def __init__(self, height, parameters):
# draw.circle is not anti-aliased and looks rather ugly.
# pygame.draw.circle(ATOM_IMG, (0, 255, 0), (15, 15), 15)
# gfxdraw.aacircle looks a bit better.
ATOM_IMG = pygame.Surface((21, 21), pygame.SRCALPHA)
transparancy = random.randint(150, 230)
pygame.gfxdraw.aacircle(ATOM_IMG, 10, 10, 10,
(0, 120, 150, transparancy))
# pygame.gfxdraw.filled_circle(ATOM_IMG, 10, 10, 11, WHITE)
pygame.gfxdraw.filled_circle(ATOM_IMG, 10, 10, 10,
(0, 120, 150, transparancy))
pygame.sprite.Sprite.__init__(self)
# self.picture = pygame.image.load("flappy_bird.png")
# self.image = pygame.transform.scale(self.picture, (45, 45))
self.image = ATOM_IMG # pygame surface you can draw on, 50x50 pixels
# self.image = self.image.convert_alpha()
# self.image.fill(WHITE)
self.rect = self.image.get_rect() # builds rectangle around surface
# starts bird at 0 x coordinate and middle of screen
self.rect.center = (100, height / 2)
self.WINDOW_HEIGHT = height
self.y_before_jump = 35 # max
self.score = 0 # the min. score (not getting through first pipe)
self.fitness = 0
self.velocity = 0
if parameters == None:
self.brain = nnet.NeuralNetwork(brain_dims, None)
else:
self.brain = nnet.NeuralNetwork(brain_dims, parameters)
def run():
# Fetch data
digits = sklearn.datasets.load_digits()
X_train = digits.data
X_train /= np.max(X_train)
y_train = digits.target
n_classes = np.unique(y_train).size
# Setup multi-layer perceptron
nn = nnet.NeuralNetwork(layers=[
nnet.Linear(
n_out=50,
weight_scale=0.1,
weight_decay=0.002,
),
nnet.Activation('relu'),
nnet.Linear(
n_out=n_classes,
weight_scale=0.1,
weight_decay=0.002,
),
nnet.LogRegression(),
], )
# Verify network for correct back-propagation of parameter gradients
print('Checking gradients')
nn.check_gradients(X_train[:100], y_train[:100])
# Train neural network
print('Training neural network')
nn.fit(X_train, y_train, learning_rate=0.1, max_iter=25, batch_size=32)
# Evaluate on training data
error = nn.error(X_train, y_train)
print('Training error rate: %.4f' % error)
def mk_samples(Nx, Ny, nb_samples, rect=False):
X = np.ndarray(shape=(nb_samples, 5))
Y = np.ndarray(shape=(nb_samples, 5))
ind = 0
filename = '%s/nnet/ACASXU_run2a_%d_%d_batch_2000.nnet' % (
os.path.dirname(__file__) or '.', Nx, Ny)
nn = nnet.NeuralNetwork(filename, True, True)
X = np.random.uniform(nn.input_min, nn.input_max,
[nb_samples, nn.output_dims])
Y = np.zeros(nb_samples)
ind = 0
while ind < nb_samples:
outputs = nn.evaluate(X[ind])
if test_properties(Nx, Ny, X[ind], outputs) is False:
continue
Y[ind] = np.argmin(outputs)
if rect:
rho, theta, psi, v_own, v_int = X[ind]
X[ind][0] = rho * np.cos(theta)
X[ind][1] = rho * np.sin(theta)
ind += 1
# ensure all labels are present in data
s = set(Y)
for lbl in range(5):
if lbl not in s:
Y[lbl] = lbl
return X, Y
def run():
# Fetch data
mnist = sklearn.datasets.fetch_mldata('MNIST original', data_home='./data')
split = 60000
X_train = np.reshape(mnist.data[:split], (-1, 1, 28, 28)) / 255.0
y_train = mnist.target[:split]
X_test = np.reshape(mnist.data[split:], (-1, 1, 28, 28)) / 255.0
y_test = mnist.target[split:]
n_classes = np.unique(y_train).size
# Downsample training data
n_train_samples = 3000
train_idxs = np.random.random_integers(0, split - 1, n_train_samples)
X_train = X_train[train_idxs, ...]
y_train = y_train[train_idxs, ...]
