# 6 = Charizard
chosen_pokemon_2 = 9 # Squirtle
# chosen_pokemon_2 = 13 # Caterpie
chosen_pokemon = 30 # Pikachu
chosen_pokemon = 146 # Jolteon
# chosen_pokemon = 161 # Dragonite
# chosen_pokemon_2 = 252 # Porygon 2
# chosen_pokemon_2 = 330 # Aaron
# chosen_pokemon = 427 # Jirachi
# chosen_pokemon_2 = 622 # Darmanitan
# chosen_pokemon = 972 # shield doggo.
interpolation_intervals = 7
X_full_HSV, Y_full_HSV = \
utilities.prepare_dataset_for_input_layer("pokedataset32_full_HSV_Two_Hot_Encoded.h5")
Y_full_HSV = Y_full_HSV * 0.5
expanded_full_X_HSV = np.append(X_full_HSV, Y_full_HSV, axis=1)
print("getting network to load model*******************")
network_instance = utilities.get_network()
network_instance = tflearn.regression(
network_instance,
optimizer='adam',
metric='R2',
loss=utilities.vae_loss,
learning_rate=0.00001) # adagrad? #adadelta #nesterov did good,
model = tflearn.DNN(network_instance)
print("LOADING MODEL.")
import tflearn
import h5py
import pokedataset32_vae_functions as utilities
from PIL import Image
import colorsys
# current_dataset = 'pokedataset'
current_dataset = 'anime_faces_'
use_anime_with_types = True
if not use_anime_with_types:
X_full_HSV, Y_full_HSV, X_full_RGB, Y_full_RGB, X, Y, test_X, test_Y = utilities.ready_all_data_sets(
current_dataset)
else:
X, Y = utilities.prepare_dataset_for_input_layer(
'anime_faces_32_train_HSV_Two_Hot_Encoded_Augmented_With_Types.h5',
in_dataset_x_label='anime_faces_32_X',
in_dataset_y_label='anime_faces_32_Y')
test_X, test_Y = utilities.prepare_dataset_for_input_layer(
'anime_faces_32_train_HSV_Two_Hot_Encoded_Augmented_With_Types.h5',
in_dataset_x_label='anime_faces_32_X_test',
in_dataset_y_label='anime_faces_32_Y_test')
X_full_RGB, Y_full_RGB = utilities.prepare_dataset_for_input_layer(
'anime_faces_32_full_RGB_Two_Hot_Encoded.h5',
in_dataset_x_label='anime_faces_32_X',
in_dataset_y_label='anime_faces_32_Y')
X_first_half = X[0:int(len(X) / 2)]
Y_first_half = Y[0:int(len(Y) / 2)]
test_X_first_half = test_X[0:int(len(test_X) / 2)]
test_Y_first_half = test_Y[0:int(len(test_Y) / 2)]
"""X_second_half = X[int(len(X) / 2):]
import h5py
import pokedataset32_vae_functions as utilities
from PIL import Image
import colorsys
import math
# current_dataset = 'pokedataset'
# current_dataset = 'anime_faces_'
# We don't need all of those.
# X_full_HSV, Y_full_HSV, X_full_RGB, Y_full_RGB, X, Y, test_X, test_Y = utilities.ready_all_data_sets(current_dataset)
# X_full_HSV, Y_full_HSV = utilities.prepare_dataset_for_input_layer('pokedataset32_full_HSV_Two_Hot_Encoded.h5')
X_full_HSV, Y_full_HSV = utilities.prepare_dataset_for_input_layer(
'pokedataset32_full_HSV_Two_Hot_Encoded.h5',
in_dataset_x_label='pokedataset32_X',
in_dataset_y_label='pokedataset32_Y')
# """
X_full_HSV_faces, Y_full_HSV_faces = utilities.prepare_dataset_for_input_layer(
'anime_faces_32_train_HSV_Two_Hot_Encoded_Augmented.h5',
in_dataset_x_label='anime_faces_32_X',
in_dataset_y_label='anime_faces_32_Y')
X_test_HSV_faces, Y_test_HSV_faces = utilities.prepare_dataset_for_input_layer(
'anime_faces_32_train_HSV_Two_Hot_Encoded_Augmented.h5',
in_dataset_x_label='anime_faces_32_X_test',
in_dataset_y_label='anime_faces_32_Y_test')
# """
"""
# FOR DEBUGGING PURPOSES:
import numpy as np
import tensorflow as tf
import tflearn
import matplotlib.colors
import pokedataset32_vae_functions as utilities
current_dataset = 'pokedataset'
# current_dataset = 'anime_faces_'
# X and Y are not used in this file.
X_full_HSV, Y_full_HSV, X_full_RGB, Y_full_RGB, X, Y, test_X, test_Y = utilities.ready_all_data_sets(
current_dataset)
X_full_HSV_regional, Y_full_HSV_regional = \
utilities.prepare_dataset_for_input_layer("pokedataset32_full_HSV_Two_Hot_Encoded_Regional.h5")
Y_full_HSV_regional = Y_full_HSV_regional * 0.5
expanded_full_X_HSV_regional = np.append(X_full_HSV_regional,
Y_full_HSV_regional,
axis=1)
X_full_RGB_regional, Y_full_RGB_regional = \
utilities.prepare_dataset_for_input_layer("pokedataset32_full_RGB_Two_Hot_Encoded_Regional.h5")
Y_full_RGB_regional = Y_full_HSV_regional * 0.5
expanded_full_X_RGB_regional = np.append(X_full_HSV_regional,
Y_full_HSV_regional,
axis=1)
Y_full_HSV = Y_full_HSV * 0.50
small_X = np.concatenate((X[0:200], test_X[0:200]), axis=0)
small_Y = np.concatenate((Y[0:200], test_Y[0:200]), axis=0)
len_X_div_2 = int(len(X) / 2)
from __future__ import division, print_function, absolute_import
import numpy as np
import tensorflow as tf
import tflearn
import matplotlib.pyplot as plt
import matplotlib.colors
import pokedataset32_vae_functions as utilities
X_full_HSV_Type_Swapped, Y_full_HSV_Type_Swapped = \
utilities.prepare_dataset_for_input_layer('pokedataset32_full_HSV_Two_Hot_Encoded_Type_Swapped.h5')
X_full_RGB_Type_Swapped, Y_full_RGB_Type_Swapped = \
utilities.prepare_dataset_for_input_layer('pokedataset32_full_RGB_Two_Hot_Encoded_Type_Swapped.h5')
Y_full_HSV_Type_Swapped = np.reshape(np.asarray(Y_full_HSV_Type_Swapped),
newshape=[Y_full_HSV_Type_Swapped.shape[0], utilities.pokemon_types_dim])
expanded_full_X_HSV_Type_Swapped = np.append(X_full_HSV_Type_Swapped, Y_full_HSV_Type_Swapped, axis=1)
print("getting network to load model*******************")
network_instance = utilities.get_network()
network_instance = tflearn.regression(network_instance,
optimizer='adam',
# optimizer='rmsprop',
metric='R2',
# loss='mean_square',
loss=utilities.vae_loss,
learning_rate=0.001) # adagrad? #adadelta #nesterov did good,