def get_data_all(path_train, path_test, array_max):
data_all = Dataset_csv(path_data=path_train, max_value=array_max)
data_all.set_minibatch(data_all.total_inputs)
X_train, y_train = data_all.generate_batch()
data_all = Dataset_csv(path_data=path_test, max_value=array_max)
data_all.set_minibatch(data_all.total_inputs)
X_test, y_test = data_all.generate_batch()
return X_train, X_test, y_train, y_test, len(y_train), len(y_test)
def get_data_all(path_data, array_max):
data_all = Dataset_csv(path_data=path_data, max_value=array_max)
data_all.set_minibatch(data_all.total_inputs)
X_data, y_data = data_all.generate_batch()
return X_data, y_data, len(y_data)
def get_data_split(path_data, array_max, test_size=0.3):
data_all = Dataset_csv(path_data=path_data, max_value=array_max)
data_all.set_minibatch(data_all.total_inputs)
data, label = data_all.generate_batch()
X_train, X_test, y_train, y_test = model_selection.train_test_split(
data, label, test_size=test_size, random_state=42)
return X_train, X_test, y_train, y_test, len(y_train), len(y_test)
data_test = Dataset_csv(path_data=path_data_test,
minibatch=mini_batch_test,
max_value=Damax,
random=False)
# data_test = Dataset_csv(path_data=path_data_train, minibatch=mini_batch_train, max_value=Damax, random=False)
with tf.Session() as sess:
x_batch = tf.placeholder(tf.float32, [None, 4096])
mask = tf.placeholder(tf.float32, [None, 4096])
noise_mode = tf.placeholder(tf.bool)
AEncode = AE.AEncoder(path_load_weight,
learning_rate=learning_rate,
noise=noise_level)
AEncode.build(x_batch, mask, noise_mode, [2048, 1024])
sess.run(tf.global_variables_initializer())
print('Original Cost: ', test_model(AEncode, sess, data_test))
train_model(AEncode,
sess,
data_train,
objDatatest=data_test,
epoch=epoch)
# SAVE WEIGHTs
AEncode.save_npy(sess, path_save_weight)
# Plot example reconstructions
plot_result(AEncode, sess, data_train.generate_batch()[0])
# Damax = data_normal.amax
# del data_normal
# utils.generate_max_csvData([path_data_train_all[0], path_data_test_all[0]], path+'maximo.csv', has_label=True)
Damax = utils.load_max_csvData(path + 'maximo.csv')
c = tf.ConfigProto()
c.gpu_options.visible_device_list = "1,2"
print('SEARCH SAMPLES')
print('--------------')
data = Dataset_csv(path_data=path_data_test_all,
minibatch=1,
max_value=Damax,
restrict=False,
random=False)
with tf.device('/cpu:0'):
with tf.Session(config=c) as sess:
aencoder = AE.ae_multiClass(session=sess,
npy_weight_paths=path_weight_ae,
num_class=num_class)
aencoder.build(dim_input=dim_input, layers=layers)
for i in range(data.total_batchs_complete):
x, label = data.generate_batch()
res = aencoder.search_sample(sample=x)
data.next_batch_test()
print(res, label)
minibatch=mini_batch_train,
max_value=Damax,
random=False)
with tf.Session() as sess:
x_batch = tf.placeholder(tf.float32, [None, 4096])
CAEncode = CAE.ConvAEncoder(path_load_weight,
learning_rate=learning_rate)
CAEncode.build(input_batch=x_batch,
n_filters=[1, 10, 10],
corruption=False)
sess.run(tf.global_variables_initializer())
print('Original Cost: ', test_model(CAEncode, sess, data_test))
train_model(CAEncode,
sess,
data_train,
objDatatest=data_test,
epoch=epoch)
# SAVE WEIGHTs
CAEncode.save_npy(sess, path_save_weight)
# Plot example reconstructions
plot_result(CAEncode,
sess,
data_train.generate_batch()[0],
dim=(64, 64))