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diff --git a/code/autoencoder_functions.py b/code/autoencoder_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f2b21926e08515c1befd0649718ca9e145aa940
--- /dev/null
+++ b/code/autoencoder_functions.py
@@ -0,0 +1,660 @@
+# ------------------------------------------------------------------------------------------------------------------- #
+
+from tensorflow.keras.layers import Input, Dropout, Conv2D, Conv2DTranspose, BatchNormalization, Flatten, Activation, Reshape, LeakyReLU
+from tensorflow.keras.models import Model
+from tensorflow.keras.optimizers import Adam
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
+def make_layered_autoencoder(input_shape, feature_multiplier, surface_kernel_size, kernel_size, dropout, init_func):
+
+ layer_conv = []
+
+ ### Conv 1 ###
+
+ # Input #
+
+ conv1_input_shape = input_shape
+
+ conv1_input = Input(shape = conv1_input_shape)
+
+ x = conv1_input
+
+ # Layer 0 #
+
+ x = Conv2D(filters = feature_multiplier * 1,
+ kernel_size = surface_kernel_size,
+ strides = 1,
+ padding = "same",
+ kernel_initializer = init_func)(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+
+ # Layer 1 #
+
+ x = Conv2D(filters = feature_multiplier * 1,
+ kernel_size = surface_kernel_size,
+ strides = 1,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+
+ # Layer 2 #
+
+ x = Conv2D(filters = feature_multiplier * 1,
+ kernel_size = surface_kernel_size,
+ strides = 2,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+ x = Dropout(dropout)(x) if dropout > 0.0 else x
+
+ # Output #
+
+ conv1_output = x
+
+ convolution_1 = Model(conv1_input, conv1_output)
+ layer_conv.append(convolution_1)
+
+ conv1_output_shape = (convolution_1.output_shape[1],
+ convolution_1.output_shape[2],
+ convolution_1.output_shape[3])
+
+ ### Conv 2 ###
+
+ # Input #
+
+ conv2_input_shape = conv1_output_shape
+
+ conv2_input = Input(shape = conv2_input_shape)
+
+ x = conv2_input
+
+ # Layer 0 #
+
+ x = Conv2D(filters = feature_multiplier * 2,
+ kernel_size = kernel_size,
+ strides = 1,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+
+ # Layer 1 #
+
+ x = Conv2D(filters = feature_multiplier * 2,
+ kernel_size = kernel_size,
+ strides = 2,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+ x = Dropout(dropout)(x) if dropout > 0.0 else x
+
+ # Output #
+
+ conv2_output = x
+
+ convolution_2 = Model(conv2_input, conv2_output)
+ layer_conv.append(convolution_2)
+
+ conv2_output_shape = (convolution_2.output_shape[1],
+ convolution_2.output_shape[2],
+ convolution_2.output_shape[3])
+
+ ### Conv 3 ###
+
+ # Input #
+
+ conv3_input_shape = conv2_output_shape
+
+ conv3_input = Input(shape = conv3_input_shape)
+
+ x = conv3_input
+
+ # Layer 0 #
+
+ x = Conv2D(filters = feature_multiplier * 4,
+ kernel_size = kernel_size,
+ strides = 1,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+
+ # Layer 1 #
+
+ x = Conv2D(filters = feature_multiplier * 4,
+ kernel_size = kernel_size,
+ strides = 2,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+ x = Dropout(dropout)(x) if dropout > 0.0 else x
+
+ # Output #
+
+ conv3_output = x
+
+ convolution_3 = Model(conv3_input, conv3_output)
+ layer_conv.append(convolution_3)
+
+ conv3_output_shape = (convolution_3.output_shape[1],
+ convolution_3.output_shape[2],
+ convolution_3.output_shape[3])
+
+ ### Conv 4 ###
+
+ # Input #
+
+ conv4_input_shape = conv3_output_shape
+
+ conv4_input = Input(shape = conv4_input_shape)
+
+ x = conv4_input
+
+ # Layer 0 #
+
+ x = Conv2D(filters = feature_multiplier * 8,
+ kernel_size = kernel_size,
+ strides = 1,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+
+ # Layer 1 #
+
+ x = Conv2D(filters = feature_multiplier * 8,
+ kernel_size = kernel_size,
+ strides = 2,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+ x = Dropout(dropout)(x) if dropout > 0.0 else x
+
+ # Output #
+
+ conv4_output = x
+
+ convolution_4 = Model(conv4_input, conv4_output)
+ layer_conv.append(convolution_4)
+
+ conv4_output_shape = (convolution_4.output_shape[1],
+ convolution_4.output_shape[2],
+ convolution_4.output_shape[3])
+
+ ### Conv 5 ###
+
+ # Input #
+
+ conv5_input_shape = conv4_output_shape
+
+ conv5_input = Input(shape = conv5_input_shape)
+
+ x = conv5_input
+
+ # Layer 0 #
+
+ x = Conv2D(filters = feature_multiplier * 16,
+ kernel_size = kernel_size,
+ strides = 2,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+ x = Dropout(dropout)(x) if dropout > 0.0 else x
+
+ # Output #
+
+ conv5_output = x
+
+ convolution_5 = Model(conv5_input, conv5_output)
+ layer_conv.append(convolution_5)
+
+ conv5_output_shape = (convolution_5.output_shape[1],
+ convolution_5.output_shape[2],
+ convolution_5.output_shape[3])
+
+ ### Conv 6 ###
+
+ # Input #
+
+ conv6_input_shape = conv5_output_shape
+
+ conv6_input = Input(shape = conv6_input_shape)
+
+ x = conv6_input
+
+ # Layer 0 #
+
+ x = Conv2D(filters = feature_multiplier * 32,
+ kernel_size = kernel_size,
