from array_module import reshape_input
from csv_module import load_execution_files
#linux
#base_directory = '/home/jadson/git/deeplearning/data'
#macos
base_directory = '/Users/jadson/git/deeplearning/data'
lstm_model = base_directory + '/lstm_model.h5'
# size of the data set
sample_number = 1
timesteps = 100
features = 1
# load model from single file
model = load_model(lstm_model)
print(' ------- loading traning data ------ ')
x = load_execution_files(base_directory, sample_number)
x = reshape_input(x, 1, timesteps,
features) # 256 samples x 100 timesteps x 1 features
print(x)
# make predictions
yhat = model.predict(x, verbose=0)
#yhat = model.predict_classes(x, verbose=1)
print(yhat)
classes = 10
#configuration of lstm
lstm_layer_size = 100
dence_layer_size = 10
batch_size = 16
epochs = 100
# ======================================================
# ============= LOAD THE TRANING DATA FROM THE /data/training ===============
print(' ------- loading traning data ------ ')
x_train = load_traning_files(training_directory, traning_samples)
x_train3d = reshape_input(x_train, traning_samples, timesteps,
features) # 256 samples x 100 timesteps x 1 features
print(' ------- loading traning output data ------ ')
y_train = load_traning_output_files(
training_directory, traning_samples) # 256 samples x 10 classes
# ============= LOAD THE TEST DATA FROM THE /data/tests ===============
print(' ------- loading test data ------ ')
x_test = load_test_files(test_directory,
test_samples) # 100 timesteps x 1 feature
x_test3d = reshape_input(x_test, test_samples, timesteps,
features) # 10 samples x 100 timesteps x 1 feature
#features_indexes = [0,1,2,3,5]
lstm_layer_size = 32
dence_layer_size = 10
batch_size = 64
epochs = 10
# ======================================================
# ==================== prepare LSTM data ===============
#prepareLSTMdata(raw_data_file_name, deep_network_file_name, features_indexes)
cvs_raw_data = load_csv_data2(deep_network_file_name)
#print(cvs_raw_data)
x_train = reshape_input(cvs_raw_data, samples, timesteps,
features) # 1 x 10 x 5
#print('### 4 ###')
#print(lstm_input_data)
y_train = np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9,
1.0]]) # 1 x 10
# 5 features, 10 timesteps
x_test = array([[0.1, 1.0, 0.1, 1.0, 0.0], [0.2, 0.9, 0.1, 1.0, 0.0],
[0.3, 0.8, 0.1, 1.0, 0.0], [0.4, 0.7, 0.1, 1.0, 0.0],
[0.5, 0.6, 0.1, 1.0, 0.0], [0.6, 0.5, 0.1, 1.0, 0.0],
[0.7, 0.4, 0.1, 1.0, 0.0], [0.8, 0.3, 0.1, 1.0, 0.0],
[0.9, 0.2, 0.1, 1.0, 0.0], [1.0, 0.1, 0.1, 1.0, 0.0]])
x_test = reshape_input(x_test, samples, timesteps, features) # 1 x 10 x 5