class Net:
def __init__(self, method=Method.Sigmoid):
self.weights = [] # Current weights
self.old_weights = [] # Last time weights
self.output = 0.0 # Neuron output
self.inputted_features = [] # Inputted features
self.summed_signal = 0.0 # Summed singal (the summation of input)
self.learning_rate = 0.8 # Learning rate
self.activition = Activation() # Activation function
# 加总信号
def summarize_inputs(self, features=[]):
if not features:
return 0.0
self.inputted_features = copy.deepcopy(features)
self.summed_signal = np.dot(self.inputted_features,
self.weights) # a = X * W
self.output = self.activition.activate(self.summed_signal) # b = f(a)
return self.output
# 更新权重
def update_weights(self, error_value=0.0):
self.old_weights = copy.deepcopy(self.weights)
for index, old_weight in enumerate(self.old_weights):
# new weight = old weight + learning rate * error_value(i) * input
new_weight = old_weight + self.learning_rate * error_value * self.inputted_features[
index]
self.weights[index] = new_weight
def differential_activition(self):
return self.activition.differentiate(self.output)
class Net(object):
# Read, Write
activation_method = None
# Readonly
output_value = None # 输出值
output_partial = None
previous_output = None
previous_output_partial = None
def __init__(self, has_recurrent=False):
self.weights = [] # <number>
self.recurrent_weights = [] # <number>
self.bias = 0.0
self.delta_value = 0.0 # Current delta value will be next delta value.
self.has_recurrent = has_recurrent # Has recurrent inputs ? (hidden net has recurrent, but output net not.
self.activation = Activation(
) # 活化函式的 Get, Set 都在这里： net.activation.method.
# 有另外开 self.activation_method 来方便存取
self.output = NetOutput()
self.timesteps = [] # <Timestep Object>
# weights<number>
def reset_weights(self, weights=[]):
if not weights:
return
self.weights = np.copy(weights).tolist()
# recurrent_weights<number>
def reset_recurrent_weights(self, recurrent_weights=[]):
if not recurrent_weights:
return
self.recurrent_weights = np.copy(recurrent_weights).tolist()
def weight_for_index(self, index=0):
return self.weights[index]
def recurrent_weight_for_index(self, index=0):
return self.recurrent_weights[index]
# bias 也在这里归零
def remove_all_weights(self):
del self.weights[:]
del self.recurrent_weights[:]
self.bias = 0.0
# Randomizing weights and bias.
def randomize_weights(self, random_count=1, min=-0.5, max=0.5):
del self.weights[:]
self.bias = 0.0
random = np.random
for i in range(0, random_count):
self.weights.append(random.uniform(min, max))
self.bias = random.uniform(min, max)
def randomize_recurrent_weights(self, random_count=1, min=-0.5, max=0.5):
if self.has_recurrent:
del self.recurrent_weights[:]
random = np.random
for i in range(0, random_count):
self.recurrent_weights.append(random.uniform(min, max))
# Net output. (Hidden net with recurrent, Output net without recurrent)
def net_output(self, inputs=[], recurrent_outputs=[]):
# 先走一般前馈（Forward）至 Hidden Layer
summed_singal = np.dot(inputs, self.weights) + self.bias
# 如果有递归层，再走递归（Recurrent) 至 Hidden Layer
if len(recurrent_outputs) > 0:
summed_singal += np.dot(recurrent_outputs, self.recurrent_weights)
# 神经元输出
output_value = self.activation.activate(summed_singal)
self.output.add_sum_input(summed_singal)
self.output.add_output_value(output_value)
return output_value
def clear(self):
self.output.refresh()
# For hidden layer nets to calculate their delta weights with recurrent layer,
# and for output layer nets to calculate their delta weights without recurrent layer.
# layer_outputs: hidden layer outputs or output layer outputs.
def calculate_delta_weights(self,
learning_rate=1.0,
layer_outputs=[],
recurrent_outputs=[]):
# 利用 Timestep 来当每一个 BP timestep 算权重修正值时的记录容器
timestep = Timestep()
# For delta bias.
timestep.delta_bias = learning_rate * self.delta_value
# For delta of weights.
for weight_index, weight in enumerate(self.weights):
# To calculate and delta of weight.
last_layer_output = layer_outputs[weight_index]
# SGD: new w = old w + (-learning rate * delta_value * x)
# -> x 可为 b[t][h] (hidden output) 或 b[t-1][h] (recurrent output) 或 x[i] (input feature)
# Output layer 的 delta_value = aE/aw[hk] = -error value * f'(net)
# Hidden layer 的 delta_value = aE/aw[ih] = SUM(delta_value[t][hk] * w[hk] + SUM(delta_value[t+1][h'h] * w
delta_weight = learning_rate * self.delta_value * last_layer_output
timestep.add_delta_weight(delta_weight)
# For delta of recurrent weights. (Noted: Output Layer is without Recurrent)
for recurrent_index, recurrent_weight in enumerate(
self.recurrent_weights):
last_recurrent_output = recurrent_outputs[recurrent_index]
recurrent_delta_weight = learning_rate * self.delta_value * last_recurrent_output
timestep.add_recurrent_delta_weight(recurrent_delta_weight)
self.timesteps.append(timestep)
def renew_weights(self, new_weights=[], new_recurrent_weights=[]):
if not new_weights and not new_recurrent_weights:
return
self.remove_all_weights()
self.reset_weights(new_weights)
self.reset_recurrent_weights(new_recurrent_weights)
def renew_bias(self):
sum_changes = 0.0
for timestep in self.timesteps:
sum_changes += timestep.delta_bias
# new b(j) = old b(j) + [-L * -delta(j)]
self.bias += sum_changes
# Renew weights and bias.
def renew(self):
# 累加每个 Timestep 里相同 Index 的 Delta Weight
new_weights = []
new_recurrent_weights = []
for weight_index, weight in enumerate(self.weights):
# For normal weight.
sum_delta_changes = 0.0
for timestep in self.timesteps:
sum_delta_changes += timestep.delta_weight(weight_index)
new_weight = weight + sum_delta_changes
new_weights.append(new_weight)
for recurrent_index, recurrent_weight in enumerate(
self.recurrent_weights):
# for recurrent weight.
sum_recurrent_changes = 0.0
for timestep in self.timesteps:
sum_recurrent_changes += timestep.recurrent_delta_weight(
recurrent_index)
new_recurrent_weight = recurrent_weight + sum_recurrent_changes
new_recurrent_weights.append(new_recurrent_weight)
self.renew_weights(new_weights, new_recurrent_weights)
self.renew_bias()
del self.timesteps[:]
self.delta_value = 0.0 # 一定要归零， 因为走 BPTT 的原故
'''
@ Getters that all Readonly
'''
@property
# The last output value from output_values.
def output_value(self):
return self.output.last_output_value
@property
# The last output value partial from output_values.
def output_partial(self):
# 是线性输出的活化函式（e.g. SGN, ReLU ... etc.），必须用输入信号（Sum Input）来求函式偏微分
# 非线性的活化函式则用输出值(Output Value)来求偏微。
output_value = self.output.last_sum_input if self.activation.is_linear == True else self.output_value
return self.activation.partial(output_value)
@property
# The last moment output. e.g. b[t-1][h]
def previous_output(self):
return self.output.previous_output
@property
# 上一刻的输出偏微分
def previous_output_partial(self):
output_value = self.output.previous_sum_input if self.activation.is_linear == True else self.previous_output
return self.activation.partial(output_value)
@property
def activation_method(self):
return self.activation.method
'''
@ Setter
'''
@activation_method.setter
def activation_method(self, method):
self.activation.method = method
class Neuron:
