def __init__(self,
nn_layers,
L2_reg=0.0001,
learning_rate=0.1,
batch_size=32,
discrete_target=False):
self.layers = nn_layers
#TODO: maybe initialize layers and set all inputs as prev outputs
self._batch_size = batch_size
# self._fprop = theano.function(
# [self.layers[0].input_var],
# self.output()
# )
self.parameters = layers.all_parameters(self.layers[-1])
# self.parameters = [param for layer in nn_layers[1:] for param in layer.params] #nn_layers[5].params + nn_layers[4].params + nn_layers[3].params + nn_layers[2].params + nn_layers[1].params
self.cost = self.layers[-1].error()
self.error_rate = self.layers[-1].error_rate()
self.regularization = sum([(W_or_b**2).sum()
for W_or_b in self.parameters])
self.updates = layers.gen_updates_sgd(
self.cost + self.regularization * L2_reg, self.parameters,
learning_rate) # the last layer must be a layers.OutputLayer
# self.updates = layers.gen_updates_sgd(cost, self.parameters, learning_rate) # the last layer must be a layers.OutputLayer
# self.train_model = theano.function(
# inputs=[self.layers[0].input_var, self.layers[-1].target_var],
# updates=self.updates,
# outputs=self.cost
# )
self._idx = T.lscalar('idx')
self.x_shared = theano.shared(
np.zeros(self.layers[0].get_output_shape(),
dtype=theano.config.floatX))
self.y_shared = theano.shared(
np.zeros(self.layers[-1].get_output_shape(),
dtype=theano.config.floatX))
self.x_shared_validate = theano.shared(
np.zeros(self.layers[0].get_output_shape(),
dtype=theano.config.floatX))
self.y_shared_validate = theano.shared(
np.zeros(self.layers[-1].get_output_shape(),
dtype=theano.config.floatX))
self.x_shared_test = theano.shared(
np.zeros(self.layers[0].get_output_shape(),
dtype=theano.config.floatX))
self.y_shared_test = theano.shared(
np.zeros(self.layers[-1].get_output_shape(),
dtype=theano.config.floatX))
self.y_converted = T.cast(
self.y_shared, 'int32') if discrete_target else self.y_shared
self.y_converted_validate = T.cast(
self.y_shared_validate,
'int32') if discrete_target else self.y_shared
self.y_converted_test = T.cast(
self.y_shared_test, 'int32') if discrete_target else self.y_shared
self._givens = {
self.layers[0].input_var:
self.x_shared[self._idx * self._batch_size:(self._idx + 1) *
self._batch_size],
self.layers[-1].target_var:
self.y_converted[self._idx * self._batch_size:(self._idx + 1) *
self._batch_size],
}
self._givens_validate = {
self.layers[0].input_var:
self.x_shared_validate[self._idx *
self._batch_size:(self._idx + 1) *
self._batch_size],
self.layers[-1].target_var:
self.y_converted_validate[self._idx *
self._batch_size:(self._idx + 1) *
self._batch_size],
}
self._givens_test = {
self.layers[0].input_var:
self.x_shared_test[self._idx * self._batch_size:(self._idx + 1) *
self._batch_size],
self.layers[-1].target_var:
self.y_converted_test[self._idx *
self._batch_size:(self._idx + 1) *
self._batch_size],
}
self._train_model_batch = theano.function(inputs=[self._idx],
updates=self.updates,
givens=self._givens,
outputs=self.cost)
self._validate_model_batch = theano.function(
inputs=[self._idx],
givens=self._givens_validate,
outputs=self.error_rate)
self._test_model_batch = theano.function(inputs=[self._idx],
givens=self._givens_test,
outputs=self.error_rate)
self._output_model_batch = theano.function(inputs=[self._idx],
updates=self.updates,
givens=self._givens,
outputs=self.output())
def __init__(self,
num_actions,
phi_length,
width,
height,
discount=.9,
learning_rate=.01,
batch_size=32,
approximator='none'):
self._batch_size = batch_size
self._num_input_features = phi_length
self._phi_length = phi_length
self._img_width = width
self._img_height = height
self._discount = discount
self.num_actions = num_actions
self.learning_rate = learning_rate
self.scale_input_by = 255.0
print "neural net initialization, lr is: ", self.learning_rate, approximator
# CONSTRUCT THE LAYERS
self.q_layers = []
self.q_layers.append(
layers.Input2DLayer(self._batch_size, self._num_input_features,
self._img_height, self._img_width,
self.scale_input_by))
if approximator == 'cuda_conv':
self.q_layers.append(