+ strides = 2,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+ x = Dropout(dropout)(x) if dropout > 0.0 else x
+
+ # Output #
+
+ conv6_output = x
+
+ convolution_6 = Model(conv6_input, conv6_output)
+ layer_conv.append(convolution_6)
+
+ conv6_output_shape = (convolution_6.output_shape[1],
+ convolution_6.output_shape[2],
+ convolution_6.output_shape[3])
+
+ ### ------------------------------------------------ ###
+
+ layer_deconv = []
+
+ ### Deconv 6 ###
+
+ # Input #
+
+ deconv6_input_shape = conv6_output_shape
+
+ deconv6_input = Input(shape = deconv6_input_shape)
+
+ x = deconv6_input
+
+ # Layer 0 #
+
+ x = Conv2DTranspose(filters = feature_multiplier * 16,
+ kernel_size = kernel_size,
+ strides = 2,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+ x = Dropout(dropout)(x) if dropout > 0.0 else x
+
+ # Output #
+
+ deconv6_output = x
+
+ deconvolution_6 = Model(deconv6_input, deconv6_output)
+ layer_deconv.append(deconvolution_6)
+
+ ### Deconv 5 ###
+
+ # Input #
+
+ deconv5_input_shape = conv5_output_shape
+
+ deconv5_input = Input(shape = deconv5_input_shape)
+
+ x = deconv5_input
+
+ # Layer 0 #
+
+ x = Conv2DTranspose(filters = feature_multiplier * 8,
+ kernel_size = kernel_size,
+ strides = 2,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+
+ # Layer 1 #
+
+ x = Conv2DTranspose(filters = feature_multiplier * 8,
+ kernel_size = kernel_size,
+ strides = 1,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+ x = Dropout(dropout)(x) if dropout > 0.0 else x
+
+ # Output #
+
+ deconv5_output = x
+
+ deconvolution_5 = Model(deconv5_input, deconv5_output)
+ layer_deconv.append(deconvolution_5)
+
+ ### Deconv 4 ###
+
+ # Input #
+
+ deconv4_input_shape = conv4_output_shape
+
+ deconv4_input = Input(shape = deconv4_input_shape)
+
+ x = deconv4_input
+
+ # Layer 0 #
+
+ x = Conv2DTranspose(filters = feature_multiplier * 4,
+ kernel_size = kernel_size,
+ strides = 2,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+
+ # Layer 1 #
+
+ x = Conv2DTranspose(filters = feature_multiplier * 4,
+ kernel_size = kernel_size,
+ strides = 1,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+ x = Dropout(dropout)(x) if dropout > 0.0 else x
+
+ # Output #
+
+ deconv4_output = x
+
+ deconvolution_4 = Model(deconv4_input, deconv4_output)
+ layer_deconv.append(deconvolution_4)
+
+ ### Deconv 3 ###
+
+ # Input #
+
+ deconv3_input_shape = conv3_output_shape
+
+ deconv3_input = Input(shape = deconv3_input_shape)
+
+ x = deconv3_input
+
+ # Layer 0 #
+
+ x = Conv2DTranspose(filters = feature_multiplier * 2,
+ kernel_size = kernel_size,
+ strides = 2,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+
+ # Layer 1 #
+
+ x = Conv2DTranspose(filters = feature_multiplier * 2,
+ kernel_size = kernel_size,
+ strides = 1,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+ x = Dropout(dropout)(x) if dropout > 0.0 else x
+
+ # Output #
+
+ deconv3_output = x
+
+ deconvolution_3 = Model(deconv3_input, deconv3_output)
+ layer_deconv.append(deconvolution_3)
+
+ ### Deconv 2 ###
+
+ # Input #
+
+ deconv2_input_shape = conv2_output_shape
+
+ deconv2_input = Input(shape = deconv2_input_shape)
+
+ x = deconv2_input
+
+ # Layer 0 #
+
+ x = Conv2DTranspose(filters = feature_multiplier * 1,
+ kernel_size = kernel_size,
+ strides = 2,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+
+ # Layer 1 #
+
+ x = Conv2DTranspose(filters = feature_multiplier * 1,
+ kernel_size = kernel_size,
+ strides = 1,
+ kernel_initializer = init_func,
+ padding = "same")(x)
+ x = LeakyReLU(alpha = 0.2)(x)
+ x = BatchNormalization()(x)
+ x = Dropout(dropout)(x) if dropout > 0.0 else x
+
+ # Output #
+
+ deconv2_output = x
+
+ deconvolution_2 = Model(deconv2_input, deconv2_output)
+ layer_deconv.append(deconvolution_2)
+
+ ### Deconv 1 ###
+
+ # Input #
+
+ deconv1_input_shape = conv1_output_shape
+
+ deconv1_input = Input(shape = deconv1_input_shape)
+
+ x = deconv1_input
+
+ # Layer 0 #
+
+ x = Conv2DTranspose(input_shape[-1],
+ kernel_size = surface_kernel_size,
+ strides = 2,
+ padding = "same",
+ kernel_initializer = init_func)(x)
+ x = Activation("linear")(x)
+
+ # Output #
+
+ deconv1_output = x
+
+ deconvolution_1 = Model(deconv1_input, deconv1_output)
+ layer_deconv.append(deconvolution_1)
+
+ layer_deconv.reverse()
+
+ return layer_conv, layer_deconv
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
+def make_adam(adam_learning_rate = 0.00015, adam_epsilon = 1e-08, adam_lr_decay = 1e-05):
+
+ optimizer = Adam(lr = adam_learning_rate,
+ epsilon = adam_epsilon,
+ decay = adam_lr_decay)
+
+ return optimizer
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
+def make_staged_autoencoder(input_shape, layer_conv, layer_deconv, optimizer):
+
+ stages = []
+
+ ### Etapa 1 ###
+
+ stage_input = Input(shape = input_shape)
+
+ x = stage_input
+
+ x = layer_conv[0](x)
+
+ x = layer_deconv[0](x)
+
+ stage_output = x
+
+ stage_1 = Model(inputs = stage_input, outputs = stage_output)
