def full_test():
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
sess = tf.Session()
sda = SDAutoencoder(dims=[6000, 1000, 500, 100],
activations=["sigmoid", "sigmoid", "sigmoid"],
sess=sess,
noise=0.5,
loss="rmse",
pretrain_lr=1e-6,
finetune_lr=1e-5,
batch_size=100,
print_step=500)
sda.pretrain_network(X_TRAIN_PATH, epochs=1)
trained_parameters = sda.finetune_parameters(X_TRAIN_PATH,
Y_TRAIN_PATH,
output_dim=6,
epochs=1)
sda.write_encoded_input(TRANSFORMED_PATH, X_TEST_PATH)
sda.save_variables(VARIABLE_SAVE_PATH)
sess.close()
test_model(parameters_dict=trained_parameters,
input_dim=sda.output_dim,
output_dim=6,
x_test_filepath=TRANSFORMED_PATH,
y_test_filepath=Y_TEST_PATH,
output_filepath=OUTPUT_PATH)
def full_test():
sess = tf.Session()
sda = SDAutoencoder(dims=[4000, 400, 400, 400],
activations=["sigmoid", "sigmoid", "sigmoid"],
sess=sess,
noise=0.20,
loss="cross-entropy",
pretrain_lr=1e-6,
finetune_lr=1e-5,
batch_size=50,
print_step=500)
sda.pretrain_network(X_TRAIN_PATH, epochs=50)
trained_parameters = sda.finetune_parameters(X_TRAIN_PATH,
Y_TRAIN_PATH,
output_dim=2,
epochs=80)
sda.write_encoded_input(TRANSFORMED_PATH, X_TEST_PATH)
sda.save_variables(VARIABLE_SAVE_PATH)
sess.close()
test_model(parameters_dict=trained_parameters,
input_dim=sda.output_dim,
output_dim=2,
x_test_filepath=TRANSFORMED_PATH,
y_test_filepath=Y_TEST_PATH,
output_filepath=OUTPUT_PATH)
def unsupervised():
sess = tf.Session()
sda = SDAutoencoder(dims=[6000, 1000, 500, 200],
activations=["sigmoid", "sigmoid", "sigmoid"],
sess=sess,
noise=0.05,
loss="rmse",
batch_size=100,
print_step=50)
layer_1_weights_path = "../data/outputs/last_weights"
layer_1_biases_path = "../data/outputs/last_biases"
sda.pretrain_network(X_TRAIN_PATH, epochs=8)
sda.write_data(sda.hidden_layers[1].weights, layer_1_weights_path)
sda.write_data(sda.hidden_layers[1].biases, layer_1_biases_path)
sda.write_encoded_input(TRANSFORMED_PATH, X_TEST_PATH)
sda.save_variables(VARIABLE_SAVE_PATH)
sess.close()
def unsupervised():
sess = tf.Session()
sda = SDAutoencoder(dims=[4000, 1000, 500, 200],
activations=["sigmoid", "sigmoid", "sigmoid"],
sess=sess,
noise=0.05,
loss="rmse",
batch_size=100,
print_step=50)
layer_1_weights_path = "../data/outputs/last_weights"
layer_1_biases_path = "../data/outputs/last_biases"
sda.pretrain_network(X_TRAIN_PATH, epochs=8)
sda.write_data(sda.hidden_layers[1].weights, layer_1_weights_path)
sda.write_data(sda.hidden_layers[1].biases, layer_1_biases_path)
sda.write_encoded_input(TRANSFORMED_PATH, X_TEST_PATH)
sda.save_variables(VARIABLE_SAVE_PATH)
sess.close()
def full_test():
clearCsv(TRANSFORMED_PATH)
clearCsv(OUTPUT_PATH)
clearCsv(BIASES_PATH)
clearCsv(WEIGHT_PATH)
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
sess = tf.Session()
sda = SDAutoencoder(dims=[160, 80, 30, 5],
activations=["sigmoid", "sigmoid", "sigmoid"],
sess=sess,
noise=0.2,
loss="rmse",
pretrain_lr=1e-4,
finetune_lr=1e-2,
batch_size=20,
print_step=100)
sda.pretrain_network(X_TRAIN_PATH, epochs=2000)
trained_parameters = sda.finetune_parameters(X_TRAIN_PATH,
Y_TRAIN_PATH,
output_dim=3,
epochs=1000)
sda.write_data(trained_parameters["weights"], WEIGHT_PATH)
sda.write_data([trained_parameters["biases"]], BIASES_PATH)
sda.write_encoded_input(TRANSFORMED_PATH, X_TEST_PATH)
sda.save_variables(VARIABLE_SAVE_PATH)
sess.close()
# print(len(sda.hidden_layers))
# print(sda.hidden_layers[0].weights,sda.hidden_layers[0].biases)
# print(sda.hidden_layers[1].weights,sda.hidden_layers[1].biases)
# with open("sda.file", "wb") as f:
# pickle.dump(sda, f, pickle.HIGHEST_PROTOCOL)
test_model(parameters_dict=trained_parameters,
input_dim=sda.output_dim,
output_dim=3,
x_test_filepath=TRANSFORMED_PATH,
y_test_filepath=Y_TEST_PATH,
output_filepath=OUTPUT_PATH)
def full_test():
sess = tf.Session()
sda = SDAutoencoder(dims=[4000, 400, 400, 400],
activations=["sigmoid", "sigmoid", "sigmoid"],
sess=sess,
noise=0.20,
loss="cross-entropy",
pretrain_lr=1e-6,
finetune_lr=1e-5,
batch_size=50,
print_step=500)
sda.pretrain_network(X_TRAIN_PATH, epochs=50)
trained_parameters = sda.finetune_parameters(X_TRAIN_PATH, Y_TRAIN_PATH, output_dim=2, epochs=80)
sda.write_encoded_input(TRANSFORMED_PATH, X_TEST_PATH)
sda.save_variables(VARIABLE_SAVE_PATH)
sess.close()
test_model(parameters_dict=trained_parameters,
input_dim=sda.output_dim,
output_dim=2,
x_test_filepath=TRANSFORMED_PATH,
y_test_filepath=Y_TEST_PATH,
output_filepath=OUTPUT_PATH)