1: barra de progreso + texto      2: solo texto\n    )\n    \n    return model, history","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"&nbsp;\n&nbsp;\n&nbsp;\n\n# **GRÁFICAS DE PÉRDIDA Y PRECISIÓN Y EVALUACIÓN DEL MODELO (SEGÚN BATCHSIZE)**","metadata":{}},{"cell_type":"code","source":"batchSize = [2, 4, 8, 16, 32, 64, 128]\n\nmin_val_loss = []\nmax_val_acc = []\n\nfor j in range(len(batchSize)):           #En cada iteración se crea el modelo DESDE CERO con la función crear_modelo()\n    \n    model, history = crear_modelo(kernelSize, paddingType, activationF, IMG_SIZE, poolSize, stridesSize, dropoutRate, X_train, y_train, X_val, y_val, batchSize[j], epochsSize)\n    \n    history_df = pd.DataFrame(history.history)    \n    \n    print('\\nbatchSize = '+str(batchSize[j]))\n    \n    plt.figure(figsize=(15,5))        #Anchura y Altura de las gráficas, respectivamente\n    \n    plt.subplot(1,2,1)\n    #history_df.loc[0:, ['loss', 'val_loss']].plot()             #Utilizar dataframe y su método plot dan problemas con plt.subplot\n    plt.plot(history.history['loss'], label='train')        \n    plt.plot(history.history['val_loss'], label='test')\n    plt.title('Loss and Validation Loss')\n    plt.xlabel('batchSize = '+str(batchSize[j]))\n    print((\"Minimum Validation Loss: {:0.4f} in epoch {:0.0f} \").format(history_df['val_loss'].min(), history_df['val_loss'].idxmin()))            \n    \n    plt.subplot(1,2,2)\n    #history_df.loc[0:, ['accuracy', 'val_accuracy']].plot()\n    plt.plot(history.history['sparse_categorical_accuracy'], label='train')\n    plt.plot(history.history['val_sparse_categorical_accuracy'], label='test')\n    plt.title('Accuracy and Validation Accuracy')\n    plt.xlabel('batchSize = '+str(batchSize[j]))\n    print((\"Maximum Validation Accuracy: {:0.4f} in epoch {:0.0f} \").format(history_df['val_sparse_categorical_accuracy'].max(), history_df['val_sparse_categorical_accuracy'].idxmax()))\n\n    print(\"\\nEvaluación del modelo con datos de entrenamiento\")\n    score = model.evaluate(X_train, y_train)\n    print(\"Test loss, Test accuracy:\", score[0], score[1])\n\n    print(\"\\nEvaluación del modelo con datos de validación\")\n    score = model.evaluate(X_val, y_val)\n    print(\"Test loss, Test accuracy:\", score[0], score[1])\n\n    #print(\"\\nEvaluación del modelo con datos de testeo\")\n    #score = model.evaluate(X_test, y_test)\n    #print(\"Test loss, Test accuracy:\", score[0], score[1],'\\n')\n\n    plt.show()    #Se muestran las gráficas\n    \n    #Proceso para almacenar los mejores resultados (menor pérdida y mayor precisión)\n    if j==0:\n        min_val_loss= [history_df['val_loss'].min(), history_df['val_loss'].idxmin(), batchSize[j]]\n    else:\n        if min_val_loss[0]> history_df['val_loss'].min():\n            min_val_loss= [history_df['val_loss'].min(), history_df['val_loss'].idxmin(), batchSize[j]]\n            \n    if j==0:\n        max_val_acc= [history_df['val_sparse_categorical_accuracy'].max(), history_df['val_sparse_categorical_accuracy'].idxmax(), batchSize[j]]\n    else:\n        if max_val_acc[0]< history_df['val_sparse_categorical_accuracy'].max():\n            max_val_acc= [history_df['val_sparse_categorical_accuracy'].max(), history_df['val_sparse_categorical_accuracy'].idxmax(), batchSize[j]]\n            \n            \nprint('\\nBest Results:')            \nprint((\"Minimum Validation Loss: {:0.4f} in epoch {:0.0f} with batchSize = {:0.0f}\").format(min_val_loss[0], min_val_loss[1], min_val_loss[2]))\nprint((\"Maximum Validation Accuracy: {:0.4f} in epoch {:0.0f} with batchSize = {:0.0f}\").format(max_val_acc[0], max_val_acc[1], max_val_acc[2]))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"&nbsp;\n&nbsp;\n&nbsp;\n\n# **Análisis de funcionamiento con distintos parámetros**","metadata":{}},{"cell_type":"markdown","source":"## Prueba #1: Sin Data Augmentation y con Early Stopping. \n\n**ERRÓNEA: EL MODELO NO SE CREA DESDE CERO EN CADA ITERACIÓN (SE ENTRENA MÁS DE UNA VEZ EL MISMO MODELO PERO CON DIFERENTES BATCH SIZE**\n\n* 1000 primeras imágenes del dataset (30% para validación)\n* kernelSize = (3,3)           \n* paddingType = 'same'          \n* activationF = 'relu'          \n* poolSize = (2,2)              \n* stridesSize = (2,2)           \n* dropoutRate = 0.5            \n* batchSize = [2, 4, 8, 16, 32, 64, 128, 256]                 \n* epochsSize = 100\n* Callback/earlystopping: min_delta=0.001, patience=15\n\nloss='sparse_categorical_crossentropy',\nmetrics=['accuracy']\n\n### Ejecución 1: units=256 última capa\n**Best Results:**\n* **Minimum Validation Loss: 3.4266 in epoch 0 with batchSize = 64**\n* **Maximum Validation Accuracy: 0.5767 in epoch 11 with batchSize = 64**\n\n### Ejecución 2: units=256 última capa\n**Best Results:**\n* **Minimum Validation Loss: 3.2840 in epoch 5 with batchSize = 32**\n* **Maximum Validation Accuracy: 0.5833 in epoch 16 with batchSize = 32**\n\n### Ejecución 2: units=n_landmark (81313) última capa\n**Best Results:**\n* **Minimum Validation Loss: 3.4750 in epoch 2 with batchSize = 16**\n* **Maximum Validation Accuracy: 0.5733 in epoch 15 with batchSize = 16**","metadata":{}},{"cell_type":"markdown","source":"&nbsp;\n&nbsp;\n&nbsp;\n\n## **Prueba #2: Sin Data Augmentation y sin Early Stopping. Creación desde cero del modelo en cada iteración.**\n\n* IMG_SIZE = 256\n* **1000 primeras imágenes del dataset (30% para validación)**\n* kernelSize = (3,3)           \n* paddingType = 'same'          \n* activationF = 'relu'          \n* poolSize = (2,2)              \n* stridesSize = (2,2)           \n* dropoutRate = 0.5            \n* batchSize = [2, 4, 8, 16, 32, 64, 128]                 \n* **epochsSize = 50** \n* No Callback/earlystopping: min_delta=0.001, patience=15\n\nloss='sparse_categorical_crossentropy', metrics=['accuracy']\n\n&nbsp;\n\n### Ejecución 1: units = n_landmark (81313) en la última capa densa\n**Además de haber un claro underfitting (no se llega ni al 80% de precisión con los datos de entrenamiento), salta un error en la GPU al empezar el proceso con batchSize=16**\n\n2022-04-17 22:18:53.870007: W tensorflow/core/common_runtime/bfc_allocator.cc:457] Allocator (GPU_0_bfc) ran out of memory trying to allocate 635.26MiB (rounded to 666116096)requested by op Fill\nIf the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation.    ...\n\n&nbsp;\n\n### Ejecución 2: units = 256 en la última capa densa\n**Best Results:**\n* **Minimum Validation Loss: 1.5969 in epoch 45 with batchSize = 64 (Con: Maximum Validation Accuracy: 0.6600 in epoch 49)**\n* **Maximum Validation Accuracy: 0.6633 in epoch 45 with batchSize = 32 (Con: Minimum Validation Loss: 1.7123 in epoch 46 )** \n\n&nbsp;\nSe ha obtenido el mejor resultado hasta el momento\n\n&nbsp;\n\n### Ejecución 3: units = 256 en la última capa densa + Nueva capa BatchNormalization antes de la última capa densa\n**Best Results:**\n* **Minimum Validation Loss: 2.1349 in epoch 37 with batchSize = 128**\n* **Maximum Validation Accuracy: 0.6400 in epoch 49 with batchSize = 64**","metadata":{}},{"cell_type":"markdown","source":"&nbsp;\n&nbsp;\n&nbsp;\n\n## **Prueba #3: CON Data Augmentation y sin Early Stopping. Creación desde cero del modelo en cada iteración.**\n\n* IMG_SIZE = 256\n* **1000 primeras imágenes del dataset (30% para validación)**\n* kernelSize = (3,3)           \n* paddingType = 'same'          \n* activationF = 'relu'          \n* poolSize = (2,2)              \n* stridesSize = (2,2)           \n* dropoutRate = 0.5            \n* batchSize = [2, 4, 8, 16, 32, 64, 128]                 \n* **epochsSize = 50** \n* No Callback/earlystopping: min_delta=0.001, patience=15\n\nloss='sparse_categorical_crossentropy', metrics=['accuracy']  \n&nbsp;  \n\npreprocessing.RandomFlip('horizontal_and_vertical'),  \npreprocessing.RandomZoom(height_factor=(-0.2, 0)),  \npreprocessing.RandomContrast(0.5),  \n\n*(Las demás formas de aumento de datos solo empeoran la predicción y el entrenamiento pues se aprenden patrones erróneos)*\n\n&nbsp;\n\n### Ejecución 1: units = 256 última capa densa. batchsSize = 64  \nbatchSize = 64  \nMinimum Validation Loss: 1.7064 in epoch 49   \nMaximum Validation Accuracy: 0.6333 in epoch 49  \n\nEvaluación del modelo con datos de entrenamiento  \nTest loss, Test accuracy: 0.21607555449008942 &nbsp; 0.977142870426178\n\nEvaluación del modelo con datos de validación  \nTest loss, Test accuracy: 1.7064040899276733 &nbsp; 0.6333333253860474\n\n![download.png](attachment:ec96dd57-889d-4c1c-b5af-aa481040a1da.png) \n\n**Comentarios:** Se puede observar cierta tendencia al alza en la precisión del modelo, que quizás pueda mejorarse entrenando más épocas que las 50 actuales.