+ stage_1.compile(optimizer = optimizer, loss = "mse", metrics = ["mae"])
+ stages.append(stage_1)
+
+ ### Etapa 2 ###
+
+ stage_input = Input(shape = input_shape)
+
+ x = stage_input
+
+ x = layer_conv[0](x)
+ x = layer_conv[1](x)
+
+ x = layer_deconv[1](x)
+ x = layer_deconv[0](x)
+
+ stage_output = x
+
+ stage_2 = Model(inputs = stage_input, outputs = stage_output)
+ stage_2.compile(optimizer = optimizer, loss = "mse", metrics = ["mae"])
+ stages.append(stage_2)
+
+ ### Etapa 3 ###
+
+ stage_input = Input(shape = input_shape)
+
+ x = stage_input
+
+ x = layer_conv[0](x)
+ x = layer_conv[1](x)
+ x = layer_conv[2](x)
+
+ x = layer_deconv[2](x)
+ x = layer_deconv[1](x)
+ x = layer_deconv[0](x)
+
+ stage_output = x
+
+ stage_3 = Model(inputs = stage_input, outputs = stage_output)
+ stage_3.compile(optimizer = optimizer, loss = "mse", metrics = ["mae"])
+ stages.append(stage_3)
+
+ ### Etapa 4 ###
+
+ stage_input = Input(shape = input_shape)
+
+ x = stage_input
+
+ x = layer_conv[0](x)
+ x = layer_conv[1](x)
+ x = layer_conv[2](x)
+ x = layer_conv[3](x)
+
+ x = layer_deconv[3](x)
+ x = layer_deconv[2](x)
+ x = layer_deconv[1](x)
+ x = layer_deconv[0](x)
+
+ stage_output = x
+
+ stage_4 = Model(inputs = stage_input, outputs = stage_output)
+ stage_4.compile(optimizer = optimizer, loss = "mse", metrics = ["mae"])
+ stages.append(stage_4)
+
+ ### Etapa 5 ###
+
+ stage_input = Input(shape = input_shape)
+
+ x = stage_input
+
+ x = layer_conv[0](x)
+ x = layer_conv[1](x)
+ x = layer_conv[2](x)
+ x = layer_conv[3](x)
+ x = layer_conv[4](x)
+
+ x = layer_deconv[4](x)
+ x = layer_deconv[3](x)
+ x = layer_deconv[2](x)
+ x = layer_deconv[1](x)
+ x = layer_deconv[0](x)
+
+ stage_output = x
+
+ stage_5 = Model(inputs = stage_input, outputs = stage_output)
+ stage_5.compile(optimizer = optimizer, loss = "mse", metrics = ["mae"])
+ stages.append(stage_5)
+
+ ### Etapa 6 ###
+
+ stage_input = Input(shape = input_shape)
+
+ x = stage_input
+
+ x = layer_conv[0](x)
+ x = layer_conv[1](x)
+ x = layer_conv[2](x)
+ x = layer_conv[3](x)
+ x = layer_conv[4](x)
+ x = layer_conv[5](x)
+
+ x = layer_deconv[5](x)
+ x = layer_deconv[4](x)
+ x = layer_deconv[3](x)
+ x = layer_deconv[2](x)
+ x = layer_deconv[1](x)
+ x = layer_deconv[0](x)
+
+ stage_output = x
+
+ stage_6 = Model(inputs = stage_input, outputs = stage_output)
+ stage_6.compile(optimizer = optimizer, loss = "mse", metrics = ["mae"])
+ stages.append(stage_6)
+
+ return stages
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
+def save_encoder(layer_conv, input_shape):
+
+ encoder_input = Input(shape = input_shape)
+
+ x = encoder_input
+
+ x = layer_conv[0](x)
+ x = layer_conv[1](x)
+ x = layer_conv[2](x)
+ x = layer_conv[3](x)
+ x = layer_conv[4](x)
+ x = layer_conv[5](x)
+
+ encoder_output = x
+
+ encoder = Model(inputs = encoder_input, outputs = encoder_output)
+
+ encoder.save("../modelos/encoder_true.h5")
+
+def save_decoder(layer_conv, layer_deconv):
+
+ decoder_target_shape = (layer_conv[5].output_shape[1],
+ layer_conv[5].output_shape[2],
+ layer_conv[5].output_shape[3])
+
+ decoder_input = Input(shape = (256,))
+
+ x = decoder_input
+
+ x = Reshape(target_shape = decoder_target_shape)(x)
+
+ x = layer_deconv[-1](x)
+ x = layer_deconv[-2](x)
+ x = layer_deconv[-3](x)
+ x = layer_deconv[-4](x)
+ x = layer_deconv[-5](x)
+ x = layer_deconv[-6](x)
+
+ decoder_output = x
+
+ decoder = Model(inputs = decoder_input, outputs = decoder_output)
+
+ decoder.save("../modelos/decoder_true.h5")
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
diff --git a/code/autoencoder_train.py b/code/autoencoder_train.py
index cbf08f51c2e1139df7f6b9c2924d4c883b94846e..c4fe8720c19f207bda72080bda53ea6797d10a3f 100644
--- a/code/autoencoder_train.py
+++ b/code/autoencoder_train.py
@@ -4,42 +4,90 @@ import tensorflow as tf
import numpy as np
import h5py
import os
-from tensorflow.keras.models import Model, load_model
-from tensorflow.keras.callbacks import ModelCheckpoint
-from datos_funciones import carga_datos, crea_sets
-from plots_funciones import training_plot
-from autoencoder_funciones import crea_autoencoder_capas, crea_adam, crea_autoencoder_etapas, guarda_encoder, guarda_decoder
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"] = "3"
-##### -------------------------------------------------- Selección GPU ---------------------------------------------- #####
+from tensorflow.keras.models import Model, load_model
+from tensorflow.keras.callbacks import ModelCheckpoint
-os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+from data_functions import load_data_density, load_data_velocity, make_sets
+from plot_functions import training_plot
+from autoencoder_functions import make_layered_autoencoder, make_adam, make_staged_autoencoder, save_encoder, save_decoder
-os.environ["CUDA_VISIBLE_DEVICES"] = "3"
##### -------------------------------------------------- Hiperparámetros -------------------------------------------- #####
-NUM_SIMS = 1900 # Índice máximo escenas.