cc_layers.ShuffleBC01ToC01BLayer(self.q_layers[-1]))
self.q_layers.append(
cc_layers.CudaConvnetConv2DLayer(self.q_layers[-1],
n_filters=16,
filter_size=8,
stride=4,
weights_std=.01,
init_bias_value=0.1))
self.q_layers.append(
cc_layers.CudaConvnetConv2DLayer(self.q_layers[-1],
n_filters=32,
filter_size=4,
stride=2,
weights_std=.01,
init_bias_value=0.1))
self.q_layers.append(
cc_layers.ShuffleC01BToBC01Layer(self.q_layers[-1]))
elif approximator == 'conv':
self.q_layers.append(
layers.StridedConv2DLayer(self.q_layers[-1],
n_filters=16,
filter_width=8,
filter_height=8,
stride_x=4,
stride_y=4,
weights_std=.01,
init_bias_value=0.01))
self.q_layers.append(
layers.StridedConv2DLayer(self.q_layers[-1],
n_filters=32,
filter_width=4,
filter_height=4,
stride_x=2,
stride_y=2,
weights_std=.01,
init_bias_value=0.01))
if approximator == 'cuda_conv' or approximator == 'conv':
self.q_layers.append(
layers.DenseLayer(self.q_layers[-1],
n_outputs=256,
weights_std=0.01,
init_bias_value=0.1,
dropout=0,
nonlinearity=layers.rectify))
self.q_layers.append(
layers.DenseLayer(self.q_layers[-1],
n_outputs=num_actions,
weights_std=0.01,
init_bias_value=0.1,
dropout=0,
nonlinearity=layers.identity))
if approximator == 'none':
self.q_layers.append(\
layers.DenseLayerNoBias(self.q_layers[-1],
n_outputs=num_actions,
weights_std=0.00,
dropout=0,
nonlinearity=layers.identity))
self.q_layers.append(layers.OutputLayer(self.q_layers[-1]))
for i in range(len(self.q_layers) - 1):
print self.q_layers[i].get_output_shape()
# Now create a network (using the same weights)
# for next state q values
self.next_layers = copy_layers(self.q_layers)
self.next_layers[0] = layers.Input2DLayer(self._batch_size,
self._num_input_features,
self._img_width,
self._img_height,
self.scale_input_by)
self.next_layers[1].input_layer = self.next_layers[0]
self.rewards = T.col()
self.actions = T.icol()
# Build the loss function ...
print "building loss funtion"
q_vals = self.q_layers[-1].predictions()
next_q_vals = self.next_layers[-1].predictions()
next_maxes = T.max(next_q_vals, axis=1, keepdims=True)
target = self.rewards + discount * next_maxes
target = theano.gradient.consider_constant(target)
diff = target - q_vals
# Zero out all entries for actions that were not chosen...
mask = build_mask(T.zeros_like(diff), self.actions, 1.0)
diff_masked = diff * mask
error = T.mean(diff_masked**2)
self._loss = error * diff_masked.shape[1] #
self._parameters = layers.all_parameters(self.q_layers[-1])
self._idx = T.lscalar('idx')
# CREATE VARIABLES FOR INPUT AND OUTPUT
self.states_shared = theano.shared(
np.zeros((1, 1, 1, 1), dtype=theano.config.floatX))
self.states_shared_next = theano.shared(
np.zeros((1, 1, 1, 1), dtype=theano.config.floatX))
self.rewards_shared = theano.shared(np.zeros(
(1, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(np.zeros((1, 1), dtype='int32'),
broadcastable=(False, True))
self._givens = \
{self.q_layers[0].input_var:
self.states_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :, :, :],
self.next_layers[0].input_var:
self.states_shared_next[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :, :, :],
self.rewards:
self.rewards_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :],
self.actions:
self.actions_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :]
}
self._updates = layers.gen_updates_rmsprop_and_nesterov_momentum(\
self._loss, self._parameters, learning_rate=self.learning_rate,
rho=0.9, momentum=0.9, epsilon=1e-6)
self._train = theano.function([self._idx],
self._loss,
givens=self._givens,
updates=self._updates)
self._compute_loss = theano.function([self._idx],
self._loss,
givens=self._givens)
self._compute_q_vals = \
theano.function([self.q_layers[0].input_var],
self.q_layers[-1].predictions(),
on_unused_input='ignore')
l5 = layers.DenseLayer(l4,
n_outputs=37,
weights_std=0.01,
init_bias_value=0.1,
dropout=0.5,
nonlinearity=layers.identity)
# l6 = layers.OutputLayer(l5, error_measure='mse')
l6 = custom.OptimisedDivGalaxyOutputLayer(
l5
) # this incorporates the constraints on the output (probabilities sum to one, weighting, etc.)