# Private usage.
__iteration_times = 0 # 迭代次数
__iteration_error = 0.0 #迭代误差总和
def __init__(self):
self.tag = self.__class__.__name__
self.samples = [] # 所有的训练样本（特征值)
self.targets = [] # 范本的目标输出
self.weights = [] # 权重
self.bias = 0.0 # 偏权值
self.learning_rate = 1.0 # 学习速率
self.max_iteration = 1 # 最大迭代数
self.convergence = 0.001 # 收敛误差
self.activation = Activation()
# Iteration Cost Function: 每个完整迭代运算后，把每一个训练样本的cost function取平均（用于判断是否收敛）
def _iteration_cost_function(self):
# 1/2 * (所有训练样本的cost function总和 / （训练样本数量 * 每笔训练样本的目标输出数量))
return 0.5 * (self.__iteration_error / (len(self.samples) * 1))
# 训练样本的Cost Function: 由于会在 _iteration_cost_function()计算迭代的cost function时去统一除 1/2。
# 故在这里计算训练样本的cost function 时不除以 1/2。
def _cost_function(self, error_value=0.0):
self.__iteration_error += (error_value**2)
def _net_input(self, features=[]):
return np.dot(features, self.weights)
def _net_output(self, net_input=0.0):
return self.activation.activate(net_input)
def _start(self, iteration, completion):
self.__iteration_times += 1
self.__iteration_error += 0.0
# 这里刻意反每一个步骤都写出来，一步步的代算清楚流程
for index, features in enumerate(self.samples):
# Forward
target_value = self.targets[index]
net_input = self._net_input(features)
net_output = self._net_output(net_input)
# Backward
error_value = target_value - net_output
derived_activation = self.activation.partial(net_output)
# Calculates cost function of the training sample.
self._cost_function(error_value)
# Updates all weights, the formula:
# delta_value = -(target value - net output) * f'(net)
# delta_weight = -learning rate * delta_value * x1 (Noted: 这里 learning rate 和 delta_value的负号会相）
# new weights, e.g. new s1 = old w1 + delta_weight w1
delta_value = error_value * derived_activation
delta_weights = np.multiply(self.learning_rate * delta_value,
features)
new_weights = np.add(self.weights, delta_weights)
self.weights = new_weights
# Finished an iteration then adjusts conditions
if (self.__iteration_times >= self.max_iteration) or (
self._iteration_cost_function() <= self.convergence):
if not completion is None:
completion(self.__iteration_times, self.weights)
else:
if not iteration is None:
iteration(self.__iteration_times, self.weights)
self._start(iteration, completion)
# One training sample: features -> one target
def add_pattern(self, features=[], target=0):
# If features is not an array that still working on here
if not features:
return
# samples[features array]
# targets[target value]
self.samples.append(features)
self.targets.append(target)
def initialize_weights(self, weights=[]):
if not weights:
return
self.weights = weights
# 全零的初始权重
def zero_weights(self):
if not self.samples:
return
length = len(self.samples[0])
for i in range(length):
self.weights.append(0.0)
def randomize_weights(self, min=0.0, max=1.0):
# Float
random = np.random
input_count = len(self.samples[0])
weights = []
for i in range(0, input_count):
weights.append(random.uniform(min, max))
self.initialize_weights(weights)
# iteration and completion are callback functions
def training(self, iteration, completion):
self.__iteration_times = 0
self.__iteration_error = 0.0
self._start(iteration, completion)
def predict(self, features=[]):
return self._net_output(self._net_input(features))
class RNN:
def __init__(self,
input_layer_size,
state_layer_size,
state_layer_activation,
output_layer_size,
output_layer_activation,
epochs=100,
bptt_truncate=None,
learning_rule='bptt',
kernel=None,
eta=0.001,
rand=None,
verbose=0):
"""
Notes:
U - weight matrix from input into hidden layer.
W - weight matrix from hidden layer to hidden layer.
V - weight matrix from hidden layer to output layer.
Inputs:
input_size:
Size of the input vector. We expect a 2D numpy array, so this should be X.shape[1]
state_layer_size:
State layer size.
state_layer_activation:
A string. Refer to activation.py
output_size:
Size of the output vector. We expect a 2D numpy array, so this should be Y.shape[1]
output_layer_activation:
A string. Refer to activation.py
epochs(opt):
Number of epochs for a single training sample.
learning_rule(opt):
Choose between 'bptt' and 'modified'
bptt_truncate(opt):
If left at None, back propagation through time will be applied for all time steps.
Otherwise, a value for bptt_truncate means that
bptt will only be applied for at most bptt_truncate steps.
Only considered when learning_rule == 'bptt'
kernel(opt):
# TODO - fill this
Only considered when learning_rule == 'modified'
eta (opt):
Learning rate. Initialized to 0.001.
rand (opt):
Random seed. Initialized to None (no random seed).
verbose (opt):
Verbosity: levels 0 - 2
Outputs:
None
"""
np.random.seed(rand)
self.learning_rule = learning_rule.lower()
if self.learning_rule == 'bptt':
self.gradient_function = self.bptt
elif self.learning_rule == 'modified':
self.gradietn_function = self.modified_learning_rule
else:
raise ValueError
self.input_layer_size = input_layer_size
self.state_layer_size = state_layer_size
self.state_layer_activation = state_layer_activation
self.state_activation = Activation(state_layer_activation)
self.output_layer_size = output_layer_size
self.output_layer_activation = output_layer_activation
self.output_activation = Activation(output_layer_activation)
self.epochs = epochs
self.kernel = kernel
self.bptt_truncate = bptt_truncate
# U - weight matrix from input into state layer.
# W - weight matrix from state layer to state layer.
# V - weight matrix from state layer to output layer.
self.U = np.random.uniform(-np.sqrt(1. / input_layer_size),
np.sqrt(1. / input_layer_size),
(state_layer_size, input_layer_size))
self.V = np.random.uniform(-np.sqrt(1. / state_layer_size),
np.sqrt(1. / state_layer_size),
(output_layer_size, state_layer_size))
self.W = np.random.uniform(-np.sqrt(1. / state_layer_size),
np.sqrt(1. / state_layer_size),
(state_layer_size, state_layer_size))
self.state_bias = np.zeros((state_layer_size, 1))
self.output_bias = np.zeros((output_layer_size, 1))
self.eta = eta
self.verbose = verbose
self.show_progress_bar = verbose > 0
def fit(self, X_train, y_train):
"""
Notes:
Inputs:
X_train:
Training inputs. Expect a list with numpy arrays of size (input_layer_size, N) where N is the number of samples.
