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())