train_loss_nonorm = l6.error(normalisation=False)
train_loss = l6.error() # but compute and print this!
valid_loss = l6.error(dropout_active=False)
all_parameters = layers.all_parameters(l6)
all_bias_parameters = layers.all_bias_parameters(l6)
xs_shared = [
theano.shared(np.zeros((1, 1, 1, 1), dtype=theano.config.floatX))
for _ in xrange(num_input_representations)
]
y_shared = theano.shared(np.zeros((1, 1), dtype=theano.config.floatX))
learning_rate = theano.shared(
np.array(LEARNING_RATE_SCHEDULE[0], dtype=theano.config.floatX))
idx = T.lscalar('idx')
givens = {
l0.input_var: xs_shared[0][idx * BATCH_SIZE:(idx + 1) * BATCH_SIZE],
l4a = layers.DenseLayer(j3, n_outputs=4096, weights_std=0.001, init_bias_value=0.01, dropout=0.5, nonlinearity=layers.identity)
l4b = layers.FeatureMaxPoolingLayer(l4a, pool_size=2, feature_dim=1, implementation='reshape')
l4c = layers.DenseLayer(l4b, n_outputs=4096, weights_std=0.001, init_bias_value=0.01, dropout=0.5, nonlinearity=layers.identity)
l4 = layers.FeatureMaxPoolingLayer(l4c, pool_size=2, feature_dim=1, implementation='reshape')
# l5 = layers.DenseLayer(l4, n_outputs=37, weights_std=0.01, init_bias_value=0.0, dropout=0.5, nonlinearity=custom.clip_01) # nonlinearity=layers.identity)
l5 = layers.DenseLayer(l4, n_outputs=37, weights_std=0.01, init_bias_value=0.1, dropout=0.5, nonlinearity=layers.identity)
# l6 = layers.OutputLayer(l5, error_measure='mse')
l6 = custom.OptimisedDivGalaxyOutputLayer(l5) # this incorporates the constraints on the output (probabilities sum to one, weighting, etc.)
train_loss_nonorm = l6.error(normalisation=False)
train_loss = l6.error() # but compute and print this!
valid_loss = l6.error(dropout_active=False)
all_parameters = layers.all_parameters(l6)
all_bias_parameters = layers.all_bias_parameters(l6)
xs_shared = [theano.shared(np.zeros((1,1,1,1), dtype=theano.config.floatX)) for _ in xrange(num_input_representations)]
y_shared = theano.shared(np.zeros((1,1), dtype=theano.config.floatX))
learning_rate = theano.shared(np.array(LEARNING_RATE_SCHEDULE[0], dtype=theano.config.floatX))
idx = T.lscalar('idx')
givens = {
l0.input_var: xs_shared[0][idx*BATCH_SIZE:(idx+1)*BATCH_SIZE],
l0_45.input_var: xs_shared[1][idx*BATCH_SIZE:(idx+1)*BATCH_SIZE],
l6.target_var: y_shared[idx*BATCH_SIZE:(idx+1)*BATCH_SIZE],
}
def __init__(self, num_actions, phi_length, width, height,
discount, learning_rate, decay, momentum=0,
batch_size=32,
approximator='none'):
self._batch_size = batch_size
self._num_input_features = phi_length
self._phi_length = phi_length
self._img_width = width