# Setup convolutional neural network
nn = nnet.NeuralNetwork(layers=[
nnet.Conv(
n_feats=12,
filter_shape=(5, 5),
strides=(1, 1),
weight_scale=0.1,
weight_decay=0.001,
),
nnet.Activation('relu'),
nnet.Pool(
pool_shape=(2, 2),
strides=(2, 2),
mode='max',
),
nnet.Conv(
n_feats=16,
filter_shape=(5, 5),
strides=(1, 1),
weight_scale=0.1,
weight_decay=0.001,
),
nnet.Activation('relu'),
nnet.Flatten(),
nnet.Linear(
n_out=n_classes,
weight_scale=0.1,
weight_decay=0.02,
),
nnet.LogRegression(),
], )
# Train neural network
t0 = time.time()
nn.fit(X_train, y_train, learning_rate=0.05, max_iter=3, batch_size=32)
t1 = time.time()
print('Duration: %.1fs' % (t1 - t0))
# Evaluate on test data
error = nn.error(X_test, y_test)
print('Test error rate: %.4f' % error)
def three_layer_test():
Xtr, Ytr, Xte, Yte, label_names = test.get_cifar10_dataset()
# Reshape each data point to be a 1-dimensional array, for a plain neural network.
Xtr = Xtr.reshape(50000, 32 * 32 * 3)
Xte = Xte.reshape(10000, 32 * 32 * 3)
# PRE-PROCESSING
Xtr = test.normalize(Xtr)
Xte = test.normalize(Xte)
mean = np.mean(np.concatenate([Xtr, Xte]), axis=0)
Xtr = Xtr - mean
Xte = Xte - mean
Xtr = test.append_zeros(Xtr)
Xte = test.append_zeros(Xte)
# Neural Net
nn = nnet.NeuralNetwork(Xtr.shape[1])
nn.batch_size = 512
nn.set_training_set(Xtr.T, Ytr)
nn.set_testing_set(Xte.T, Yte)
nn.add_layer(
nnet.FullyConnectedLayer(pass_type="test|train",
output_size=100,
initialization_type='xavier'))
nn.add_layer(nnet.BatchNormalizationLayer(pass_type="test|train"))
nn.add_layer(
nnet.ActivationLayer(pass_type="test|train",
activation_type="leaky_relu"))
nn.add_layer(
nnet.FullyConnectedLayer(pass_type="test|train",
output_size=50,
initialization_type='xavier'))
nn.add_layer(nnet.BatchNormalizationLayer(pass_type="test|train"))
nn.add_layer(
nnet.ActivationLayer(pass_type="test|train",
activation_type="leaky_relu"))
nn.add_layer(
nnet.FullyConnectedLayer(pass_type="test|train",
output_size=10,
initialization_type='xavier'))
nn.add_layer(nnet.SoftmaxLayer(pass_type="test"))
nn.add_layer(nnet.LossLayer(pass_type="train"))
# Print out each layer's information in order, then train.
nn.print_info()
final_loss, final_accuracy = nn.train(iterations=300)
assert (final_loss < 1)
assert (final_accuracy > 0.30)
def run():
# Fetch data
mnist = sklearn.datasets.fetch_mldata('MNIST original', data_home='./data')
split = 60000
X_train = mnist.data[:split] / 255.0
y_train = mnist.target[:split]
X_test = mnist.data[split:] / 255.0
y_test = mnist.target[split:]
n_classes = np.unique(y_train).size
# Downsample training data
n_train_samples = 10000
train_idxs = np.random.random_integers(0, split - 1, n_train_samples)
X_train = X_train[train_idxs, ...]
y_train = y_train[train_idxs, ...]