Y_train:
Training outputs. Expect a list with numpy arrays of size (output_layer_size, N) where N is the number of samples.
Outputs:
None
"""
eta = self.eta
if self.show_progress_bar:
bar = ProgressBar(max_value=self.epochs)
for epoch in range(self.epochs):
for x, y in zip(X_train, y_train):
dLdU, dLdV, dLdW, dLdOb, dLdSb = self.gradient_function(x, y)
self.U -= eta * dLdU
self.V -= eta * dLdV
self.W -= eta * dLdW
self.output_bias -= dLdOb
self.state_bias -= dLdSb
if self.show_progress_bar:
bar.update(epoch)
def forward_propagation(self, x):
"""
Inputs:
x:
Expect size (T, input_layer_size), where T is the length of time.
Outputs:
o:
The activation of the output layer.
s:
The activation of the hidden state.
"""
T = x.shape[0]
s = np.zeros((T + 1, self.state_layer_size))
o = np.zeros((T, self.output_layer_size))
s_linear = np.zeros((T + 1, self.state_layer_size))
o_linear = np.zeros((T, self.output_layer_size))
state_bias = Convert2DTo1D(self.state_bias)
output_bias = Convert2DTo1D(self.output_bias)
for t in np.arange(T):
state_linear = np.dot(self.U, x[t]) + np.dot(self.W,
s[t - 1]) + state_bias
s_linear[t] = state_linear
s[t] = self.state_activation.activate(state_linear)
output_linear = np.dot(self.V, s[t]) + output_bias
o[t] = self.output_activation.activate(output_linear)
o_linear[t] = output_linear
return (o, s, s_linear, o_linear)
def modified_learning_rule(self, x, y):
"""
Output:
dLdU:
Gradient for U matrix
dLdV:
Gradient for V matrix
dLdW:
Gradient for W matrix
dLdOb:
Gradient for output layer bias
dLdSb:
Gradient for state layer bias
TODO - implement this
"""
raise NotImplementedError
def bptt(self, x, y):
"""
Output:
dLdU:
Gradient for U matrix
dLdV:
Gradient for V matrix
dLdW:
Gradient for W matrix
dLdOb:
Gradient for output layer bias
dLdSb:
Gradient for state layer bias
"""
# TODO - numpy likes to provide 1D matrices instead of 2D, and unfortunately
# we need 2D matrices. Therefore we have a lot of converting 1D to 2D matrices
# and we might want to clean that later somehow...
# TODO - also this can probably be cleaned more.
T = len(y)
assert T == len(x)
if self.bptt_truncate is None:
bptt_truncate = T
else:
bptt_truncate = self.bptt_truncate
o, s, s_linear, o_linear = self.forward_propagation(x)
dLdU = np.zeros(self.U.shape)
dLdV = np.zeros(self.V.shape)
dLdW = np.zeros(self.W.shape)
dLdOb = np.zeros(self.output_bias.shape)
dLdSb = np.zeros(self.state_bias.shape)
num_dU_additions = 0
num_dVdW_additions = 0
delta_o = o - y
for t in reversed(range(T)):
# Backprop the error at the output layer
g = delta_o[t]
o_linear_val = o_linear[t]
state_activation = s[t]
g = Convert1DTo2D(g)
o_linear_val = Convert1DTo2D(o_linear_val)
state_activation = Convert1DTo2D(state_activation)
g = g * self.output_activation.dactivate(o_linear_val)
dLdV += np.dot(g, state_activation.T)
dLdOb += g
num_dU_additions += 1
g = np.dot(self.V.T, g)
# Backpropagation through time for at most bptt truncate steps
for bptt_step in reversed(range(max(0, t - bptt_truncate), t + 1)):
state_linear = s_linear[bptt_step]
state_activation_prev = s[bptt_step - 1]
x_present = x[t]
state_linear = Convert1DTo2D(state_linear)
state_activation_prev = Convert1DTo2D(state_activation_prev)
x_present = Convert1DTo2D(x_present)
g = g * self.state_activation.dactivate(state_linear)
dLdW += np.dot(g, state_activation_prev.T)
dLdU += np.dot(g, x_present.T)
dLdSb += g
num_dVdW_additions += 1
g = g * np.dot(self.W.T, g)
return [
dLdU / num_dU_additions, dLdV / num_dVdW_additions,
dLdW / num_dVdW_additions, dLdOb / num_dU_additions,
dLdSb / num_dVdW_additions
]
def predict(self, X):
"""
Inputs:
X:
Training inputs. Expect a list with numpy arrays of size (input_layer_size, N) where N is the number of samples.
Outputs:
predictions
"""
predictions = []
for x in X:
o, _, _, _ = self.forward_propagation(x)
predictions.append(o)
return predictions
def score(self, X, Y):
"""
Inputs:
X:
Training inputs. Expect a list with numpy arrays of size (input_layer_size, N) where N is the number of samples.
Y:
Training outputs. Expect a list with numpy arrays of size (output_layer_size, N) where N is the number of samples.
