def _build_model(self):
model = self.load()
if model is None:
main_input = Input(shape=self.input_dim, name='main_input')
block = self._add_conv_block(
prev_block=main_input,
filters=self.layers_metadata[0]['filters'],
kernel_size=self.layers_metadata[0]['kernel_size'])
if len(self.layers_metadata) > 1:
for metadata in self.layers_metadata[1:]:
block = self._add_residual_block(
prev_block=block,
filters=metadata['filters'],
kernel_size=metadata['kernel_size'])
value_head = self._add_value_head(prev_block=block)
policy_head = self._add_policy_head(prev_block=block)
model = Model(inputs=main_input, outputs=[value_head, policy_head])
model.compile(loss={
'value_head': 'mse',
'policy_head': 'categorical_crossentropy'
},
optimizer=SGD(lr=self.learning_rate),
loss_weights={
'value_head': 0.5,
'policy_head': 0.5
},
metrics={
'value_head': 'mse',
'policy_head': 'acc'
})
# invoke following 3 protected methods to make the model can be shared by multiple threads
model._make_predict_function()
model._make_train_function()
model._make_test_function()
return model
def make_model():
_x = Input((10, ))
_y = Dense(10)(_x)
_m = Model(_x, _y)
_m.compile('adam', 'mean_squared_error')
_m._make_train_function()
return _m
def create_test_model(In=(10, ), Out=1, learning_rate=1e-3):
net_input = Input(shape=In, name='Input')
x = Dense(Out, activation='sigmoid', name='Output')(net_input)
model = Model(inputs=net_input, outputs=x)
adam = Adam(lr=learning_rate)
model.compile(optimizer=adam, loss='mse')
model._make_train_function()
return model
class OldDroneNet(UnitNet):
_in_channel = 18
_out_channel = 6
def __init__(self, loading=False):
self._in_channel = OldDroneNet._in_channel
self._out_channel = OldDroneNet._out_channel
# super(DroneNet,self).__init__(loading)
self.session = KTF.get_session()
self.graph = tf.get_default_graph()
with self.session.as_default():
with self.graph.as_default():
self.inp = Input((WINDOW_SIZE, WINDOW_SIZE, self._in_channel),
dtype='float32')
# self.conv1=conv_block(self.inp,1,True)
self.conv1 = Conv2D(32, (1, 1),
activation='relu',
padding='same')(self.inp)
self.pool1 = MaxPooling2D((2, 2))(self.conv1)
self.conv2 = conv_block(self.pool1, 1)
self.pool2 = MaxPooling2D((2, 2))(self.conv2)
self.conv3 = conv_block(self.pool2, 1)
self.pool3 = MaxPooling2D((2, 2))(self.conv3)
self.deconv1 = deconv_block(self.pool3, 2)
self.up1 = UpSampling2D((2, 2))(self.deconv1)
self.deconv2 = deconv_block(
Concatenate(axis=3)([self.up1, self.conv3]), 1)
self.up2 = UpSampling2D((2, 2))(self.deconv2)
self.deconv3 = deconv_block(
Concatenate(axis=3)([self.up2, self.conv2]), 1)
self.up3 = UpSampling2D((2, 2))(self.deconv3)
# self.deconv4=deconv_block(Concatenate(axis=3)([self.up3,self.conv1]),1)
self.deconv4 = Conv2DTranspose(64, (3, 3),
activation='relu',
padding='same')(self.up3)
self.out = Conv2DTranspose(DroneNet._out_channel, (3, 3),
activation='softmax',
padding='same')(self.deconv4)
# self.deconv6.set_shape([None,WINDOW_SIZE,WINDOW_SIZE,OUT_CHANNEL])
self.model = Model(inputs=self.inp, outputs=self.out)
# print(self.model.summary())
opt = Adam(lr=0.0001)
self.model.compile(optimizer=opt,
loss='categorical_crossentropy')
self.model._make_predict_function()
self.model._make_test_function()
self.model._make_train_function()
if (os.path.isfile('DroneNet.h5') and loading == True):
self.model = load_model("DroneNet.h5")
