loss=utilities.vae_loss,
learning_rate=0.00001) # adagrad? #adadelta #nesterov did good,
model = tflearn.DNN(network_instance)
print("LOADING MODEL.")
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" +
model_name + ".tflearn")
print("MODEL SUCCESSFULLY LOADED.")
encoder_model = utilities.get_encoder_network(model)
generator_model = utilities.get_generative_network(model)
print("LOADED GENERATOR MODEL.")
encoded_pokemon = utilities.predict_batches(expanded_full_X_HSV,
encoder_model,
in_samples_per_batch=64,
in_input_name='input_images')
# Pick one pokemon and repeat it a number of times equal to the total noise samples
target_pokemon = encoded_pokemon[chosen_pokemon]
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,
n_epoch=1,
shuffle=True,
show_metric=True,
snapshot_epoch=True,
batch_size=128,
# validation_set=0.15, # It also accepts a float < 1 to performs a data split over training data.
validation_set=(expanded_test_X, expanded_test_X
), # We use it for validation for now. But also test.
run_id='encoder_decoder')
print("getting samples to show on screen.")
encode_decode_sample = []
if predict_full_dataset:
predicted_X = X
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)
model = tflearn.DNN(network_instance)
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 +
loss=utilities.vae_loss,
learning_rate=0.00001) # adagrad? #adadelta #nesterov did good,
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.
exporting_RGB = X_full_RGB
else:
# Non-regional to regional
expanded_fake_X = np.append(X_full_HSV_non_regional,
new_types_array,
axis=1)
exporting_RGB = X_full_RGB_non_regional
# Regional to Non-regional
# expanded_fake_X = np.append(X_full_HSV_regional, new_types_array, axis=1)
# exporting_RGB = X_full_RGB_regional
else:
expanded_fake_X = np.append(small_X, new_types_array, axis=1)
exporting_RGB = small_X_RGB
print("getting samples to show on screen.")
encode_decode_sample_original = utilities.predict_batches(
predicted_X, model, in_samples_per_batch=64)
encode_decode_sample_fake = utilities.predict_batches(expanded_fake_X,
model,
in_samples_per_batch=64)
reconstructed_pixels_original, reconstructed_types_original = \
utilities.reconstruct_pixels_and_types(encode_decode_sample_original)
reconstructed_pixels_fake, reconstructed_types_fake = \
utilities.reconstruct_pixels_and_types(encode_decode_sample_fake)
wanted_indices_RGB = [exporting_RGB[i] for i in indices_to_predict]
wanted_indices_pixels_original = [
reconstructed_pixels_original[i] for i in indices_to_predict
]
wanted_indices_pixels_fake = [