Outputs:
MSE
"""
predictions = self.predict(X)
mses = []
for prediction, y in zip(predictions, Y):
mses.append(np.mean((predictions - y)**2))
return np.mean(mses)
class Dense:
# Definiation and Initialization of the Dense Layer
def __init__(self, num_neurons, input_shape):
print(
'Adding Layer: input_shape: {}, number of neurons: {}, output_shape: {}'
.format(input_shape, num_neurons, num_neurons))
# Let's initialize the weights in interval [0,1) for respective synaptic inputs
self.weights = np.random.uniform(low=0, high=1, size=input_shape)
# Lets initialize the biases all with value '1' for every neuron in current layer
self.biases = np.ones(num_neurons)
# Lets initialize the activation_potentials all with value '0' for every neuron in current layer
self.activation_potentials = np.zeros(num_neurons)
# Outputs of this layer
self.outputs = np.zeros(num_neurons)
# Local Gradients of all the neurons in current layer
self.local_gradients = np.zeros(num_neurons)
# And finally the activation function, for non-linearity of outputs
self.activation = Activation()
# Inputs to this layer -> Outputs from previous layer
self.previous_layers_outputs = []
print('Added Layer ... ')
def get_gradients(self):
return self.local_gradients
def get_weights(self):
return self.weights
# The activation potential vector calculator for a given layer
def activation_potential(self, inputs):
self.activation_potentials = np.dot(inputs, self.weights) + self.biases
#Local Gradient
def local_gradient(self, error_at_end, layer, network, next_gradients,
next_weights):
if layer == network[-1]:
# Output layer = error * derivative of activation function
self.local_gradients = np.dot(
error_at_end,
self.activation.activate_derivative(
self.activation_potentials))
else:
# Hidden layers = derivative of activation function * sum of all (derivative of activation
# function of next layer * weights associated with this neuron going to all neurons in next layer)
diff_of_activation_funct = self.activation.activate_derivative(
self.activation_potentials)
self.local_gradients = np.dot(diff_of_activation_funct,
np.dot(next_gradients, next_weights))
# Forward Signal Definition
def forward_signal(self, inputs):
# Calculate activation potentials for all neurons in this layer
self.activation_potential(inputs)
# Activate activation_potentials and save it in self.outputs
self.outputs = self.activation.activate(self.activation_potentials)
# Return the outputs of this layer, will need it in next layer
return self.outputs
# Backward Signal Definition
def backward_signal(self, error_at_end, layer, network, learning_rate,
next_gradients, next_weights, inputs):
# Calculate local_gradients
self.local_gradient(error_at_end, layer, network, next_gradients,
next_weights)
# Update weights
self.weights = np.sum(
self.weights, learning_rate * np.dot(self.local_gradients, inputs))
# Update Biases
self.biases = np.sum(self.biases,
learning_rate * np.dot(self.local_gradients, 1))
class RNN:
def __init__(self,
input_layer_size,
state_layer_size,
state_layer_activation,
output_layer_size,
output_layer_activation,
epochs=100,
bptt_truncate=None,
learning_rule='bptt',
tau=None,
eta=1e-5,
rand=None,
verbose=0):
"""
Notes:
U - weight matrix from input into hidden layer.
W - weight matrix from hidden layer to hidden layer.
V - weight matrix from hidden layer to output layer.
Inputs:
input_size:
Size of the input vector. We expect a 2D numpy array, so this should be X.shape[1]
state_layer_size:
State layer size.
state_layer_activation:
A string. Refer to activation.py
output_size:
Size of the output vector. We expect a 2D numpy array, so this should be Y.shape[1]
output_layer_activation:
A string. Refer to activation.py
epochs(opt):
Number of epochs for a single training sample.
learning_rule(opt):
Choose between 'bptt','fa' or 'modified'
bptt_truncate(opt):
If left at None, back propagation through time will be applied for all time steps.
Otherwise, a value for bptt_truncate means that
bptt will only be applied for at most bptt_truncate steps.
Only considered when learning_rule == 'bptt'
kernel(opt):
# TODO - fill this
Only considered when learning_rule == 'modified'
eta (opt):
Learning rate. Initialized to 0.001.
rand (opt):
Random seed. Initialized to None (no random seed).
verbose (opt):
Verbosity: levels 0 - 2
Outputs:
None
"""
np.random.seed(rand)
self.learning_rule = learning_rule.lower()
if self.learning_rule == 'bptt':
self.gradient_function = self.bptt
elif self.learning_rule == 'fa':
self.gradient_function = self.feedback_alignment
elif self.learning_rule == 'modified':
self.gradient_function = self.modified_learning_rule
else:
raise ValueError
self.input_layer_size = input_layer_size
self.state_layer_size = state_layer_size
self.state_layer_activation = state_layer_activation
self.state_activation = Activation(state_layer_activation)
self.output_layer_size = output_layer_size
self.output_layer_activation = output_layer_activation
self.output_activation = Activation(output_layer_activation)
self.epochs = epochs
self.tau = tau
self.convolutions = None
if self.tau:
self.convolutions = np.zeros((state_layer_size, ))
self.bptt_truncate = bptt_truncate
# U - weight matrix from input into state layer.
# W - weight matrix from state layer to state layer.
# V - weight matrix from state layer to output layer.
"""
self.U = np.eye(-np.sqrt(1./input_layer_size),
np.sqrt(1./input_layer_size),
(state_layer_size, input_layer_size))
self.V = np.random.uniform(-np.sqrt(1./state_layer_size),
np.sqrt(1./state_layer_size),
(output_layer_size, state_layer_size))
self.W = np.random.uniform(-np.sqrt(1./state_layer_size),
np.sqrt(1./state_layer_size),
(state_layer_size, state_layer_size))
"""
self.U = np.eye(state_layer_size)
self.V = np.eye(state_layer_size)
self.W = np.random.uniform(-np.sqrt(1. / state_layer_size),
np.sqrt(1. / state_layer_size),
(state_layer_size, state_layer_size))
self.state_bias = np.zeros((state_layer_size, 1))
self.output_bias = np.zeros((output_layer_size, 1))
# B - Feedback weight matrix for all layers
self.B = np.random.uniform(-np.sqrt(1. / state_layer_size),
np.sqrt(1. / state_layer_size),
(state_layer_size, input_layer_size))
self.eta = eta
self.verbose = verbose
self.show_progress_bar = verbose > 0
def kernel_compute(self, t):
time_const = 1
M = np.exp(-t / time_const)
return (M)
def fit(self, X_train, y_train):
"""
Notes:
Inputs:
X_train:
Training inputs. Expect a list with numpy arrays of size (input_layer_size, N) where N is the number of samples.
Y_train:
Training outputs. Expect a list with numpy arrays of size (output_layer_size, N) where N is the number of samples.
Outputs:
None
"""
eta = self.eta
if self.show_progress_bar:
bar = ProgressBar()
for epoch in bar(range(self.epochs)):
print 'epoch {}'.format(epoch)
if self.convolutions is not None:
self.convolutions *= 0.
for x, y in zip(X_train, y_train):
dLdU, dLdV, dLdW, dLdOb, dLdSb = self.gradient_function(x, y)
#self.U -= eta * dLdU
#self.V -= eta * dLdV
self.W -= eta * dLdW
#self.output_bias -= dLdOb
self.state_bias -= dLdSb
def forward_propagation(self, x):
"""
Inputs:
x:
Expect size (T, input_layer_size), where T is the length of time.
Outputs:
o:
The activation of the output layer.
s:
The activation of the hidden state.