# self.model._make_prediction_function()
@staticmethod
def msg2mask(disGame, msg):
ans = numpy.zeros([WINDOW_SIZE, WINDOW_SIZE, OldDroneNet._out_channel])
ans[WINDOW_SIZE // 2, WINDOW_SIZE // 2, 0] = 1
ans[:, :, 5] = (msg[1][1] or msg[1][2])
x = msg[0][0]
y = msg[0][1]
ax = max(0, WINDOW_SIZE // 2 - x)
ay = max(0, WINDOW_SIZE // 2 - y)
hei, wid = disGame.regions.shape
ans[ax:min(WINDOW_SIZE, hei - x + WINDOW_SIZE // 2),
ay:min(WINDOW_SIZE, wid - y + WINDOW_SIZE // 2),
1] = disGame.regions[
max(0, x - WINDOW_SIZE // 2):min(x + WINDOW_SIZE // 2, hei),
max(0, y - WINDOW_SIZE // 2):min(y + WINDOW_SIZE // 2, wid)]
if (msg[1][3] == 0):
for i in msg[2]:
ans[i[0][0] - x + WINDOW_SIZE // 2,
i[0][1] - y + WINDOW_SIZE // 2, 4] = 1 - i[2]
for i in msg[3]:
ans[i[0][0] - x + WINDOW_SIZE // 2,
i[0][1] - y + WINDOW_SIZE // 2, 4] = 1 - i[2]
for i in msg[4]:
if (i[0]):
ans[i[1][0] - x + WINDOW_SIZE // 2,
i[1][1] - y + WINDOW_SIZE // 2, 3] = 1
for i in msg[5]:
ans[i[1][0] - x + WINDOW_SIZE // 2, i[1][1] - y + WINDOW_SIZE // 2,
3] = 1
return ans
@staticmethod
def y2state(ind):
ans = numpy.zeros([WINDOW_SIZE, WINDOW_SIZE, OldDroneNet._out_channel])
if (ind[2] in [0, 5]):
ans[WINDOW_SIZE // 2, WINDOW_SIZE // 2, ind[2]] = 1
else:
ans[shrinkScr(ind[0]), shrinkScr(ind[1]), ind[2]] = 1
return ans
@staticmethod
def msg2state(disGame, msg):
ans = numpy.zeros([WINDOW_SIZE, WINDOW_SIZE, OldDroneNet._in_channel])
x, y = msg[0]
ans[:, :, 11] = msg[1][0]
ans[:, :, 16] = msg[1][1]
ans[:, :, 17] = msg[1][2]
for u in msg[2]:
nx = u[0][0] - x + WINDOW_SIZE // 2
ny = u[0][1] - y + WINDOW_SIZE // 2
if (u[2]):
ans[nx, ny, 5] = 1
elif (u[3]):
ans[nx, ny, 6] = 1
else:
ans[nx, ny, 4] = 1
ans[nx, ny, 13] = u[1]
ans[nx, ny, 14] = u[4]
ans[nx, ny, 15] = u[5]
for u in msg[3]:
nx = u[0][0] - x + WINDOW_SIZE // 2
ny = u[0][1] - y + WINDOW_SIZE // 2
if (u[2]):
ans[nx, ny, 5] = 1
elif (u[3]):
ans[nx, ny, 6] = 1
else:
ans[nx, ny, 4] = 1
ans[nx, ny, 12] = u[1]
for u in msg[4]:
nx = u[1][0] - x + WINDOW_SIZE // 2
ny = u[1][1] - y + WINDOW_SIZE // 2
if (u[0]):
ans[nx, ny, 7] = 1
else:
ans[nx, ny, 8] = 1
for u in msg[5]:
nx = u[0] - x + WINDOW_SIZE // 2
ny = u[1] - y + WINDOW_SIZE // 2
# print(u,x,y,nx,ny)
ans[nx, ny, 9] = 1
ax = max(0, WINDOW_SIZE // 2 - x)
ay = max(0, WINDOW_SIZE // 2 - y)
X = disGame.hground.shape[0]
Y = disGame.hground.shape[1]
'''
print(ax,min(WINDOW_SIZE,X-x+WINDOW_SIZE//2),
ay,min(WINDOW_SIZE,Y-y+WINDOW_SIZE//2),
max(0,x-WINDOW_SIZE//2),min(x+WINDOW_SIZE//2,X),
max(0,y-WINDOW_SIZE//2),min(y+WINDOW_SIZE//2,Y))
'''
ans[ax:min(WINDOW_SIZE, X - x + WINDOW_SIZE // 2),
ay:min(WINDOW_SIZE, Y - y + WINDOW_SIZE // 2),
10] = disGame.hground[
max(0, x - WINDOW_SIZE // 2):min(x + WINDOW_SIZE // 2, X),
max(0, y - WINDOW_SIZE // 2):min(y + WINDOW_SIZE // 2, Y)]
ans[ax:min(WINDOW_SIZE, X - x + WINDOW_SIZE // 2),
ay:min(WINDOW_SIZE, Y - y + WINDOW_SIZE // 2),
0] = disGame.regions[
max(0, x - WINDOW_SIZE // 2):min(x + WINDOW_SIZE // 2, X),
max(0, y - WINDOW_SIZE // 2):min(y + WINDOW_SIZE // 2, Y)]
return ans
class Wide_ResNet(object):
'''
Modified from:
https://gist.github.com/kashif/0ba0270279a0f38280423754cea2ee1e
'''
def __init__(self, config):
self.verbose = config['verbose']
self.size = config['size']
self.rank = config['rank']
self.name = 'Wide_ResNet'
# data
from data.cifar10 import Cifar10_data
self.data = Cifar10_data(verbose=False)
