second_target_pokemon = encoded_pokemon[chosen_pokemon_2]
target_pokemon_difference = target_pokemon - second_target_pokemon
target_pokemon_difference = target_pokemon_difference / float(
interpolation_intervals) # Divided by the number of samples
interpolated_pokemon = []
for i in range(0, interpolation_intervals):
current_interpolation = second_target_pokemon + (
target_pokemon_difference * i)
interpolated_pokemon.append(current_interpolation)
interpolated_transfer = utilities.predict_batches(interpolated_pokemon,
generator_model,
in_samples_per_batch=64,
in_input_name='input_noise')
reconstructed_pixels_generated_transfer, reconstructed_types_generated_transfer = \
utilities.reconstruct_pixels_and_types(interpolated_transfer)
##########################################################################
second_model_name = "_regular_V3_no_noise2"
model.load("saved_models/model_Jul_29_optim_adam_loss_vae_loss_"
"last_activ_relu_latent_128_num_filters_512_1024_decoder_width_8" +
second_model_name + ".tflearn",
weights_only=True)
encoder_model_transfer = utilities.get_encoder_network(model)
generator_model_transfer = utilities.get_generative_network(model)
print("LOADED GENERATOR MODEL.")
encoded_pokemon = utilities.predict_batches(expanded_full_X_HSV,
encoder_model_transfer,
in_samples_per_batch=64,
in_input_name='input_images_1')
target_pokemon = encoded_pokemon[chosen_pokemon]
predicted_Y = Y_full_RGB
encode_decode_sample = utilities.predict_batches(
expanded_full_X_HSV, model, in_samples_per_batch=64)
else:
predicted_X = small_X
predicted_Y = small_Y
encode_decode_sample = utilities.predict_batches(
expanded_small_X, model, in_samples_per_batch=64)
# encode_decode_sample = model.predict(expanded_X) # Just to test training with RGB. It seemed worse.
print("The number of elements in the predicted samples is: " +
str(len(encode_decode_sample)))
reconstructed_pixels = []
reconstructed_types = []
# Made a function to avoid repeating that fragment of code in other python files.
reconstructed_pixels, reconstructed_types = utilities.reconstruct_pixels_and_types(
encode_decode_sample)
print("Exporting reconstructed pokemon as an image.")
# utilities.export_as_atlas(X_full_RGB, reconstructed_pixels) # I have checked that it works perfectly.
if predict_full_dataset:
correct_indices = utilities.export_types_csv(Y_full_RGB,
reconstructed_types)
else:
correct_indices = utilities.export_types_csv(small_Y,
reconstructed_types)
# This is used to export an image only containing the ones whose types were correctly predicted by the NN.
# correct_X_RGB = [X_full_RGB[i] for i in correct_indices]
# correct_reconstructed_pixels = [reconstructed_pixels[i] for i in correct_indices]
# utilities.export_as_atlas(correct_X_RGB, correct_reconstructed_pixels, name_annotations='correct')
print_ssim_scores = True
print("LOADING MODEL.")
first_model_name = "_regular_V3_no_noise2" # This is the one for the encoder-only part. From Jul_28
# first_model_name = "_regular_V2_more_noise2" # this was from Jul_23
# This hasn't been commited yet, due to network restrictions (AKA slow upload connection).
# Double check to have a folder with the correct path here.
model.load("saved_models/model_Jul_29_optim_adam_loss_vae_loss_"
"last_activ_relu_latent_128_num_filters_512_1024_decoder_width_8" +
first_model_name + ".tflearn")
print("getting samples to show on screen.")
encode_decode_sample_original = utilities.predict_batches(
expanded_full_X_HSV, model, in_samples_per_batch=64)
reconstructed_pixels_original, reconstructed_types_original = \
utilities.reconstruct_pixels_and_types(encode_decode_sample_original)
print("MSE value for the " + first_model_name +
" model, over the WHOLE dataset is: ")
utilities.mean_square_error(reconstructed_pixels_original, X_full_RGB)
if print_ssim_scores:
print("SSIM is: ")
utilities.ssim_comparison(reconstructed_pixels_original, X_full_RGB)
# Now, training only:
encode_decode_sample_original_train = utilities.predict_batches(
expanded_X, model, in_samples_per_batch=64)
reconstructed_pixels_original_train, reconstructed_types_original_train = \
utilities.reconstruct_pixels_and_types(encode_decode_sample_original_train)
print("MSE value for the " + first_model_name +
" model, over the TRAINING dataset is: ")
utilities.mean_square_error(
model = tflearn.DNN(network_instance)
print("LOADING MODEL.")
#####################
# TRANSFER RECONSTRUCTED
# Now, we can load the transfer learning model.
# second_model_name = "_V3_noise2" # 21 of july # "_V3_noise4" THIS WAS A GOOD MODEL.
first_model_name = "_anime_V2_poke5"
model.load("saved_models/model_Jul_29_optim_adam_loss_vae_loss_"
"last_activ_relu_latent_128_num_filters_512_1024_decoder_width_8" +
first_model_name + ".tflearn")
encode_decode_sample_transfer = utilities.predict_batches(
expanded_originals, model, in_samples_per_batch=64)
reconstructed_transfer, reconstructed_types_transfer = \
utilities.reconstruct_pixels_and_types(encode_decode_sample_transfer)
###################
# SWAPPED TRANSFER
encode_decode_sample_transfer = utilities.predict_batches(
originals_fake_type, model, in_samples_per_batch=64)
swapped_transfer, reconstructed_types_transfer = \
utilities.reconstruct_pixels_and_types(encode_decode_sample_transfer)
###################
# RECONSTRUCTED NON-TRANSFER
second_model_name = "_regular_V3_no_noise2" # This is the one for the encoder-only part. From Jul_28
# This hasn't been commited yet, due to network restrictions (AKA slow upload connection).
# Double check to have a folder with the correct path here.
model.load("saved_models/model_Jul_29_optim_adam_loss_vae_loss_"
"last_activ_relu_latent_128_num_filters_512_1024_decoder_width_8" +
interpolated_pokemon.append(current_interpolation)
interpolated_transfer = utilities.predict_batches(interpolated_pokemon, generator_model,
in_samples_per_batch=64,
in_input_name='input_noise')
modified_encoded_pokemon = np.tile(target_pokemon, (len(latent_dim_changes), 1))
modified_encoded_pokemon = modified_encoded_pokemon + np.asarray(latent_dim_changes)
# Check how to do it in batches later. input_noise_list
encode_decode_generated_samples_transfer = utilities.predict_batches(modified_encoded_pokemon, generator_model,
in_samples_per_batch=64,
in_input_name='input_noise')
# encode_decode_generated_samples = generator_model.predict({'input_noise': input_noise_list})
reconstructed_pixels_generated_transfer, reconstructed_types_generated_transfer = \
utilities.reconstruct_pixels_and_types(encode_decode_generated_samples_transfer)
second_model_name = "_regular_V3_no_noise2"
model.load("saved_models/model_Jul_29_optim_adam_loss_vae_loss_"
"last_activ_relu_latent_128_num_filters_512_1024_decoder_width_8" + second_model_name + ".tflearn",
weights_only=True)
encoder_model_transfer = utilities.get_encoder_network(model)
generator_model_transfer = utilities.get_generative_network(model)
print("LOADED GENERATOR MODEL.")
encoded_pokemon = utilities.predict_batches(expanded_full_X_HSV, encoder_model_transfer,
in_samples_per_batch=64,
in_input_name='input_images_1')
# Pick one pokemon and repeat it a number of times equal to the total noise samples