self._img_height = height
self._discount = discount
self.num_actions = num_actions
self.learning_rate = learning_rate
self.decay = decay
self.momentum = momentum
self.scale_input_by = 255.0
# CONSTRUCT THE LAYERS
self.q_layers = []
self.q_layers.append(layers.Input2DLayer(self._batch_size,
self._num_input_features,
self._img_height,
self._img_width,
self.scale_input_by))
if approximator == 'cuda_conv':
self.q_layers.append(cc_layers.ShuffleBC01ToC01BLayer(
self.q_layers[-1]))
self.q_layers.append(
cc_layers.CudaConvnetConv2DLayer(self.q_layers[-1],
n_filters=16,
filter_size=8,
stride=4,
weights_std=.01,
init_bias_value=0.1))
self.q_layers.append(
cc_layers.CudaConvnetConv2DLayer(self.q_layers[-1],
n_filters=32,
filter_size=4,
stride=2,
weights_std=.01,
init_bias_value=0.1))
self.q_layers.append(cc_layers.ShuffleC01BToBC01Layer(
self.q_layers[-1]))
elif approximator == 'conv':
self.q_layers.append(layers.StridedConv2DLayer(self.q_layers[-1],
n_filters=16,
filter_width=8,
filter_height=8,
stride_x=4,
stride_y=4,
weights_std=.01,
init_bias_value=0.01))
self.q_layers.append(layers.StridedConv2DLayer(self.q_layers[-1],
n_filters=32,
filter_width=4,
filter_height=4,
stride_x=2,
stride_y=2,
weights_std=.01,
init_bias_value=0.01))
if approximator == 'cuda_conv' or approximator == 'conv':
self.q_layers.append(layers.DenseLayer(self.q_layers[-1],
n_outputs=256,
weights_std=0.01,
init_bias_value=0.1,
dropout=0,
nonlinearity=layers.rectify))
self.q_layers.append(
layers.DenseLayer(self.q_layers[-1],
n_outputs=num_actions,
weights_std=0.01,
init_bias_value=0.1,
dropout=0,
nonlinearity=layers.identity))
if approximator == 'none':
self.q_layers.append(\
layers.DenseLayerNoBias(self.q_layers[-1],
n_outputs=num_actions,
weights_std=0.00,
dropout=0,
nonlinearity=layers.identity))
self.q_layers.append(layers.OutputLayer(self.q_layers[-1]))
for i in range(len(self.q_layers)-1):
print self.q_layers[i].get_output_shape()
# Now create a network (using the same weights)
# for next state q values
self.next_layers = copy_layers(self.q_layers)
self.next_layers[0] = layers.Input2DLayer(self._batch_size,
self._num_input_features,
self._img_width,
self._img_height,
self.scale_input_by)
self.next_layers[1].input_layer = self.next_layers[0]
self.rewards = T.col()
self.actions = T.icol()
# Build the loss function ...
q_vals = self.q_layers[-1].predictions()
next_q_vals = self.next_layers[-1].predictions()
next_maxes = T.max(next_q_vals, axis=1, keepdims=True)
target = self.rewards + discount * next_maxes
target = theano.gradient.consider_constant(target)