\n\n&nbsp;\n\n### Ejecución 2: units = 256 última capa densa. batchsSize = 64  epochsSize = 100\nbatchSize = 64  \nMinimum Validation Loss: 1.7399 in epoch 53   \nMaximum Validation Accuracy: 0.6667 in epoch 73   \n\nEvaluación del modelo con datos de entrenamiento  \nTest loss, Test accuracy: 0.0065935407765209675 &nbsp; 1.0\n\nEvaluación del modelo con datos de validación  \nTest loss, Test accuracy: 2.112797498703003 &nbsp; 0.6499999761581421  \n\n![download.png](attachment:3daef69f-71e5-4311-8554-97dc3ad711b2.png)  \n\n**Comentarios:** Efectivamente, al aumentar las épocas se ha conseguido mejorar la precisión hasta un 66.7%, aunque la pérdida se ha elevado\n\n&nbsp;\n\n### Ejecución 3: units = 256 última capa densa. / batchsSize = [2, 4, 8, 16, 32, 64, 128] / epochsSize = 100\nBest Results:  \nMinimum Validation Loss: 1.6887 in epoch 26 with batchSize = 32  \n**Maximum Validation Accuracy: 0.6900 in epoch 44 with batchSize = 16  ----> Mejor precisión hasta el momento**\n\n&nbsp;\n\n### Ejecución 4: units = 256 última capa densa. / batchsSize = 16 / epochsSize = 100  \n*Esta ejecución se realiza para comprobar el carácter no determinista del proceso pues está basado en el método iterativo Stochastic Gradient Descent*\n\nbatchSize = 16  \nMinimum Validation Loss: 1.9392 in epoch 53   \nMaximum Validation Accuracy: 0.6733 in epoch 78   \n\nEvaluación del modelo con datos de entrenamiento  \nTest loss, Test accuracy: 0.072989322245121 &nbsp; 0.9757142663002014  \n\nEvaluación del modelo con datos de validación  \nTest loss, Test accuracy: 2.500619888305664 &nbsp; 0.6466666460037231  \n\n![download.png](attachment:435ddefb-f78c-48a0-9f42-d614cf633be0.png)\n\n**Comentarios:** No se llega al 69% de precisión conseguido en la ejecución anterior con los mismos hiperparámetros\n\n&nbsp;\n\n### Ejecución 5: units = 256 última capa densa. / batchsSize = 64 / epochsSize = 100 / metrics=['sparse_categorical_accuracy']  \n*Se ha cambiado la métrica 'accuracy' por 'sparse_categorical_accuracy'*\n\nbatchSize = 64  \nMinimum Validation Loss: 1.7709 in epoch 47   \nMaximum Validation Accuracy: **0.6833 in epoch 80**   \n\nEvaluación del modelo con datos de entrenamiento  \nTest loss, Test accuracy: 0.017007440328598022 0.9971428513526917   \n\nEvaluación del modelo con datos de validación  \nTest loss, Test accuracy: 2.0611343383789062 0.6399999856948853  \n\n![download.png](attachment:cd59a73f-ebc8-4627-98e9-fcbb45dff91f.png)  \n\n**Comentarios:** Se obtienen mejores resultados que empleando esta nueva métrica (comparar con Ejecución 2)\n\n&nbsp;\n\n### Ejecución 6: units = n_landmark (81313) última capa densa. / batchsSize = 64 / epochsSize = 100 / metrics=['sparse_categorical_accuracy']  \n\n*Aparece: 'Your notebook tried to allocate more memory than is available. It has restarted.', pero se ha conseguido visualizar:*\n\nbatchSize = 64\nMinimum Validation Loss: 2.1983 in epoch 61 \nMaximum Validation Accuracy: **0.6900 in epoch 83**  \n\n\n**Comentarios:** Es necesario liberar memoria. Para ello los datos no usados serán eliminados (usando traindf = None).\n\nVolvemosa repetir tras la implementación de esta línea:\n\nbatchSize = 64\nMinimum Validation Loss: 2.1569 in epoch 52 \nMaximum Validation Accuracy: 0.6800 in epoch 69 \n\n**Comentarios:** El programa se ejecuta sin problemas","metadata":{},"attachments":{"ec96dd57-889d-4c1c-b5af-aa481040a1da.png":{"image/png":"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"},"3daef69f-71e5-4311-8554-97dc3ad711b2.png":{"image/png":"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"},"435ddefb-f78c-48a0-9f42-d614cf633be0.png":{"image/png":"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"},"cd59a73f-ebc8-4627-98e9-fcbb45dff91f.png":{"image/png":"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"}}}]}