-NUM_INICIO = 1000
+NUM_SIMS = 1010 # Índice máximo escenas.
+NUM_INITIAL = 1000 # Índice inicial escenas.
NUM_SCENES = NUM_SIMS - 1000 # Número de escenas.
NUM_FRAMES = 200 # Frames por escena.
-AE_EPOCHS = 100 # Epochs para entrenamiento normal.
+AE_EPOCHS = 25 # Epochs para entrenamiento normal.
AE_EPOCHS_LIST = 5 # Epochs para cada batch size de la lista.
PRE_EPOCHS = 1 # Epochs de preentrenamiento.
AE_BATCH_MULTIPLE = False # Probar distintos batch sizes, comparar diferencias.
-PRE_TRAINING = True # Realizar preentrenamiento.
+PRE_TRAINING = False # Realizar preentrenamiento.
ae_batch_list = [1024, 512, 256, 128, 64, 32] # Posibles batch sizes de prueba.
AE_BATCH_SIZE = 128 # Batch size oficial.
PRE_BATCH_SIZE = 128 # Bacth size para preentrenamiento.
+COMBI_FIELD = False
+VEL_FIELD = True
+DEN_FIELD = False
+
+
##### -------------------------------------------------- Carga de datos --------------------------------------------- #####
-densities = carga_datos(num_inicio = NUM_INICIO, num_sims = NUM_SIMS, frames = NUM_FRAMES)
+density_field = load_data_density(num_initial = NUM_INITIAL, num_sims = NUM_SIMS, frames = NUM_FRAMES)
+velocity_field = load_data_velocity(num_initial = NUM_INITIAL, num_sims = NUM_SIMS, frames = NUM_FRAMES)
+
+if COMBI_FIELD:
+
+ combination_field = np.concatenate((velocity_field, density_field), axis = 3)
+
+ input_shape_combination = (combination_field.shape[1],
+ combination_field.shape[2],
+ combination_field.shape[3])
+
+ train_data_combination, vali_data_combination = make_sets(field = combination_field, input_shape = input_shape_combination)
+
+
+elif VEL_FIELD:
+
+ input_shape_velocity = (velocity_field.shape[1],
+ velocity_field.shape[2],
+ velocity_field.shape[3])
+
+ train_data_velocity, vali_data_velocity = make_sets(field = velocity_field, input_shape = input_shape_velocity)
+
+ u_field = velocity_field[:, :, :, 0:1]
+
+ input_shape_u = (u_field.shape[1],
+ u_field.shape[2],
+ u_field.shape[3])
+
+ train_data_u, vali_data_u = make_sets(field = u_field, input_shape = input_shape_u)
+
+ v_field = velocity_field[:, :, :, 1:2]
+
+ input_shape_v = (v_field.shape[1],
+ v_field.shape[2],
+ v_field.shape[3])
+
+ train_data_v, vali_data_v = make_sets(field = v_field, input_shape = input_shape_v)
+
+
+elif DEN_FIELD:
+
+ input_shape_density = (density_field.shape[1],
+ density_field.shape[2],
+ density_field.shape[3])
+
+ train_data_density, vali_data_density = make_sets(field = density_field, input_shape = input_shape_density)
-train_data, vali_data = crea_sets(densities)
##### -------------------------------------------------- Autoencoder 2D --------------------------------------------- #####
@@ -47,26 +95,27 @@ FEATURE_MULTIPLIER = 8 # Controla la cantidad de filtros de convolución utiliz
SURFACE_KERNEL_SIZE = 4 # Matriz 4x4
KERNEL_SIZE = 2 # Matriz 2x2
DROPOUT = 0.0 # Porcentaje de nodos que apagar mediante dropout.
-INIT_FUNCTION = "glorot_normal" # Inicialización de los pesos de la red neuronal.
+INIT_FUNCTION = "glorot_normal" # Inicialización aleatoria de los pesos de la red neuronal.
-input_shape = (train_data.shape[1],
- train_data.shape[2],
- train_data.shape[3])
+input_shape = input_shape_u
+train_data = train_data_u
+vali_data = vali_data_u
-layer_conv, layer_deconv = crea_autoencoder_capas(input_shape = input_shape, feature_multiplier = FEATURE_MULTIPLIER, surface_kernel_size = SURFACE_KERNEL_SIZE, kernel_size = KERNEL_SIZE, dropout = DROPOUT, init_func = INIT_FUNCTION)
+layer_conv, layer_deconv = make_layered_autoencoder(input_shape = input_shape, feature_multiplier = FEATURE_MULTIPLIER, surface_kernel_size = SURFACE_KERNEL_SIZE, kernel_size = KERNEL_SIZE, dropout = DROPOUT, init_func = INIT_FUNCTION)
-optimizer = crea_adam()
+optimizer = make_adam()
+
+autoencoder_stages = make_staged_autoencoder(input_shape = input_shape, layer_conv = layer_conv, layer_deconv = layer_deconv, optimizer = optimizer)
-stages = crea_autoencoder_etapas(input_shape = input_shape, layer_conv = layer_conv, layer_deconv = layer_deconv, optimizer = optimizer)
##### -------------------------------------------------- Autoencoder Entrenamiento ---------------------------------- #####
-autoencoder = stages [-1]
+autoencoder = autoencoder_stages[-1]
autoencoder_clean_weights = autoencoder.get_weights()
if PRE_TRAINING:
- for stage in stages:
+ for stage in autoencoder_stages:
autoencoder_pre_train = stage.fit(train_data, train_data,
epochs = PRE_EPOCHS,
@@ -77,6 +126,7 @@ if PRE_TRAINING:
autoencoder.save("../modelos/autoencoder_true_pretraining.h5")
autoencoder_pre_weights = autoencoder.get_weights()
+
# ------------------------------------------------------------------------------------------------------------------- #
mc = ModelCheckpoint(filepath = "../modelos/autoencoder_true.h5",
@@ -121,5 +171,7 @@ else:
# ------------------------------------------------------------------------------------------------------------------- #
-guarda_encoder(layer_conv, input_shape)
-guarda_decoder(layer_conv, layer_deconv)
+LATENT_DIM = 256
+
+save_encoder(layer_conv, input_shape)
+save_decoder(layer_conv, layer_deconv, latent_dim = LATENT_DIM)
diff --git a/code/data_functions.py b/code/data_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..7169ab42be4f6880999513501d4bb947d33e497b
--- /dev/null
+++ b/code/data_functions.py
@@ -0,0 +1,116 @@
+# ------------------------------------------------------------------------------------------------------------------- #
+
+import os
+import sys
+import numpy as np
+
+sys.path.append("../tools") # Herramientas propias de MantaFlow.