diff = target - q_vals
# Zero out all entries for actions that were not chosen...
mask = build_mask(T.zeros_like(diff), self.actions, 1.0)
diff_masked = diff * mask
error = T.mean(diff_masked ** 2)
self._loss = error * diff_masked.shape[1] #
self._parameters = layers.all_parameters(self.q_layers[-1])
self._idx = T.lscalar('idx')
# CREATE VARIABLES FOR INPUT AND OUTPUT
self.states_shared = theano.shared(
np.zeros((1, 1, 1, 1), dtype=theano.config.floatX))
self.states_shared_next = theano.shared(
np.zeros((1, 1, 1, 1), dtype=theano.config.floatX))
self.rewards_shared = theano.shared(
np.zeros((1, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(
np.zeros((1, 1), dtype='int32'), broadcastable=(False, True))
self._givens = \
{self.q_layers[0].input_var:
self.states_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :, :, :],
self.next_layers[0].input_var:
self.states_shared_next[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :, :, :],
self.rewards:
self.rewards_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :],
self.actions:
self.actions_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :]
}
if self.momentum != 0:
self._updates = layers.gen_updates_rmsprop_and_nesterov_momentum(\
self._loss, self._parameters, learning_rate=self.learning_rate,
rho=self.decay, momentum=self.momentum, epsilon=1e-6)
else:
self._updates = layers.gen_updates_rmsprop(self._loss,
self._parameters, learning_rate=self.learning_rate,
rho=self.decay, epsilon=1e-6)
self._train = theano.function([self._idx], self._loss,
givens=self._givens,
updates=self._updates)
self._compute_loss = theano.function([self._idx],
self._loss,
givens=self._givens)
self._compute_q_vals = \
theano.function([self.q_layers[0].input_var],
self.q_layers[-1].predictions(),
on_unused_input='ignore')
def __init__(self, nn_layers, L2_reg=0.0001, learning_rate=0.1, batch_size=32, discrete_target=False):
self.layers = nn_layers
#TODO: maybe initialize layers and set all inputs as prev outputs
self._batch_size = batch_size
# self._fprop = theano.function(
# [self.layers[0].input_var],
# self.output()
# )
self.parameters = layers.all_parameters(self.layers[-1])
# self.parameters = [param for layer in nn_layers[1:] for param in layer.params] #nn_layers[5].params + nn_layers[4].params + nn_layers[3].params + nn_layers[2].params + nn_layers[1].params
self.cost = self.layers[-1].error()
self.error_rate = self.layers[-1].error_rate()
self.regularization = sum([(W_or_b ** 2).sum() for W_or_b in self.parameters])
self.updates = layers.gen_updates_sgd(self.cost + self.regularization * L2_reg, self.parameters, learning_rate) # the last layer must be a layers.OutputLayer
# self.updates = layers.gen_updates_sgd(cost, self.parameters, learning_rate) # the last layer must be a layers.OutputLayer
# self.train_model = theano.function(
# inputs=[self.layers[0].input_var, self.layers[-1].target_var],
# updates=self.updates,
# outputs=self.cost
# )
self._idx = T.lscalar('idx')
self.x_shared = theano.shared(
np.zeros(self.layers[0].get_output_shape(), dtype=theano.config.floatX)
)
self.y_shared = theano.shared(
np.zeros(self.layers[-1].get_output_shape(), dtype=theano.config.floatX)
)
self.x_shared_validate = theano.shared(
np.zeros(self.layers[0].get_output_shape(), dtype=theano.config.floatX)
)
self.y_shared_validate = theano.shared(
np.zeros(self.layers[-1].get_output_shape(), dtype=theano.config.floatX)
)
self.x_shared_test = theano.shared(
np.zeros(self.layers[0].get_output_shape(), dtype=theano.config.floatX)
)
self.y_shared_test = theano.shared(
np.zeros(self.layers[-1].get_output_shape(), dtype=theano.config.floatX)
)
self.y_converted = T.cast(self.y_shared, 'int32') if discrete_target else self.y_shared
self.y_converted_validate = T.cast(self.y_shared_validate, 'int32') if discrete_target else self.y_shared
self.y_converted_test = T.cast(self.y_shared_test, 'int32') if discrete_target else self.y_shared
self._givens = {
self.layers[0].input_var: self.x_shared[self._idx * self._batch_size: (self._idx+1)*self._batch_size],
self.layers[-1].target_var: self.y_converted[self._idx * self._batch_size: (self._idx+1)*self._batch_size],
}
self._givens_validate = {
self.layers[0].input_var: self.x_shared_validate[self._idx * self._batch_size: (self._idx+1)*self._batch_size],
self.layers[-1].target_var: self.y_converted_validate[self._idx * self._batch_size: (self._idx+1)*self._batch_size],
}
self._givens_test= {
self.layers[0].input_var: self.x_shared_test[self._idx * self._batch_size: (self._idx+1)*self._batch_size],
self.layers[-1].target_var: self.y_converted_test[self._idx * self._batch_size: (self._idx+1)*self._batch_size],
}
self._train_model_batch = theano.function(
inputs=[self._idx],
updates=self.updates,
givens=self._givens,
outputs=self.cost
)
self._validate_model_batch = theano.function(
inputs=[self._idx],
givens=self._givens_validate,
outputs=self.error_rate
)
self._test_model_batch = theano.function(
inputs=[self._idx],
givens=self._givens_test,
outputs=self.error_rate
)
self._output_model_batch = theano.function(
inputs=[self._idx],
updates=self.updates,
givens=self._givens,
outputs=self.output()
)