"""
T = x.shape[0]
s = np.zeros((T + 1, self.state_layer_size))
o = np.zeros((T, self.output_layer_size))
s_linear = np.zeros((T + 1, self.state_layer_size))
o_linear = np.zeros((T, self.output_layer_size))
state_bias = Convert2DTo1D(self.state_bias)
output_bias = Convert2DTo1D(self.output_bias)
for t in np.arange(T):
state_linear = np.dot(self.U, x[t]) + np.dot(self.W,
s[t - 1]) + state_bias
s_linear[t] = state_linear
s[t] = self.state_activation.activate(state_linear)
output_linear = np.dot(self.V, s[t]) + output_bias
o[t] = self.output_activation.activate(output_linear)
o_linear[t] = output_linear
if self.convolutions is not None:
if all(self.convolutions == 0):
self.convolutions = s[t]
self.convolutions = (
1 - 1 / self.tau) * self.convolutions + 1 / self.tau * s[t]
return (o, s, s_linear, o_linear)
def modified_learning_rule(self, x, y):
"""
Output:
dLdU:
Gradient for U matrix
dLdV:
Gradient for V matrix
dLdW:
Gradient for W matrix
dLdOb:
Gradient for output layer bias
dLdSb:
Gradient for state layer bias
Hyper Parameters:
K : Kernel
T : Timesteps after which the weights are updated
Learning Rule:
Take a Random Backward Weight Vector(B) in same direction as W and minimize the error
"""
T = len(y)
assert T == len(x)
if self.bptt_truncate is None:
bptt_truncate = T
else:
bptt_truncate = self.bptt_truncate
o, s, s_linear, o_linear = self.forward_propagation(x)
#Initialize Random backward weights
dLdU = np.zeros(self.U.shape)
dLdV = np.zeros(self.V.shape)
dLdW = np.zeros(self.W.shape)
dLdOb = np.zeros(self.output_bias.shape)
dLdSb = np.zeros(self.state_bias.shape)
num_dU_additions = 0
num_dVdW_additions = 0
delta_o = o - y
for t in reversed(range(T)):
# Backprop the error at the output layer
e = delta_o[t]
o_linear_val = o_linear[t]
state_activation = s[t]
e = Convert1DTo2D(e)
o_linear_val = Convert1DTo2D(o_linear_val)
state_activation = Convert1DTo2D(state_activation)
e = e * self.output_activation.dactivate(o_linear_val)
e = np.dot(self.V.T, e)
#kernel_sum = 0
# Backpropagation through time for at most bptt truncate steps
#for t_prime in (range(t+1)):
# k = self.kernel_compute(t - t_prime)
# kernel_sum += k * x[t]
# dLdW += e * kernel_sum * self.B # TODO fix this
# num_dVdW_additions +=1
assert self.convolutions is not None
dLdW += self.B.dot(e).dot(Convert1DTo2D(self.convolutions).T)
return [dLdU, dLdV, dLdW / T, dLdOb, dLdSb]
#raise NotImplementedError
def feedback_alignment(self, x, y):
"""
Output:
dLdU:
Gradient for U matrix
dLdV:
Gradient for V matrix
dLdW:
Gradient for W matrix
dLdOb:
Gradient for output layer bias
dLdSb:
Gradient for state layer bias
"""
# TODO - numpy likes to provide 1D matrices instead of 2D, and unfortunately
# we need 2D matrices. Therefore we have a lot of converting 1D to 2D matrices
# and we might want to clean that later somehow...
# TODO - also this can probably be cleaned more.
T = len(y)
assert T == len(x)
if self.bptt_truncate is None:
bptt_truncate = T
else:
bptt_truncate = self.bptt_truncate
o, s, s_linear, o_linear = self.forward_propagation(x)
dLdU = np.zeros(self.U.shape)
dLdV = np.zeros(self.V.shape)
dLdW = np.zeros(self.W.shape)
dLdOb = np.zeros(self.output_bias.shape)
dLdSb = np.zeros(self.state_bias.shape)
num_dU_additions = 0
num_dVdW_additions = 0
delta_o = o - y
for t in reversed(range(T)):
# Backprop the error at the output layer
g = delta_o[t]
o_linear_val = o_linear[t]
state_activation = s[t]
g = Convert1DTo2D(g)
o_linear_val = Convert1DTo2D(o_linear_val)
state_activation = Convert1DTo2D(state_activation)
g = g * self.output_activation.dactivate(o_linear_val)
dLdV += np.dot(g, state_activation.T)
dLdOb += g
num_dU_additions += 1
g = np.dot(self.V.T, g)
# Backpropagation through time for at most bptt truncate steps
for bptt_step in reversed(range(max(0, t - bptt_truncate), t + 1)):
state_linear = s_linear[bptt_step]
state_activation_prev = s[bptt_step - 1]
x_present = x[t]
state_linear = Convert1DTo2D(state_linear)
state_activation_prev = Convert1DTo2D(state_activation_prev)
x_present = Convert1DTo2D(x_present)
g = g * self.state_activation.dactivate(state_linear)
dLdW += np.dot(g, state_activation_prev.T)
dLdU += np.dot(g, x_present.T)
dLdSb += g
num_dVdW_additions += 1
g = g * np.dot(self.B.T, g)
return [
dLdU / num_dU_additions, dLdV / num_dVdW_additions,
dLdW / num_dVdW_additions, dLdOb / num_dU_additions,
dLdSb / num_dVdW_additions
]
def bptt(self, x, y):
"""
Output:
dLdU:
Gradient for U matrix
dLdV:
Gradient for V matrix
dLdW:
Gradient for W matrix
dLdOb:
Gradient for output layer bias
dLdSb:
Gradient for state layer bias
"""
# TODO - numpy likes to provide 1D matrices instead of 2D, and unfortunately
# we need 2D matrices. Therefore we have a lot of converting 1D to 2D matrices
# and we might want to clean that later somehow...
# TODO - also this can probably be cleaned more.
T = len(y)
assert T == len(x)
if self.bptt_truncate is None:
bptt_truncate = T
else:
bptt_truncate = self.bptt_truncate
o, s, s_linear, o_linear = self.forward_propagation(x)
dLdU = np.zeros(self.U.shape)
dLdV = np.zeros(self.V.shape)
dLdW = np.zeros(self.W.shape)
dLdOb = np.zeros(self.output_bias.shape)
dLdSb = np.zeros(self.state_bias.shape)
num_dU_additions = 0
num_dVdW_additions = 0
delta_o = o - y
for t in reversed(range(T)):
# Backprop the error at the output layer
g = delta_o[t]
o_linear_val = o_linear[t]
state_activation = s[t]
g = Convert1DTo2D(g)
o_linear_val = Convert1DTo2D(o_linear_val)
state_activation = Convert1DTo2D(state_activation)
g = g * self.output_activation.dactivate(o_linear_val)
dLdV += np.dot(g, state_activation.T)
dLdOb += g
num_dU_additions += 1
g = np.dot(self.V.T, g)
# Backpropagation through time for at most bptt truncate steps
for bptt_step in reversed(range(max(0, t - bptt_truncate), t + 1)):
state_linear = s_linear[bptt_step]
state_activation_prev = s[bptt_step - 1]
x_present = x[t]
state_linear = Convert1DTo2D(state_linear)
state_activation_prev = Convert1DTo2D(state_activation_prev)
x_present = Convert1DTo2D(x_present)
g = g * self.state_activation.dactivate(state_linear)
dLdW += np.dot(g, state_activation_prev.T)
dLdU += np.dot(g, x_present.T)
dLdSb += g
num_dVdW_additions += 1
g = g * np.dot(self.W.T, g)
return [
dLdU / num_dU_additions, dLdV / num_dVdW_additions,
dLdW / num_dVdW_additions, dLdOb / num_dU_additions,
dLdSb / num_dVdW_additions
]
def predict(self, X):
"""
Inputs:
X:
Training inputs. Expect a list with numpy arrays of size (input_layer_size, N) where N is the number of samples.