+import uniio # Lectura de ficheros .uni
+
+
+def load_data_density (num_initial, num_sims, frames):
+
+ base_path = "../data"
+
+ densities = []
+
+ for sim in range(num_initial, num_sims):
+
+ if os.path.exists("%s/simSimple_%04d" % (base_path, sim)): # Comprueba la existencia de las carpetas (cada una 200 frames de datos).
+
+ for i in range(0, frames):
+
+ filename = "%s/simSimple_%04d/density_%04d.uni" # Nombre de cada frame (densidad).
+ uni_path = filename % (base_path, sim, i) # 200 frames por sim, rellena parametros de la ruta.
+ header, content = uniio.readUni(uni_path) # Devuelve un array Numpy [Z, Y, X, C].
+
+ h = header["dimX"]
+ w = header["dimY"]
+
+ arr = content[:, ::-1, :, :] # Cambia el orden del eje Y.
+ arr = np.reshape(arr, [w, h, 1]) # Deshecha el eje Z.
+
+ densities.append(arr)
+
+ load_num = len(densities)
+
+ if load_num < 2 * frames:
+
+ print("Error - Usa al menos dos simulaciones completas")
+
+ exit(True)
+
+ densities = np.reshape(densities, (len(densities), 64, 64, 1)) # Reconvierte la lista en array de Numpy.
+
+ print("Forma del array: {}".format(densities.shape))
+ print("Dimensiones del array: {}".format(densities.ndim))
+ print("Número de pixels en total: {}".format(densities.size))
+
+ return densities
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
+def load_data_velocity(num_initial, num_sims, frames):
+
+ base_path = "../data"
+
+ velocities = []
+
+ for sim in range(num_initial, num_sims):
+
+ if os.path.exists("%s/simSimple_%04d" % (base_path, sim)): # Comprueba la existencia de las carpetas (cada una 200 frames de datos).
+
+ for i in range(0, frames):
+
+ filename = "%s/simSimple_%04d/vel_%04d.uni" # Nombre de cada frame (velocidad).
+ uni_path = filename % (base_path, sim, i) # 200 frames por sim, rellena parametros de la ruta.
+ header, content = uniio.readUni(uni_path) # Devuelve un array Numpy [Z, Y, X, C].
+
+ h = header["dimX"]
+ w = header["dimY"]
+
+ arr = content[:, ::-1, :, :] # Cambia el orden del eje Y.
+ arr = np.reshape(arr, [w, h, 3]) # Deshecha el eje Z.
+
+ velocities.append(arr)
+
+ load_num = len(velocities)
+
+ if load_num < 2 * frames:
+
+ print("Error - Usa al menos dos simulaciones completas")
+
+ exit(True)
+
+ velocities = np.reshape(velocities, (len(velocities), 64, 64, 3)) # Reconvierte la lista en array de Numpy
+ velocities = velocities[:, :, :, 0:2] # Campo de velocidad en 2 dimensiones solamente.
+
+ print("Forma del array: {}".format(velocities.shape))
+ print("Dimensiones del array: {}".format(velocities.ndim))
+ print("Número de pixels en total: {}".format(velocities.size))
+
+ return velocities
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
+def make_sets(field, input_shape):
+
+ load_num = len(field)
+
+ vali_set_size = max(200, int(load_num * 0.1)) # Al menos una sim completa o el 10% de los datos.
+
+ vali_data = field[load_num - vali_set_size : load_num, :] # "load_num" datos del final de "densities".
+ train_data = field[0 : load_num - vali_set_size, :] # El resto de datos de "densities".
+
+ print("Separamos en {} frames de entrenamiento y {} frames de validación.".format(train_data.shape[0], vali_data.shape[0]))
+
+ train_data = np.reshape(train_data, (len(train_data), input_shape[0], input_shape[1], input_shape[2])) # Reconvertimos a arrays de Numpy.
