def feed(self, data):
# apply forget gate
self.state = np.dot(self.state, self.forget_gate_layer.feed(data))
# calculate state update values
update_values = np.dot(self.input_gate_layer.feed(data),
self.update_values_layer.feed(data))
# apply state update values
self.state = self.state + update_values
# calculate output from new state and output gate
output = np.dot(Layer.activation_tanh(self.state),
self.output_gate_layer.feed(data))
return output
def feed(self, data):
# apply forget gate
self.state = np.dot(self.state, self.forget_gate_layer.feed(data))
# calculate state update values
update_values = np.dot(
self.input_gate_layer.feed(data),
self.update_values_layer.feed(data)
)
# apply state update values
self.state = self.state + update_values
# calculate output from new state and output gate
output = np.dot(
Layer.activation_tanh(self.state),
self.output_gate_layer.feed(data))
return output
def __init__(self, in_size, out_size):
self.forget_gate_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.input_gate_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of update_values_layer (transposed state)
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.update_values_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_tanh,
activation_fn_deriv=Layer.activation_tanh_deriv)
self.output_gate_layer = Layer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.state = None
def __init__(self, in_size, out_size):
self.forget_gate_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.input_gate_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of update_values_layer (transposed state)
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.update_values_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_tanh,
activation_fn_deriv=Layer.activation_tanh_deriv)
self.output_gate_layer = Layer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.state = None
class LSTMLayer(object):
def __init__(self, in_size, out_size):
self.forget_gate_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.input_gate_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of update_values_layer (transposed state)
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.update_values_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_tanh,
activation_fn_deriv=Layer.activation_tanh_deriv)
self.output_gate_layer = Layer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.state = None
def feed(self, data):
# apply forget gate
self.state = np.dot(self.state, self.forget_gate_layer.feed(data))
# calculate state update values
update_values = np.dot(
self.input_gate_layer.feed(data),
self.update_values_layer.feed(data)
)
# apply state update values
self.state = self.state + update_values
# calculate output from new state and output gate
output = np.dot(
Layer.activation_tanh(self.state),
self.output_gate_layer.feed(data))
return output
class LSTMLayer(object):
def __init__(self, in_size, out_size):
self.forget_gate_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.input_gate_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of update_values_layer (transposed state)
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.update_values_layer = BiasedLayer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_tanh,
activation_fn_deriv=Layer.activation_tanh_deriv)
self.output_gate_layer = Layer(
in_size=in_size, # size of input/last output
out_size=out_size, # size of state
activation_fn=Layer.activation_sigmoid,
activation_fn_deriv=Layer.activation_sigmoid_deriv)
self.state = None
def feed(self, data):
# apply forget gate
self.state = np.dot(self.state, self.forget_gate_layer.feed(data))
# calculate state update values
update_values = np.dot(self.input_gate_layer.feed(data),
self.update_values_layer.feed(data))
# apply state update values
self.state = self.state + update_values
# calculate output from new state and output gate
output = np.dot(Layer.activation_tanh(self.state),
self.output_gate_layer.feed(data))
return output