Outputs:
predictions
"""
predictions = []
for x in X:
o, _, _, _ = self.forward_propagation(x)
predictions.append(o)
return predictions
def score(self, X, Y):
"""
Inputs:
X:
Training inputs. Expect a list with numpy arrays of size (input_layer_size, N) where N is the number of samples.
Y:
Training outputs. Expect a list with numpy arrays of size (output_layer_size, N) where N is the number of samples.
Outputs:
MSE
"""
predictions = self.predict(X)
mses = []
for prediction, y in zip(predictions, Y):
mses.append(np.mean((predictions - y)**2))
return np.mean(mses)
class RNN(object):
def __init__(self,
input_layer_size,
state_layer_size,
state_layer_activation,
output_layer_size,
output_layer_activation,
epochs=100,
bptt_truncate=None,
learning_rule='bptt',
tau=None,
eta=0.001,
rand=None,
verbose=0):
"""
Notes:
U - weight matrix from input into hidden layer.
W - weight matrix from hidden layer to hidden layer.
V - weight matrix from hidden layer to output layer.
Inputs:
input_size:
Size of the input vector. We expect a 2D numpy array, so this should be X.shape[1]
state_layer_size:
State layer size.
state_layer_activation:
A string. Refer to activation.py
output_size:
Size of the output vector. We expect a 2D numpy array, so this should be Y.shape[1]
output_layer_activation:
A string. Refer to activation.py
epochs(opt):
Number of epochs for a single training sample.
learning_rule(opt):
Choose between 'bptt','fa', 'dfa' or 'modified'
bptt_truncate(opt):
If left at None, back propagation through time will be applied for all time steps.
Otherwise, a value for bptt_truncate means that
bptt will only be applied for at most bptt_truncate steps.
Only considered when learning_rule == 'bptt'
kernel(opt):
# TODO - fill this
Only considered when learning_rule == 'modified'
eta (opt):
Learning rate. Initialized to 0.001.
rand (opt):
Random seed. Initialized to None (no random seed).
verbose (opt):
Verbosity: levels 0 - 2
Outputs:
None
"""
np.random.seed(rand)
self.learning_rule = learning_rule.lower()
if self.learning_rule == 'bptt':
self.gradient_function = self.bptt
elif self.learning_rule == 'fa':
self.gradient_function = self.feedback_alignment
elif self.learning_rule == 'dfa':
self.gradient_function = self.direct_feedback_alignment
elif self.learning_rule == 'modified':
self.gradient_function = self.modified_learning_rule
else:
raise ValueError
self.input_layer_size = input_layer_size
self.state_layer_size = state_layer_size
self.state_layer_activation = state_layer_activation
self.state_activation = Activation(state_layer_activation)
self.output_layer_size = output_layer_size
self.output_layer_activation = output_layer_activation
self.output_activation = Activation(output_layer_activation)
self.epochs = epochs
self.tau = tau
self.bptt_truncate = bptt_truncate
self.kernel_convs = None
# U - weight matrix from input into state layer.
# W - weight matrix from state layer to state layer.
# V - weight matrix from state layer to output layer.
"""
if self.learning_rule == 'bptt':
self.U = np.random.uniform(-np.sqrt(1./input_layer_size),
np.sqrt(1./input_layer_size),
(state_layer_size, input_layer_size))
self.V = np.random.uniform(-np.sqrt(1./state_layer_size),
np.sqrt(1./state_layer_size),
(output_layer_size, state_layer_size))
self.W = np.random.uniform(-np.sqrt(1./state_layer_size),
np.sqrt(1./state_layer_size),
(state_layer_size, state_layer_size))
else:
"""
if state_layer_size == input_layer_size and state_layer_size == output_layer_size:
print "Using identity matrices for U and V"
self.U = np.eye(state_layer_size)
self.V = np.eye(state_layer_size)
else:
self.U = np.random.uniform(1, 2.,
(state_layer_size, input_layer_size))
self.V = np.random.uniform(1, 2.,
(output_layer_size, state_layer_size))
self.W = np.random.uniform(-0.5, 0.5,
(state_layer_size, state_layer_size))
# see if W matrix randomization is the cause
#self.W = np.random.rand(2, 2) - 1/2#np.array([[0.51940038, -0.57702151],[0.64065148, 0.31259335]])
#self.W = np.array([[0.51940038, -0.57702151],[0.64065148, 0.31259335]])
self.state_bias = np.zeros((state_layer_size, 1))
self.output_bias = np.zeros((output_layer_size, 1))
# B - Feedback weight matrix for all layers
"""
self.B = np.random.uniform(-np.sqrt(1./state_layer_size),
np.sqrt(1./state_layer_size),
(state_layer_size, input_layer_size))
"""
self.B = np.random.uniform(0., 0.5, self.W.shape)
self.eta = eta
self.verbose = verbose
self.show_progress_bar = verbose > 0
def kernel_compute(self, t):
return np.exp(-t / self.tau)
def eWBe(self, x, y):
o, s, s_linear, o_linear = self.forward_propagation(x)
delta_o = o - y
T = len(x)
eWBe = []
for t in reversed(range(T)):
e = delta_o[t]
eWBe.append(np.dot(np.dot(np.dot(e.T, self.W), self.B), e))
return eWBe
def fit(self, X, y, validation_size=0.1):
"""
Notes:
Inputs:
X_train:
Training inputs. Expect a list with numpy arrays of size (input_layer_size, N) where N is the number of samples.
Y_train:
Training outputs. Expect a list with numpy arrays of size (output_layer_size, N) where N is the number of samples.
Outputs:
None
"""
eta = self.eta
X = np.array(X)
y = np.array(y)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=validation_size, random_state=0)
if self.verbose:
print "Validation size: {0}".format(validation_size)
print "Training on {0} samples".format(len(X_train))
training_losses = []
validation_losses = []
# Non-online
if self.show_progress_bar:
bar = ProgressBar(max_value=len(X_train))
for epoch in range(self.epochs):
#if self.learning_rule == 'modified':
# self.kernel_convs = np.zeros_like(self.kernel_convs)
training_loss = self.score(X_train, y_train)
validation_loss = self.score(X_test, y_test)
training_losses.append(training_loss)
validation_losses.append(validation_loss)
if self.verbose == 2:
print "--------"
print "Epoch {0}/{1}".format(epoch, self.epochs)
print "Training loss: {0}".format(training_loss)
print "Validation loss: {0}".format(validation_loss)
print "--------"
#eWBe = []
for i, (x, y) in enumerate(zip(X_train, y_train)):
dLdU, dLdV, dLdW, dLdOb, dLdSb = self.gradient_function(x, y)
self.W -= eta * dLdW
#self.U -= eta * dLdU
#self.V -= eta * dLdV
self.state_bias -= eta * dLdSb
#self.output_bias -= eta * dLdOb
#eWBe.append(np.mean(self.eWBe(x, y)))
if self.show_progress_bar:
bar.update(i)
if self.show_progress_bar:
bar.update(0)
return training_losses, validation_losses
def forward_propagation(self, x):
"""
Inputs:
x:
Expect size (T, input_layer_size), where T is the length of time.
