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 main():
sess = tf.Session()
sda = SDAutoencoder(dims=[784, 500],
activations=["sigmoid"],
sess=sess,
noise=0.40,
loss="cross-entropy")
mnist_train_gen_f = lambda: get_mnist_batch_xs_generator(True, batch_size=100, batch_limit=12000)
sda.pretrain_network_gen(mnist_train_gen_f)
trained_parameters = sda.finetune_parameters_gen(
get_mnist_batch_generator(True, batch_size=100, batch_limit=18000), output_dim=10)
transformed_filepath = "../data/mnist_test_transformed.csv"
test_ys_filepath = "../data/mnist_test_ys.csv"
output_filepath = "../data/mnist_pred_ys.csv"
sda.write_encoded_input_with_ys(transformed_filepath, test_ys_filepath,
get_mnist_batch_generator(False, batch_size=100, batch_limit=100))
sess.close()
test_model(parameters_dict=trained_parameters,
input_dim=sda.output_dim,
output_dim=10,
x_test_filepath=transformed_filepath,
y_test_filepath=test_ys_filepath,
output_filepath=output_filepath)
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 sda():
sess = tf.Session()
sda = SDAutoencoder(dims=[784, 400, 200, 80],
activations=["sigmoid", "sigmoid", "sigmoid"],
sess=sess,
noise=0.20,
loss="cross-entropy",
pretrain_lr=0.0001,
finetune_lr=0.0001)
mnist_train_gen_f = lambda: get_mnist_batch_xs_generator(
True, batch_size=100, batch_limit=12000)
# pretrain locally, layer by layer
sda.pretrain_network_gen(mnist_train_gen_f)
# fine-tune the model by training all the weights
trained_parameters = sda.finetune_parameters_gen(get_mnist_batch_generator(
True, batch_size=100, batch_limit=18000),
output_dim=10)
mainDir = data_storage_path
if not os.path.exists(mainDir):
os.makedirs(mainDir)
transformed_filepath = mainDir + "/mnist_test_transformed.csv"
test_ys_filepath = mainDir + "/mnist_test_ys.csv"
output_filepath = mainDir + "/mnist_pred_ys.csv"
# for testing. write the encoded x value along with y values to csv
sda.write_encoded_input_with_ys(
transformed_filepath, test_ys_filepath,
get_mnist_batch_generator(False, batch_size=100, batch_limit=100))
sess.close()
test_model(parameters_dict=trained_parameters,
input_dim=sda.output_dim,
output_dim=10,
x_test_filepath=transformed_filepath,
y_test_filepath=test_ys_filepath,
output_filepath=output_filepath)
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 wrap_sensor(X=[]):
sess = tf.Session()
# Restore variables from disk.
new_saver = tf.train.import_meta_graph(VARIABLE_SAVE_PATH + ".meta")
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('../data/deephand/'))
weight = openData(WEIGHT_PATH)
biases = openData(BIASES_PATH)
#write encoded
sda = SDAutoencoder(dims=[160, 80, 30, 5],
activations=["sigmoid", "sigmoid", "sigmoid"],
sess=sess,
noise=0.2,
loss="rmse",
pretrain_lr=1e-5,
finetune_lr=1e-3,
batch_size=1,
print_step=1)
print(len(sda.hidden_layers))
var = tf.all_variables()
encW = []
encB = []
for v in var:
# print(v.name)
# print(sess.run(v))
# if(v.name[0:13]=="hidden_layer_" and v.name[] =="encoding_vars")
n = v.name.split("/")
if (len(n) == 3):
if (n[1][0:13] == "hidden_layer_" and n[0] == "finetuning"):
ind = int(n[1][13:15])
val = sess.run(v)
if (n[2] == "weights:0"):
sda.hidden_layers[ind].set_weights(val)
else:
sda.hidden_layers[ind].set_biases(val)
trained_parameters = {"weights": weight, "biases": biases}
if (len(X) == 0):
X = openData("../data/TestX.csv")
X = X[14]
print(type(X), X)
Transformed = sda.transformX(X)
print(Transformed)
c = get_class(parameters_dict=trained_parameters,
input_dim=5,
output_dim=3,
data=[Transformed])
print(c)
cS = []
from HandCommand import Hand
hand = Hand()
for x in range(len(c)):
if (c[x] > 0.49999):
cS.append(1)
else:
cS.append(0)
if (cS[0] == 1):
hand.lateral()
elif (cS[1] == 1):
hand.fist()
elif (cS[2] == 1):
hand.grasp()
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 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()