def build_network(self, layer_type, activation_function, layers=1):
prev_layer = InputLayer(self.input_size)
for l in range(layers):
prev_layer = prev_layer >> layer_type(self.output_size, act_func=activation_function)
net = build_net(prev_layer)
net.param_buffer = rnd.randn(net.get_param_size())
return net
def build_network(self, layer_type, activation_function, layers=1):
prev_layer = InputLayer(self.input_size)
for l in range(layers):
prev_layer = prev_layer >> layer_type(self.output_size,
act_func=activation_function)
net = build_net(prev_layer)
net.param_buffer = rnd.randn(net.get_param_size())
return net
def setUp(self):
self.input_size = 2
self.output_size = 3
self.timesteps = 10
self.batch_size = 4
self.net = build_net(InputLayer(self.input_size) >> ClockworkLayer(self.output_size))
self.net.param_buffer = np.ones(self.net.get_param_size())*2 #rnd.randn(self.net.get_param_size()) * 0.1
self.net.get_param_view_for('ClockworkLayer')['Timing'][:] = [1, 2, 3]
self.X = rnd.randn(self.timesteps, self.batch_size, self.input_size)
def setUp(self):
self.input_size = 2
self.output_size = 3
self.timesteps = 10
self.batch_size = 4
self.net = build_net(
InputLayer(self.input_size) >> ClockworkLayer(self.output_size))
self.net.param_buffer = np.ones(self.net.get_param_size(
)) * 2 #rnd.randn(self.net.get_param_size()) * 0.1
self.net.get_param_view_for('ClockworkLayer')['Timing'][:] = [1, 2, 3]
self.X = rnd.randn(self.timesteps, self.batch_size, self.input_size)
def setUp(self):
self.net = build_net(InputLayer(1) >> ForwardLayer())