Outputs:
o:
The activation of the output layer.
s:
The activation of the hidden state.
"""
if self.learning_rule == 'modified':
self.kernel_convs = np.zeros((self.state_layer_size, x.shape[0]))
T = x.shape[0]
s = np.zeros((T + 1, self.state_layer_size))
o = np.zeros((T, self.output_layer_size))
s_linear = np.zeros((T + 1, self.state_layer_size))
o_linear = np.zeros((T, self.output_layer_size))
state_bias = Convert2DTo1D(self.state_bias)
output_bias = Convert2DTo1D(self.output_bias)
for t in np.arange(T):
state_linear = np.dot(self.U, x[t]) + np.dot(self.W,
s[t - 1]) + state_bias
s_linear[t] = state_linear
s[t] = self.state_activation.activate(state_linear)
if self.learning_rule == 'modified' and t > 0:
alpha = 1 / self.tau
self.kernel_convs[:, t] = alpha * s[t] + (
1 - alpha) * self.kernel_convs[:, t - 1]
output_linear = np.dot(self.V, s[t]) + output_bias
o[t] = self.output_activation.activate(output_linear)
o_linear[t] = output_linear
return (o, s, s_linear, o_linear)
def modified_learning_rule(self, x, y):
"""
Output:
dLdU:
Gradient for U matrix
dLdV:
Gradient for V matrix
dLdW:
Gradient for W matrix
dLdOb:
Gradient for output layer bias
dLdSb:
Gradient for state layer bias
Hyper Parameters:
K : Kernel
T : Timesteps after which the weights are updated
Learning Rule:
Take a Random Backward Weight Vector(B) in same direction as W and minimize the error
"""
T = len(y)
assert T == len(x)
if self.bptt_truncate is None:
bptt_truncate = T
else:
bptt_truncate = self.bptt_truncate
o, s, s_linear, o_linear = self.forward_propagation(x)
#Initialize Random backward weights
dLdU = np.zeros(self.U.shape)
dLdV = np.zeros(self.V.shape)
dLdW = np.zeros(self.W.shape)
dLdOb = np.zeros(self.output_bias.shape)
dLdSb = np.zeros(self.state_bias.shape)
num_dW_additions = 0
delta_o = o - y
for t in reversed(range(T)):
# Get the error at the output layer
e = self.V.T.dot(delta_o[t])
o_linear_val = o_linear[t]
e = Convert1DTo2D(e)
o_linear_val = Convert1DTo2D(o_linear_val)
#kernel_sum = 0
# Backpropagation through time for at most bptt truncate steps
#for t_prime in (range(max(0,t-50),t+1)):
#for t_prime in (range(t+1)):
# state_activation = s[t_prime]
# state_linear = s_linear[t_prime - 1]
# k = self.kernel_compute(t - t_prime)
# kernel_sum += k * state_activation * self.state_activation.dactivate(state_linear)
#kernel_sum = kernel_sum/(t+1)
#kernel_sum = Convert1DTo2D(kernel_sum)
dLdW += self.B.dot(e).dot(
Convert1DTo2D(self.kernel_convs[:, t]).T
) #np.dot(np.dot(self.B, e), kernel_sum.T)
dLdSb += np.dot(self.B, e)
num_dW_additions += 1
return [
dLdU, dLdV, dLdW / num_dW_additions, dLdOb,
dLdSb / num_dW_additions
]
def direct_feedback_alignment(self, x, y):
"""
Output:
dLdU:
Gradient for U matrix
dLdV:
Gradient for V matrix
dLdW:
Gradient for W matrix
dLdOb:
Gradient for output layer bias
dLdSb:
Gradient for state layer bias
"""
T = len(y)
assert T == len(x)
if self.bptt_truncate is None:
bptt_truncate = T
else:
bptt_truncate = self.bptt_truncate
o, s, s_linear, o_linear = self.forward_propagation(x)
dLdU = np.zeros(self.U.shape)
dLdV = np.zeros(self.V.shape)
dLdW = np.zeros(self.W.shape)
dLdOb = np.zeros(self.output_bias.shape)
dLdSb = np.zeros(self.state_bias.shape)
num_dU_additions = 0
num_dVdW_additions = 0
delta_o = o - y
for t in reversed(range(T)):
# Backprop the error at the output layer
e = self.V.T.dot(delta_o[t])
o_linear_val = o_linear[t]
state_activation = s[t]
e = Convert1DTo2D(e)
o_linear_val = Convert1DTo2D(o_linear_val)
state_activation = Convert1DTo2D(state_activation)
num_dU_additions += 1
# Backpropagation through time for at most bptt truncate steps
for bptt_step in reversed(range(max(0, t - bptt_truncate), t + 1)):
state_linear = s_linear[bptt_step]
state_activation_prev = s[bptt_step - 1]
x_present = x[t]
g = self.B.dot(e.copy())
state_linear = Convert1DTo2D(state_linear)
state_activation_prev = Convert1DTo2D(state_activation_prev)
x_present = Convert1DTo2D(x_present)
g = g * self.state_activation.dactivate(state_linear)
dLdW += np.dot(g, state_activation_prev.T)
dLdSb += g
num_dVdW_additions += 1
num_dVdW_additions = T
return [
dLdU / num_dU_additions, dLdV / num_dVdW_additions,
dLdW / num_dVdW_additions, dLdOb / num_dU_additions,
dLdSb / num_dVdW_additions
]
def feedback_alignment(self, x, y):
"""
Output:
dLdU:
Gradient for U matrix
dLdV:
Gradient for V matrix
dLdW:
Gradient for W matrix
dLdOb:
Gradient for output layer bias
dLdSb:
Gradient for state layer bias
"""
T = len(y)
assert T == len(x)
if self.bptt_truncate is None:
bptt_truncate = T
else:
bptt_truncate = self.bptt_truncate
o, s, s_linear, o_linear = self.forward_propagation(x)
dLdU = np.zeros(self.U.shape)
dLdV = np.zeros(self.V.shape)
dLdW = np.zeros(self.W.shape)
dLdOb = np.zeros(self.output_bias.shape)
dLdSb = np.zeros(self.state_bias.shape)
num_dU_additions = 0
num_dVdW_additions = 0
delta_o = o - y
for t in reversed(range(T)):
# Backprop the error at the output layer
g = self.V.T.dot(delta_o[t])
o_linear_val = o_linear[t]
state_activation = s[t]
g = Convert1DTo2D(g)
o_linear_val = Convert1DTo2D(o_linear_val)
state_activation = Convert1DTo2D(state_activation)
num_dU_additions += 1
# Backpropagation through time for at most bptt truncate steps
for bptt_step in reversed(range(max(0, t - bptt_truncate), t + 1)):
state_linear = s_linear[bptt_step]
state_activation_prev = s[bptt_step - 1]
x_present = x[t]
state_linear = Convert1DTo2D(state_linear)
state_activation_prev = Convert1DTo2D(state_activation_prev)
x_present = Convert1DTo2D(x_present)
g = g * self.state_activation.dactivate(state_linear)
dLdW += np.dot(g, state_activation_prev.T)
dLdSb += g
num_dVdW_additions += 1
g = np.dot(self.B, g)
num_dVdW_additions = T
return [
dLdU / num_dU_additions, dLdV / num_dVdW_additions,
dLdW / num_dVdW_additions, dLdOb / num_dU_additions,
dLdSb / num_dVdW_additions
]
# online version
#def bptt(self, x, y):
"""
Output:
dLdU:
Gradient for U matrix
dLdV:
Gradient for V matrix
dLdW:
Gradient for W matrix
dLdOb:
Gradient for output layer bias
dLdSb:
Gradient for state layer bias
"""
"""