# Setup multi-layer perceptron
nn = nnet.NeuralNetwork(layers=[
nnet.Linear(
n_out=100,
weight_scale=0.2,
weight_decay=0.004,
),
nnet.Activation('relu'),
nnet.Linear(
n_out=50,
weight_scale=0.2,
weight_decay=0.004,
),
nnet.Activation('relu'),
nnet.Linear(
n_out=n_classes,
weight_scale=0.2,
weight_decay=0.004,
),
nnet.LogRegression(),
], )
# Train neural network
t0 = time.time()
nn.fit(X_train, y_train, learning_rate=0.1, max_iter=5, batch_size=64)
t1 = time.time()
print('Duration: %.1fs' % (t1 - t0))
# Evaluate on test data
error = nn.error(X_test, y_test)
print('Test error rate: %.4f' % error)
def create_random_brain():
""" Create a brain with randomized parameters """
brain = nnet.NeuralNetwork()
# Logic neurons
logic_0 = nnet.Neuron("lgc_0")
logic_1 = nnet.Neuron("lgc_1")
logic_2 = nnet.Neuron("lgc_2")
logic_3 = nnet.Neuron("lgc_3")
# Input (hunger) neuron turns on as health goes down
hunger = nnet.Neuron("hunger", is_input=True)
# Input (smell) neurons for nearby Food
food_left = nnet.Neuron("fd_lft", is_input=True)
food_right = nnet.Neuron("fd_rght", is_input=True)
# Input (sight) neurons for nearby Agents
agent_left = nnet.Neuron("agnt_lft", is_input=True)
agent_right = nnet.Neuron("agnt_rght", is_input=True)
# Output (movement) neurons
move_left = nnet.Neuron("mv_lft")
move_right = nnet.Neuron("mv_rght")
# Output (attack) neuron
attack = nnet.Neuron("atk")
# Input layer
brain.add_neuron(agent_left)
brain.add_neuron(food_left)
brain.add_neuron(hunger)
brain.add_neuron(food_right)
brain.add_neuron(agent_right)
# Hidden layer
brain.add_neuron(logic_0)
brain.add_neuron(logic_1)
brain.add_neuron(logic_2)
brain.add_neuron(logic_3)
# Output layer
brain.add_neuron(move_left)
brain.add_neuron(attack)
brain.add_neuron(move_right)
# Input to hidden layer: left side
brain.add_synapse(
nnet.Synapse(agent_left, logic_0, util.rand(-0.25, 1)))
brain.add_synapse(nnet.Synapse(agent_left, logic_1, util.rand(-1, 1)))
brain.add_synapse(
nnet.Synapse(food_left, logic_0, util.rand(-0.25, 0.25)))
brain.add_synapse(nnet.Synapse(food_left, logic_1, util.rand(-0.25,
1)))
# Input to hidden layer: center
brain.add_synapse(nnet.Synapse(hunger, logic_1, util.rand(-0.25,
0.75)))
brain.add_synapse(nnet.Synapse(hunger, logic_2, util.rand(-0.25,
0.75)))
# Input to hidden layer: right side
brain.add_synapse(
nnet.Synapse(food_right, logic_2, util.rand(-0.25, 1)))
brain.add_synapse(
nnet.Synapse(food_right, logic_3, util.rand(-0.25, 0.25)))
brain.add_synapse(
nnet.Synapse(agent_right, logic_2, util.rand(-0.25, 0.25)))
brain.add_synapse(
nnet.Synapse(agent_right, logic_3, util.rand(-0.25, 1)))
# Hidden to output layer: left side
brain.add_synapse(
nnet.Synapse(logic_0, move_left, util.rand(-0.25, 0.25)))
brain.add_synapse(nnet.Synapse(logic_0, attack, util.rand(-0.5, 0.5)))
brain.add_synapse(nnet.Synapse(logic_1, move_left, util.rand(-0.25,
1)))
brain.add_synapse(nnet.Synapse(logic_1, attack, util.rand(-0.25,
0.25)))
# Hidden to output layer: right side
brain.add_synapse(nnet.Synapse(logic_2, attack, util.rand(-0.25,
0.25)))
brain.add_synapse(
nnet.Synapse(logic_2, move_right, util.rand(-0.25, 1)))
brain.add_synapse(nnet.Synapse(logic_3, attack, util.rand(-0.5, 0.5)))
brain.add_synapse(
nnet.Synapse(logic_3, move_right, util.rand(-0.25, 0.25)))
return brain
k_fold = KFold(n_splits=10)
result = []
for index, nf in enumerate(n_feats):
fold_result = []
print('*** Starting Test of feat [', n_feats[index], ']...')
# SETUP one-layer CONVnet
nn = nnet.NeuralNetwork(layers=[
nnet.Conv(
n_feats=nf,
filter_shape=(5, 5),
strides=(1, 1),
weight_scale=0.1,
),
nnet.Activation('relu'),
nnet.Flatten(),
nnet.Linear(
n_out=n_classes,
weight_scale=0.1,
),
nnet.LogRegression(),
], )
# TRAINING
for train_indices, valid_indices in k_fold.split(np.array(X_train)):
np.random.shuffle(train_indices)
#print(train_indices, valid_indices)
X_tr = X_train[train_indices, ...]
Y_tr = Y_train[train_indices, ...]
X_val = X_train[valid_indices, ...]
def __init__(self, model_config):
self.model_config = model_config
self.nnet = nnet.NeuralNetwork(model_config)
def optimize_filter(n_train_samples,
n_classes,
X_train,
Y_train,
split,
weight_decay=0.0):
train_idxs = np.random.randint(0, split - 1, n_train_samples)
n_feats = [2, 4, 6, 8, 12, 16] # for the second layer!
X_train = X_train[train_idxs, ...]