+ vali_data = np.reshape(vali_data, (len(vali_data), input_shape[0], input_shape[1], input_shape[2]))
+
+ print("Forma del set de entrenamiento: {}".format(train_data.shape))
+ print("Forma del set de validación: {}".format(vali_data.shape))
+
+ return train_data, vali_data
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
diff --git a/code/loss_physics.py b/code/loss_physics.py
index 0459809a9e547163e2de915df9ff9aa3c8520ffe..4b698bd493d08fc631a73ec05202deb9f0c31ff0 100644
--- a/code/loss_physics.py
+++ b/code/loss_physics.py
@@ -1,5 +1,22 @@
import tensorflow as tf
+import numpy as np
-def loss_fluid_velocity(y_true, y_pred):
+def curl(field):
+
+ dfydx = field[:, 1:, 1:2] - field[:, :-1, 1:2]
+ dfxdy = field[1:, :, 0:1] - field[:-1, :, 0:1]
+
+ dfydx = tf.concat([dfydx, tf.expand_dims(dfydx[:, -1, :], axis = 1)], axis = 1)
+ dfxdy = tf.concat([dfxdy, tf.expand_dims(dfxdy[-1, :, :], axis = 0)], axis = 0)
+
+ curl = dfydx - dfxdy
+
+ return curl
+
+def curl_custom_loss(y_true, y_pred):
+
+ curl_difference = tf.reduce_mean(tf.abs(y_true - curl(y_pred)))
+
+ return curl_difference
diff --git a/code/lstm_functions.py b/code/lstm_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..92c91078b8561dedc15d1bc121e0bcc5e4c7e58f
--- /dev/null
+++ b/code/lstm_functions.py
@@ -0,0 +1,157 @@
+# ------------------------------------------------------------------------------------------------------------------- #
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.layers import RepeatVector, LSTM, Conv1D, Reshape, Input, Flatten
+from tensorflow.keras.losses import mean_absolute_error, mean_squared_error
+from tensorflow.keras.optimizers import RMSprop
+from tensorflow.keras.models import Model
+from math import floor
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
+def prepara_datos_lstm(encoder, train_data, vali_data, batch_size, time_steps, out_time_steps, frames):
+
+ encoded_train = encoder.predict(train_data)
+ encoded_vali = encoder.predict(vali_data)
+
+ def generator_count(encoded_data, batch_size, time_steps, out_time_steps, frames):
+
+ scene_count = len(encoded_data) // frames
+ sample_count = frames
+ scene_iteration_count = floor((sample_count + 1 - (time_steps + out_time_steps)) / batch_size)
+
+ return scene_count, sample_count, scene_iteration_count
+
+ def generator_batch_samples(encoded_data, batch_size, time_steps, out_time_steps, frames):
+
+ scene_count, sample_count, scene_iteration_count = generator_count(encoded_data, batch_size, time_steps, out_time_steps, frames)
+ batch_samples = scene_count * scene_iteration_count
+
+ return batch_samples
+
+ def shuffle_in_unison(*np_arrays):
+
+ rng = np.random.get_state()
+
+ for array in np_arrays:
+
+ np.random.set_state(rng)
+ np.random.shuffle(array)
+
+ def restructure_encoded_data(encoded_data, time_steps, out_time_steps, batch_size):
+
+ content_shape = encoded_data[0].shape # (256,)
+ final_sample_count = encoded_data.shape[0] - time_steps - out_time_steps # frames, frames - batch_size, frames - 2 * batch_size, ...
+ final_sample_count = min(batch_size, final_sample_count) # 8
+
+ X_data = np.zeros((final_sample_count, time_steps) + content_shape) # (8, 6, 256)
+ y_data = np.zeros((final_sample_count, out_time_steps) + content_shape) # (8, 1, 256)
+
+ curTS = 0
+
+ for z in range(time_steps, final_sample_count + time_steps):
+
+ X_data[curTS] = np.array(encoded_data[curTS:z])
+ y_data[curTS] = np.array(encoded_data[z:z+out_time_steps])
+ curTS += 1
+
+ return X_data, y_data
+
+ def generator_scene(encoded_data, batch_size, time_steps, out_time_steps, frames):
+
+ scene_count, sample_count, scene_iteration_count = generator_count(encoded_data, batch_size, time_steps, out_time_steps, frames)
+
+ while True:
+
+ for i in range(scene_count):
+
+ scene = encoded_train[(i * frames):((i + 1) * frames)] # Selecciona escenas individualmente.
+
+ for j in range(scene_iteration_count): # Número de batches que entran en una escena individual.
+
+ start = j * batch_size
+ end = sample_count
+
+ data = scene[start:end]
+ X, Y = restructure_encoded_data(data, time_steps, out_time_steps, batch_size)
+
+ X = X.reshape(*X.shape[0:2], -1)
+ Y = np.squeeze(Y.reshape(Y.shape[0], out_time_steps, -1))
+
+ shuffle_in_unison(X, Y)
+
+ yield X, Y
+
+ train_gen_samples = generator_batch_samples(encoded_train, batch_size, time_steps, out_time_steps, frames)
+ print ("Number of train batch samples per epoch: {}".format(train_gen_samples))
+ train_generator = generator_scene(encoded_train, batch_size, time_steps, out_time_steps, frames)
+
+ vali_gen_samples = generator_batch_samples(encoded_vali, batch_size, time_steps, out_time_steps, frames)
+ print ("Number of validation batch samples per epoch: {}".format(vali_gen_samples))
+ vali_generator = generator_scene(encoded_vali, batch_size, time_steps, out_time_steps, frames)
+
+ return train_gen_samples, train_generator, vali_gen_samples, vali_generator
+
+def crea_lstm(time_steps, out_time_steps, latent_dimension, encoder_lstm_neurons, decoder_lstm_neurons, activation, use_bias, dropout, recurrent_dropout, stateful, lstm_optimizer, loss):
+
+ lstm_input = Input(shape = (time_steps, latent_dimension))
+
+ x = lstm_input
+
+ x = LSTM(units = encoder_lstm_neurons,
+ activation = activation,
+ use_bias = use_bias,
+ recurrent_activation = "hard_sigmoid",
+ kernel_initializer = "glorot_uniform",
+ recurrent_initializer = "orthogonal",
+ bias_initializer = "zeros",
+ unit_forget_bias = True,
+ dropout = dropout,
+ recurrent_dropout = recurrent_dropout,
+ return_sequences = False,
+ go_backwards = True,
+ stateful = stateful)(x)
+
+ x = RepeatVector(out_time_steps)(x)
+
+ x = LSTM(units = decoder_lstm_neurons,
+ activation = activation,
+ use_bias = use_bias,
+ recurrent_activation = "hard_sigmoid",
+ kernel_initializer = "glorot_uniform",
