std = params_appliance[application]['std']
sess = tf.InteractiveSession()
windowlength = params_appliance[args.appliance_name]['windowlength']
offset = int(0.5 * (params_appliance[application]['windowlength'] - 1.0))
test_kwag = {'inputs': test_set_x, 'targets': ground_truth, 'flatten': False}
# val_kwag = {
# 'inputs': val_set_x,
# 'targets': val_set_y,
# 'flatten':False}
test_provider = DataProvider.MultiApp_Slider(batchsize=batchsize,
shuffle=False,
offset=offset)
# val_provider = DataProvider.DoubleSourceSlider(batchsize = 5000,
# shuffle = False, offset=offset)
x = tf.placeholder(tf.float32, shape=[None, windowlength], name='x')
y_ = tf.placeholder(tf.float32, shape=[None, 1], name='y_')
##### cnn2
network = tl.layers.InputLayer(x, name='input_layer')
network = tl.layers.ReshapeLayer(network, shape=(-1, windowlength, 1, 1))
network = tl.layers.Conv2dLayer(network,
act=tf.nn.relu,
shape=[10, 1, 1, 30],
strides=[1, 1, 1, 1],
padding='SAME',
# load the data set
tra_set_x, tra_set_y, val_set_x, val_set_y = load_dataset()
# get the window length of the training examples
windowlength = 599
sess = tf.InteractiveSession()
offset = int(0.5 * (windowlength - 1.0))
tra_kwag = {'inputs': tra_set_x, 'targets': tra_set_y, 'flatten': False}
val_kwag = {'inputs': val_set_x, 'targets': val_set_y, 'flatten': False}
tra_provider = DataProvider.MultiApp_Slider(batchsize=batchsize,
shuffle=True,
offset=offset)
val_provider = DataProvider.MultiApp_Slider(batchsize=5000,
shuffle=False,
offset=offset)
x = tf.placeholder(tf.float32, shape=[None, windowlength], name='x')
y_ = tf.placeholder(tf.float32, shape=[None, 6], name='y_')
network = tl.layers.InputLayer(x, name='input_layer')
network = tl.layers.ReshapeLayer(network, shape=(-1, windowlength, 1, 1))
network = tl.layers.Conv2dLayer(network,
act=tf.nn.relu,
shape=[10, 1, 1, 30],
strides=[1, 1, 1, 1],
padding='SAME',