Y_train = Y_train[train_idxs, ...]
one_layer_result = []
for index, nf in enumerate(n_feats):
fold_result = []
print('*** Starting 1-layer test of feat [', nf, ']...')
# SETUP one-layer CONVnet
nn = nnet.NeuralNetwork(layers=[
nnet.Conv(
n_feats=nf,
filter_shape=(5, 5),
strides=(1, 1),
weight_scale=0.1,
weight_decay=weight_decay,
),
nnet.Activation('relu'),
nnet.Flatten(),
nnet.Linear(
n_out=n_classes,
weight_scale=0.1,
),
nnet.LogRegression(),
], )
# TRAINING
for train_indices, valid_indices in k_fold.split(np.array(X_train)):
np.random.shuffle(train_indices)
#print(train_indices, valid_indices)
X_tr = X_train[train_indices, ...]
Y_tr = Y_train[train_indices, ...]
X_val = X_train[valid_indices, ...]
Y_val = Y_train[valid_indices, ...]
# Train neural network
t0 = time.time()
# TODO: max_iter 50
nn.fit(X_tr, Y_tr, learning_rate=0.1, max_iter=50, batch_size=30)
t1 = time.time()
# Evaluate on test data
error = nn.error(X_val, Y_val)
fold_result.append(error)
print('Duration: %.1fs' % (t1 - t0))
print('Valid error rate: %.4f' % error)
# save the result for each n_feat
one_layer_result.append(np.mean(np.array(fold_result)))
best_one_layer = n_feats[np.argmin(one_layer_result)]
# Try two-layer CONVnet
two_layer_result = []
for index, nf in enumerate(n_feats):
fold_result = []
print('*** Starting 2-layers-test of feat [', nf, ']...')
# SETUP two-layers CONVnet
nn = nnet.NeuralNetwork(layers=[
nnet.Conv(
n_feats=best_one_layer,
filter_shape=(5, 5),
strides=(1, 1),
weight_scale=0.1,
weight_decay=weight_decay,
),
nnet.Activation('relu'),
nnet.Pool(
pool_shape=(2, 2),
strides=(2, 2),
mode='max',
),
nnet.Conv(
n_feats=nf,
filter_shape=(5, 5),
strides=(1, 1),
weight_scale=0.1,
weight_decay=weight_decay,
),
nnet.Activation('relu'),
nnet.Flatten(),
nnet.Linear(
n_out=n_classes,
weight_scale=0.1,
),
nnet.LogRegression(),
], )
# TRAINING
for train_indices, valid_indices in k_fold.split(np.array(X_train)):
np.random.shuffle(train_indices)
#print(train_indices, valid_indices)
X_tr = X_train[train_indices, ...]
Y_tr = Y_train[train_indices, ...]
X_val = X_train[valid_indices, ...]
Y_val = Y_train[valid_indices, ...]
# Train neural network
t0 = time.time()
# TODO: max_iter 50
nn.fit(X_tr, Y_tr, learning_rate=0.1, max_iter=50, batch_size=30)
t1 = time.time()
# Evaluate on test data
error = nn.error(X_val, Y_val)
fold_result.append(error)
print('Duration: %.1fs' % (t1 - t0))
print('Valid error rate: %.4f' % error)
# save the result for each n_feat
two_layer_result.append(np.mean(np.array(fold_result)))
best_two_layer = n_feats[np.argmin(two_layer_result)]
print('One-layer result :', one_layer_result)
print('Two-layer result :', two_layer_result)
print('Two-Layer Optimum N_feat Value :', best_one_layer, best_two_layer)
return best_one_layer, best_two_layer
X.append(x / 1000)
Y.append(y / 1000)
L.append(label)
return ax.scatter(X, Y, c=L, cmap=ListedColormap(colors))
if __name__ == '__main__':
font = {'family': 'sans', 'size': 16}
plt.rc('font', **font)
filename = '%s/nnet/ACASXU_run2a_1_1_batch_2000.nnet' % (
os.path.dirname(__file__) or '.')
nn = nnet.NeuralNetwork(filename, True, True)
filename = '%s/models/ACASXU_1_1.json' % (os.path.dirname(__file__) or '.')
gbm = vote.Ensemble.from_file(filename)
fig, (ax1, ax2) = plt.subplots(2, sharex=True, sharey=True)
render_prediction(ax1, nn.evaluate)
render_prediction(ax2, lambda xvec: gbm.eval(*xvec))
legend_elements = [
Patch(color=colors[0], label='COC'),
Patch(color=colors[1], label='WL'),
Patch(color=colors[2], label='WR'),
Patch(color=colors[3], label='SL'),
Patch(color=colors[4], label='SR')