+ recurrent_initializer = "orthogonal",
+ bias_initializer = "zeros",
+ unit_forget_bias = True,
+ dropout = dropout,
+ recurrent_dropout = recurrent_dropout,
+ return_sequences = True,
+ go_backwards = False,
+ stateful = stateful)(x)
+
+ x = Conv1D(filters = latent_dimension, kernel_size = 1)(x)
+
+ x = Flatten()(x) if out_time_steps == 1 else x
+
+ lstm_output = x
+
+ lstm = Model(inputs = lstm_input, outputs = lstm_output)
+
+ lstm.compile(loss = loss,
+ optimizer = lstm_optimizer,
+ metrics = ["mse"])
+
+ return lstm
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
+def crea_optimizador_lstm():
+
+ optimizer = RMSprop(lr = 0.000126,
+ rho = 0.9,
+ epsilon = 1e-08,
+ decay = 0.000334)
+
+ return optimizer
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
diff --git a/code/plot_functions.py b/code/plot_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..94fdd2edf4689afa7b82da82dcefa4b0e746b51a
--- /dev/null
+++ b/code/plot_functions.py
@@ -0,0 +1,41 @@
+# ------------------------------------------------------------------------------------------------------------------- #
+
+import matplotlib.pyplot as plt
+
+def training_plot(network_train, epochs, batch_size, dropout, identification, loss, metric):
+
+ plot_epochs = range(epochs)
+ plot_loss = network_train.history["loss"]
+ plot_val_loss = network_train.history["val_loss"]
+ plot_metric = network_train.history[metric]
+ plot_val_metric = network_train.history["val_" + metric]
+
+ plt.figure(figsize = (15, 5))
+
+ ax = plt.subplot(1, 2, 1)
+ plt.plot(plot_epochs, plot_loss, label = loss.upper())
+ plt.plot(plot_epochs, plot_val_loss, label = "Validation " + loss.upper())
+ plt.legend()
+ plt.xlabel("Epoch")
+ plt.ylabel(loss.upper())
+
+ ax = plt.subplot(1, 2, 2)
+ plt.plot(plot_epochs, plot_metric, label = metric.upper())
+ plt.plot(plot_epochs, plot_val_metric, label = "Validation " + metric.upper())
+ plt.legend()
+ plt.xlabel("Epoch")
+ plt.ylabel(metric.upper())
+
+ if dropout > 0.0:
+
+ plt.savefig("../plots/model_" + identification + "_DO-" + str(dropout * 100) + "_BS-" + str(batch_size) + ".png")
+
+ else:
+
+ plt.savefig("../plots/model_" + identification + "_BS-" + str(batch_size) + ".png")
+
+# ------------------------------------------------------------------------------------------------------------------- #
+
+def velocity_plot(velocity_field):
+
+ pass
diff --git a/code/predictions.py b/code/predictions.py
new file mode 100644
index 0000000000000000000000000000000000000000..93dcf56cbf103df041d6dafc6cf80d2758d04ada
--- /dev/null
+++ b/code/predictions.py
@@ -0,0 +1,83 @@
+import os
+import tensorflow as tf
+import numpy as np
+import scipy.misc
+import matplotlib.pyplot as plt
+from tensorflow.keras.models import load_model
+from random import randrange
+
+from datos_funciones import carga_datos
+
+NUM_INICIO = 1900
+NUM_SIMS = 2000
+FRAMES = 200
+TIME_STEPS = 6
+OUT_TIME_STEPS = 1
+
+os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "3"
+
+densities = carga_datos(num_inicio = NUM_INICIO, num_sims = NUM_SIMS, frames = FRAMES)
+
+autoencoder = load_model("../modelos/autoencoder_24e-5.h5")
+encoder = load_model("../modelos/encoder_24e-5.h5")
+decoder = load_model("../modelos/decoder_24e-5.h5")
+lstm = load_model("../modelos/lstm_true.h5")
+
+NUM_SCENES = densities.shape[0] // FRAMES
+RAND_SCENE = randrange(0, NUM_SCENES)
+
+scene = densities[RAND_SCENE*FRAMES : RAND_SCENE*FRAMES + FRAMES, :, :, :]
+
+autoencoder_scene = autoencoder.predict(scene)
+
+encoded_scene = encoder.predict(scene)
+latent_dim = encoded_scene.shape[-1]
+
+lstm_scene = []
+
+for i in range(FRAMES - 5):
+
+ time_frames = encoded_scene[i : i + 6]
+ time_frames = time_frames.reshape(1, 6, latent_dim)
+ lstm_prediction = lstm.predict(time_frames, batch_size = 1)
+ decoded_frame = decoder.predict(lstm_prediction)
+ lstm_scene.append(decoded_frame)
+
+lstm_scene = np.reshape(lstm_scene, (len(lstm_scene), 64, 64, 1))
+
+n = 10
+
+plt.figure(figsize = (10, 3))
+
+for i in range(n):
+
+ ax = plt.subplot(3, n, i + 1)
+ plt.imshow(scene[i].reshape(64, 64))
+ plt.gray()
+ ax.get_xaxis().set_visible(False)
+ ax.get_yaxis().set_visible(False)
+
+ ax = plt.subplot(3, n, i + 1 + n)
+ plt.imshow(autoencoder_scene[i].reshape(64, 64))
+ plt.gray()
+ ax.get_xaxis().set_visible(False)
+ ax.get_yaxis().set_visible(False)
+
+ ax = plt.subplot(3, n, i + 1 + n + n)
+ plt.imshow(lstm_scene[i].reshape(64, 64))
+ plt.gray()
+ ax.get_xaxis().set_visible(False)
+ ax.get_yaxis().set_visible(False)
+
+ plt.savefig("../plots/comparativa.png")
+
+out_dir = "../imagenes"
+if not os.path.exists(out_dir): os.makedirs(out_dir)
+
+for i in range(TIME_STEPS, FRAMES):
+ scipy.misc.toimage(np.reshape(scene[i - TIME_STEPS], [64, 64])).save("{}/in_{}.png".format(out_dir, i))
+ scipy.misc.toimage(np.reshape(autoencoder_scene[i - TIME_STEPS], [64, 64])).save("{}/out_{}.png".format(out_dir, i))
+ scipy.misc.toimage(np.reshape(lstm_scene[i - TIME_STEPS], [64, 64])).save("{}/pred_{}.png".format(out_dir, i))
+
diff --git a/code/test_velocity.py b/code/test_velocity.py
index 20b479e470105cdbc9f0400c30de5cbe75184bbf..d8f248808efa397fa439210f50c1e0688850114e 100644
--- a/code/test_velocity.py
+++ b/code/test_velocity.py
@@ -2,8 +2,9 @@ import numpy as np
import tensorflow as tf
import os
import matplotlib.pyplot as plt
+import imageio
-from datos_funciones import carga_datos_velocity
+from data_functions import load_data_density, load_data_velocity
##### -------------------------------------------------- Selección GPU ---------------------------------------------- #####
@@ -13,23 +14,52 @@ os.environ["CUDA_VISIBLE_DEVICES"] = "3"
##### -------------------------------------------------- Hiperparámetros -------------------------------------------- #####
-NUM_SIMS = 1900 # Índice máximo escenas.