# TODO - numpy likes to provide 1D matrices instead of 2D, and unfortunately
# we need 2D matrices. Therefore we have a lot of converting 1D to 2D matrices
# and we might want to clean that later somehow...
# TODO - also this can probably be cleaned more.
t = len(y)
assert t == len(x)
if self.bptt_truncate is None:
bptt_truncate = t
else:
bptt_truncate = self.bptt_truncate
o, s, s_linear, o_linear = self.forward_propagation(x)
dLdU = np.zeros(self.U.shape)
dLdV = np.zeros(self.V.shape)
dLdW = np.zeros(self.W.shape)
dLdOb = np.zeros(self.output_bias.shape)
dLdSb = np.zeros(self.state_bias.shape)
num_dU_additions = 0
num_dVdW_additions = 0
delta_o = o - y
# Backprop the error at the output layer
g = delta_o[t - 1]
o_linear_val = o_linear[t - 1]
state_activation = s[t - 1]
g = Convert1DTo2D(g)
o_linear_val = Convert1DTo2D(o_linear_val)
state_activation = Convert1DTo2D(state_activation)
g = g * self.output_activation.dactivate(o_linear_val)
dLdV += np.dot(g, state_activation.T)
dLdOb += g
num_dU_additions += 1
g = np.dot(self.V.T, g)
# Backpropagation through time for at most bptt truncate steps
for bptt_step in reversed(range(max(0, t - bptt_truncate), t + 1)):
state_linear = s_linear[bptt_step]
state_activation_prev = s[bptt_step - 1]
x_present = x[t - 1]
state_linear = Convert1DTo2D(state_linear)
state_activation_prev = Convert1DTo2D(state_activation_prev)
x_present = Convert1DTo2D(x_present)
g = g * self.state_activation.dactivate(state_linear)
dLdW += np.dot(g, state_activation_prev.T)
dLdU += np.dot(g, x_present.T)
dLdSb += g
num_dVdW_additions += 1
g = g * np.dot(self.W.T, g)
return [dLdU/num_dU_additions,
dLdV/num_dVdW_additions,
dLdW/num_dVdW_additions,
dLdOb/num_dU_additions,
dLdSb/num_dVdW_additions]
"""
# Non-online version
def bptt(self, x, y):
# TODO - numpy likes to provide 1D matrices instead of 2D, and unfortunately
# we need 2D matrices. Therefore we have a lot of converting 1D to 2D matrices
# and we might want to clean that later somehow...
# TODO - also this can probably be cleaned more.
T = len(y)
assert T == len(x)
if self.bptt_truncate is None:
bptt_truncate = T
else:
bptt_truncate = self.bptt_truncate
o, s, s_linear, o_linear = self.forward_propagation(x)
dLdU = np.zeros(self.U.shape)
dLdV = np.zeros(self.V.shape)
dLdW = np.zeros(self.W.shape)
dLdOb = np.zeros(self.output_bias.shape)
dLdSb = np.zeros(self.state_bias.shape)
num_dU_additions = 0
num_dVdW_additions = 0
delta_o = o - y
for t in reversed(range(T)):
# Backprop the error at the output layer
g = delta_o[t]
o_linear_val = o_linear[t]
state_activation = s[t]
g = Convert1DTo2D(g)
o_linear_val = Convert1DTo2D(o_linear_val)
state_activation = Convert1DTo2D(state_activation)
g = g * self.output_activation.dactivate(o_linear_val)
dLdV += np.dot(g, state_activation.T)
dLdOb += g
num_dU_additions += 1
g = np.dot(self.V.T, g)
# Backpropagation through time for at most bptt truncate steps
for bptt_step in reversed(range(max(0, t - bptt_truncate), t + 1)):
state_linear = s_linear[bptt_step]
state_activation_prev = s[bptt_step - 1]
x_present = x[t]
state_linear = Convert1DTo2D(state_linear)
state_activation_prev = Convert1DTo2D(state_activation_prev)
x_present = Convert1DTo2D(x_present)
g = g * self.state_activation.dactivate(state_linear)
dLdW += np.dot(g, state_activation_prev.T)
dLdU += np.dot(g, x_present.T)
dLdSb += g
num_dVdW_additions += 1
g = np.dot(self.W.T, g)
num_dVdW_additions = T
return [
dLdU / num_dU_additions, dLdV / num_dVdW_additions,
dLdW / num_dVdW_additions, dLdOb / num_dU_additions,
dLdSb / num_dVdW_additions
]
def predict(self, X):
"""
Inputs:
X:
Training inputs. Expect a list with numpy arrays of size (input_layer_size, N) where N is the number of samples.
Outputs:
predictions
"""
predictions = []
for x in X:
o, _, _, _ = self.forward_propagation(x)
predictions.append(o)
return predictions
def score(self, X, Y):
"""
Inputs:
X:
Training inputs. Expect a list with numpy arrays of size (input_layer_size, N) where N is the number of samples.
Y:
Training outputs. Expect a list with numpy arrays of size (output_layer_size, N) where N is the number of samples.
Outputs:
MSE
"""
predictions = self.predict(X)
mses = []
for prediction, y in zip(predictions, Y):
#mses.append(np.mean((prediction - y)**2))
mses.append(mean_squared_error(prediction, y))
return np.mean(mses)
