print("Dataset: " + conf['data_path'])
print("Model: " + conf['model_path'])
print("Evaluating with " + data_type + " a " + complexity + " " +
net_type + " model")
# Load data
if data_type == "Functions_dataset":
parameters, test_set = func_utils.read_function_data(conf['data_path'])
gap = float(parameters[0][3])
dim = None
print('Puting the test data into the right shape...')
testX, testY = func_utils.reshape_function_data(test_set)
to_test_net = Net.Mlp(model_file=conf['model_path'], framework="keras")
elif data_type == "Vectors_dataset":
parameters, test_set = vect_utils.read_vector_data(conf['data_path'])
gap = parameters.iloc[0]['gap']
dim = None
print('Puting the test data into the right shape...')
testX, testY = vect_utils.reshape_vector_data(test_set)
if net_type == "NOREC":
to_test_net = Net.Convolution1D(model_file=conf['model_path'],
framework="keras")
else:
to_test_net = Net.Lstm(model_file=conf['model_path'],
framework="keras")
filename = root + '/' + parameters[0][4] + '_' + parameters[0][3] + '_' + parameters[0][5] + '_Predictor'
# Put the train data into the right shape
trainX, trainY = func_utils.reshape_function_data(train_set)
# Put the validation data into the right shape
valX, valY = func_utils.reshape_function_data(val_set)
train_data = [trainX, trainY]
val_data = [valX, valY]
# Model settings
in_dim = trainX.shape[1:]
out_dim = 1
to_train_net = Net.Mlp(activation=activation, loss=loss, dropout=dropout,
drop_percentage=drop_percentage, input_shape=trainX[0].shape,
output_shape=out_dim, data_type="Function", framework="keras")
elif data_type == 'Vectors_dataset':
print('Training with vectors')
loss = conf['vect_loss']
# Load data
channels = False
batch_data = False
_, train_set = vect_utils.read_vector_data(data_dir + 'train/samples')
_, val_set = vect_utils.read_vector_data(data_dir + 'val/samples')
filename = root
# Put the train data into the right shape
trainX, trainY = vect_utils.reshape_vector_data(train_set)