self.build_model()
# iter related
self.current_t = 0
self.last_one_t = False
self.current_v = 0
self.last_one_v = False
self.n_subb = 1
self.n_epochs = nb_epoch
self.epoch = 0
def build_model(self):
img_input = Input(shape=(img_channels, img_rows, img_cols))
# one conv at the beginning (spatial size: 32x32)
x = ZeroPadding2D((1, 1))(img_input)
x = Convolution2D(16, nb_row=3, nb_col=3)(x)
# Stage 1 (spatial size: 32x32)
x = bottleneck(x, n, 16, 16 * k, dropout=0.3, subsample=(1, 1))
# Stage 2 (spatial size: 16x16)
x = bottleneck(x, n, 16 * k, 32 * k, dropout=0.3, subsample=(2, 2))
# Stage 3 (spatial size: 8x8)
x = bottleneck(x, n, 32 * k, 64 * k, dropout=0.3, subsample=(2, 2))
x = BatchNormalization(mode=0, axis=1)(x)
x = Activation('relu')(x)
x = AveragePooling2D((8, 8), strides=(1, 1))(x)
x = Flatten()(x)
preds = Dense(nb_classes, activation='softmax')(x)
self.model = Model(input=img_input, output=preds)
self.keras_get_params()
def compile_iter_fns(self, *args, **kwargs):
try:
sync_type = kwargs['sync_type']
except:
raise RuntimeError(
'Keras wresnet needs sync_type keyword argument')
if sync_type != 'avg':
raise RuntimeError(
'currently wresnet only support compiling with sync_type avg, add BSP_SYNC_TYPE=avg in your session cfg'
)
import time
start = time.time()
self.model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
self.shared_lr = self.model.optimizer.lr
self.base_lr = self.shared_lr.get_value()
if self.verbose: print('Compiling......')
self.model._make_train_function()
self.model._make_test_function()
if self.verbose: print('Compile time: %.3f s' % (time.time() - start))
self.data.batch_data(self.model, batch_size)
self.data.extend_data(rank=self.rank, size=self.size)
self.data.shuffle_data(mode='train', common_seed=1234)
self.data.shuffle_data(mode='val')
self.data.shard_data(mode='train', rank=self.rank,
size=self.size) # to update data.n_batch_train
self.data.shard_data(mode='val', rank=self.rank,
size=self.size) # to update data.n_batch_val
def train_iter(self, count, recorder):
if self.current_t == 0:
self.data.shuffle_data(mode='train', common_seed=self.epoch)
self.data.shard_data(mode='train', rank=self.rank, size=self.size)
recorder.start()
cost, acc = self.model.train_function(
self.data.train_batches_shard[self.current_t])
recorder.train_error(count, cost, 1.0 - acc)
recorder.end('calc')
if self.current_t == self.data.n_batch_train - 1:
self.last_one_t = True
else:
self.last_one_t = False
if self.last_one_t == False:
self.current_t += 1
else:
self.current_t = 0
def val_iter(self, count, recorder):
if self.current_v == 0:
self.data.shuffle_data(mode='val')
self.data.shard_data(mode='val', rank=self.rank, size=self.size)
cost, acc = self.model.test_function(
self.data.val_batches_shard[self.current_v])
recorder.val_error(count, cost, 1.0 - acc, 0)
if self.current_v == self.data.n_batch_val - 1:
self.last_one_v = True
else:
self.last_one_v = False
if self.last_one_v == False:
self.current_v += 1
else:
self.current_v = 0
def reset_iter(self, mode):
'''used at the begininig of another mode'''
if mode == 'train':
self.current_t = 0
self.subb_t = 0
self.last_one_t = False
else:
self.current_v = 0
self.subb_v = 0
self.last_one_v = False
def adjust_hyperp(self, epoch):
'to be called once per epoch'
if lr_policy == 'step':
if epoch in lr_step:
tuned_base_lr = self.shared_lr.get_value() / 10.
