def populate(self, in_size, out_layer, hidden_layers=[]):
"""
initializes the layers according to size parameters
@in_size input size for first lstm layer
@out_size output size for output layer
@memory_sizes sizes for state vectors of lstm layers
"""
layers = hidden_layers
layers.append(out_layer)
self.training_layers.append(CachingNeuralLayer(
in_size=in_size, # input size equals output(=memory) size of preceding layer
out_size=layers[0]["size"],
activation_fn=layers[0]["fn"],
activation_fn_deriv=layers[0]["fn_deriv"]
))
# for each size in memory_sizes create another lstm layer
for i in range(1, len(layers)):
self.training_layers.append(CachingNeuralLayer(
in_size=self.training_layers[-1].size, # input size equals output(=memory) size of preceding layer
out_size=layers[i]["size"],
activation_fn=layers[i]["fn"],
activation_fn_deriv=layers[i]["fn_deriv"]
))
# mark network as populated
Logger.log(str(len(self.training_layers)) + " layers created.")
self.populated = True
def learn(self, deltas, learning_rate=0.001):
"""
apply learning algorithm by using deltas from next layer
@deltas deltas from last (actually next) layer
"""
Logger.debug("learn(" + str(deltas) + ")")
# learn recursively over all caches (corresponds to time steps), starting with last cache
self.learn_recursive(self.last_cache.predecessor, deltas)
# apply pending weight and bias updates
self.apply_training(learning_rate)
# calculate and return deltas for this layer from losses
deltas = [cache.loss_input for cache in self.caches]
return deltas
def feed(self, input_data, time_steps=1):
"""
calculate output vector for given input vector
@time_steps number of feedforward results to be cached for use in backpropagation through time
"""
assert time_steps > 0, "time_steps must be at least 1 (for recursive input)!"
Logger.debug("feed(" + str(input_data) + ")")
# concatenate input_vector with recurrent input (last output) vector
concat_in = np.concatenate([input_data, self.last_cache.predecessor.output_values])
# delete first cache if maximum number of time_steps are already cached
if time_steps <= len(self.caches):
self.first_cache.successor.remove()
self.caches.pop(0)
# create new cache at end of cache list
self.last_cache.insert_before(LSTMLayerCache())
cache = self.last_cache.predecessor
self.caches.append(cache)
# cache input and concatenated input values
cache.input_values = input_data
cache.concatenated_input = concat_in
# calculate and cache gate/update_values results
cache.forget_gate_results = self.forget_gate_layer.feed(concat_in)
cache.input_gate_results = self.input_gate_layer.feed(concat_in)
cache.update_values_layer_results = self.update_values_layer.feed(concat_in)
cache.output_gate_results = self.output_gate_layer.feed(concat_in)
# calculate state update values
update_values = np.multiply(
cache.input_gate_results,
cache.update_values_layer_results)
# apply forget layer and apply state update values
cache.state = cache.predecessor.state * cache.forget_gate_results \
+ update_values
# calculate output from new state and output gate
cache.output_values = Layer.activation_tanh(cache.state) * cache.output_gate_results
# return calculated output vector
return cache.output_values
def learn_rec(self, target, layer_id):
Logger.debug("learn recursive: " + str(layer_id))
if layer_id > len(self.training_layers):
raise Exception("invalid layer id!")
elif layer_id == len(self.training_layers):
return [target - self.training_layers[-1].caches[0].output_values]
else:
last_deltas = self.learn_rec(target, layer_id+1)
delta = self.training_layers[layer_id].get_delta(
in_data=self.training_layers[layer_id].caches[0].input_values,
delta=last_deltas[0],
predecessor_activation_deriv=self.training_layers[layer_id-1].activation_deriv)
self.training_layers[layer_id].learn(
result=self.training_layers[layer_id].caches[0].output_values.T,
delta=last_deltas[0].T,
learning_rate=self.config['learning_rate']
)
deltas = [delta]
deltas.extend(last_deltas)
return deltas
def learn(self, target):
"""
Calculates the weight updates depending on the loss function derived to the individual weights.
Requires feedforwarding to be finished for backpropagation through time.
@targets expected outputs to be compared to actual outputs
"""
if not self.populated:
raise Exception("MLP Network needs to be populated first! Have a look at MLPNetwork.populate().")
Logger.debug("mlpnetwork:learn")
Logger.debug("target: " + str(target))
Logger.debug("result: " + str(self.training_layers[-1].caches[0].output_values))
Logger.debug("out_size: " + str(self.training_layers[-1].size))
Logger.debug("out_weights: " + str(self.training_layers[-1].weights))
# calculate output error
deltas = [target - self.training_layers[-1].caches[0].output_values]
rng = range(len(self.training_layers) - 2, -1, -1)
#Logger.log("range: " + str(rng))
#Logger.log("reversed range: " + str(reversed(rng)))
#raw_input("press Enter")
for layer_id, last_weights in reversed(zip(
rng,
[l.weights for l in self.training_layers[1:]]
)):
Logger.debug(str(layer_id) + "/" + str(len(self.training_layers) - 2))
deltas.append(
self.training_layers[layer_id].get_delta(
self.training_layers[layer_id].caches[0].output_values,
deltas[-1],
self.training_layers[layer_id + 1].weights
)
)
error = deltas[0][0]**2
Logger.debug("deltas: " + str(deltas))
for delta, layer in zip(reversed(deltas), self.training_layers):
layer.learn(layer.caches[0].input_values, delta, self.config["learning_rate"])
#self.bias_1 = min(0, -(learning_rate * delta1) + self.bias_1)
#self.bias_2 = min(0, -(learning_rate * delta2) + self.bias_2)
# returns the absolute error (distance of target output and actual output)
return error
"""
from neural_network.util import Logger
from neural_network.NeuralLayer import NeuralLayer as Layer
from multi_layer_perceptron.MLPNetwork import MLPNetwork
Logger.DEBUG = False
data_in = numpy.array([[[0], [0]], [[0], [1]], [[1], [0]], [[1], [1]]])
data_out_xor = numpy.array([[0], [1], [1], [0]])
data_out_and = numpy.array([[0], [0], [0], [1]])
data_out_or = numpy.array([[0], [1], [1], [1]])
data_out = data_out_xor
Logger.log("data_shape: " + str(numpy.shape(data_in)))
Logger.log("data[0]_shape: " + str(numpy.shape(data_in[0])))
mlp = MLPNetwork()
mlp.populate(
in_size=2,
out_layer= {
"size": 1,
"fn": None,
"fn_deriv": None
},
hidden_layers=[{
"size": 3,
"fn": Layer.activation_sigmoid,
"fn_deriv": Layer.activation_sigmoid_deriv