-NUM_INICIO = 1000
-NUM_SCENES = NUM_SIMS - 1000 # Número de escenas.
+NUM_SIMS = 1020 # Índice máximo escenas.
+NUM_INITIAL = 1000
+NUM_SCENES = NUM_SIMS - NUM_INITIAL # Número de escenas.
NUM_FRAMES = 200 # Frames por escena.
-AE_EPOCHS = 100 # Epochs para entrenamiento normal.
-AE_EPOCHS_LIST = 5 # Epochs para cada batch size de la lista.
-PRE_EPOCHS = 1 # Epochs de preentrenamiento.
+##### -------------------------------------------------- Carga de datos --------------------------------------------- #####
-AE_BATCH_MULTIPLE = False # Probar distintos batch sizes, comparar diferencias.
-PRE_TRAINING = True # Realizar preentrenamiento.
+density_field = load_data_density(num_initial = NUM_INITIAL, num_sims = NUM_SIMS, frames = NUM_FRAMES)
+velocity_field = load_data_velocity(num_initial = NUM_INITIAL, num_sims = NUM_SIMS, frames = NUM_FRAMES)
-ae_batch_list = [1024, 512, 256, 128, 64, 32] # Posibles batch sizes de prueba.
-AE_BATCH_SIZE = 128 # Batch size oficial.
-PRE_BATCH_SIZE = 128 # Bacth size para preentrenamiento.
+gif = []
-##### -------------------------------------------------- Carga de datos --------------------------------------------- #####
+for scene in range(NUM_SCENES):
+
+ for frame in range(NUM_FRAMES):
+
+ u = velocity_field[scene * NUM_FRAMES + frame, :, :, 0]
+ v = velocity_field[scene * NUM_FRAMES + frame, :, :, 1]
+
+ rho = density_field[scene * NUM_FRAMES + frame, :, :, 0]
+
+ fig, ax = plt.subplots(figsize = (5, 5), nrows = 2, ncols = 2)
+
+ ax1, ax2, ax3, ax4 = ax.flatten()
+
+ ax1.imshow(rho, cmap = "gray")
+ ax1.axis("off")
+
+ ax2.imshow(np.sqrt(np.square(u) + np.square(v)), cmap = "viridis")
+ ax2.axis("off")
+
+ ax3.imshow(u, cmap = "RdBu")
+ ax3.axis("off")
+
+ ax4.imshow(v, cmap = "PRGn")
+ ax4.axis("off")
+
+ fig.tight_layout()
+
+ plt.savefig("../imagenes/plot_velocity/velocity_scene-" + str(scene) + "_frame-" + str(frame) + ".png")
+
+ plt.close()
+
+ # gif.append(imageio.imread("../imagenes/plot_velocity/velocity_scene-" + str(scene) + "_frame-" + str(frame) + ".png"))
+
+ # imageio.mimsave("../gifs/plot_velocity/velocity_scene-" + str(scene) + ".gif", gif)
+
-velocities = carga_datos_velocity(num_inicio = NUM_INICIO, num_sims = NUM_SIMS, frames = NUM_FRAMES)
diff --git a/deeplearningcode.zip b/deeplearningcode.zip
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diff --git a/modelos/autoencoder_true.h5 b/modelos/autoencoder_true.h5
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diff --git a/modelos/decoder_true.h5 b/modelos/decoder_true.h5
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diff --git a/modelos/encoder_true.h5 b/modelos/encoder_true.h5
index d3507aed8b515609616523705c031a0790bca245..a6cca7de36c083060b089cf9067eecb514970405 100644
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diff --git a/plots/model_ae_true_single_BS-128.png b/plots/model_ae_true_single_BS-128.png
index af7d0ef46aa831db81e5b5a5df5b1bb1ee852751..13e22ee7ab9b008cc5e49062e9456906ed5093ea 100644
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diff --git a/update_files.py b/update_files.py
index 5bb84716080fe13260adb48e056f4372b16d63e7..bb122a27c051b55e058cadc143847cc65eb559e2 100644
--- a/update_files.py
+++ b/update_files.py
@@ -3,6 +3,6 @@ from os import system as sys
sys("scp -r guest@172.26.211.24:./deeplearningjon/code ./")
sys("scp -r guest@172.26.211.24:./deeplearningjon/plots ./")
sys("scp -r guest@172.26.211.24:./deeplearningjon/modelos ./")
-sys("scp -r guest@172.26.211.24:./deeplearningjon/imagenes ./")
-sys("scp -r guest@172.26.211.24:./deeplearningjon/gifs ./")
+# sys("scp -r guest@172.26.211.24:./deeplearningjon/imagenes ./")
+# sys("scp -r guest@172.26.211.24:./deeplearningjon/gifs ./")
sys("zip -r deeplearningcode.zip ./code")