self.shared_lr.set_value(np.float32(tuned_base_lr))
def scale_lr(self, size):
self.shared_lr.set_value(np.array(self.base_lr * size,
dtype='float32'))
def cleanup(self):
pass
def keras_get_params(self):
self.params = []
for l in self.model.layers:
self.params.extend(l.trainable_weights)
class PseudoLabel:
"""
Pseudo-label Class
"""
def __init__(self,
image_width=256,
image_height=256,
train_data_directory="../data/train",
validation_data_directory="../data/validation",
test_data_directory="../data/test",
no_label_data_directory="../data/no_label",
batch_size=8,
pseudo_label_batch_size=16,
epochs=1,
class_names=[],
architecture="VGG16",
image_channels=3,
learnin_rate=0.001,
save_heights=False,
transfer_learning={
'use_transfer_learning': False,
'fine_tuning': None
},
optimizer='SGD',
metrics_list=['acc'],
h5_filename=None,
class_labels=None,
alpha=0.5,
print_pseudo_generate=False):
"""
Pseudo-label class construtor
"""
# Atributes declarations
self.image_width = image_width
self.image_height = image_height
self.train_data_directory = train_data_directory
self.validation_data_directory = validation_data_directory
self.test_data_directory = test_data_directory
self.no_label_data_directory = no_label_data_directory
self.batch_size = batch_size
self.pseudo_label_batch_size = pseudo_label_batch_size
self.epochs = epochs
self.class_names = class_names
self.architecture = architecture
self.image_channels = image_channels
self.learning_rate = learnin_rate
self.use_transfer_learning = transfer_learning.get(
'use_transfer_learning')
self.fine_tuning_rate = transfer_learning.get('fine_tuning')
self.optimizer = optimizer
self.metrics_list = metrics_list
self.model = None
self.train_generator = None
self.validation_generator = None
self.h5_filename = h5_filename
self.class_labels = class_labels
self.alpha = alpha
self.print_pseudo_generate = print_pseudo_generate
# Make your model and dataset
self.make_data_generators()
self.make_model(architecture=self.architecture,
use_transfer_learning=self.use_transfer_learning,
fine_tuning_rate=self.fine_tuning_rate,
optimizer=self.optimizer,
metrics_list=self.metrics_list)
self.generate_h5_filename()
def make_model(self,
architecture=None,
use_transfer_learning=False,
fine_tuning_rate=None,
optimizer='SGD',
metrics_list=['accuracy']):
"""
Create your CNN keras model
Arguments:
architecture (str): architecture of model
"""
# Validations
for metric in metrics_list:
if metric not in LIST_OF_ACCEPTABLES_METRICS:
raise ValueError("The specified metric \'" + metric +
"\' is not supported")
if optimizer not in LIST_OF_ACCEPTABLES_OPTIMIZERS.keys():
raise ValueError("The specified optimizer \'" + optimizer +
"\' is not supported!")
if architecture not in LIST_OF_ACCEPTABLES_ARCHITECTURES.keys():
raise ValueError("The specified architecture \'" + architecture +
"\' is not supported!")
else:
if use_transfer_learning and not 0 <= fine_tuning_rate <= 100:
raise ValueError(
"The fine tuning rate must be beetween 0 and 100!")
if use_transfer_learning and fine_tuning_rate == None:
raise ValueError(
"You need to specify a fine tuning rate if you're using transfer learning!"
)
# With transfer learning
if use_transfer_learning:
self.model = LIST_OF_ACCEPTABLES_ARCHITECTURES.get(architecture)(
weights="imagenet",
include_top=False,
input_shape=(self.image_height, self.image_width,
self.image_channels))
last_layers = len(self.model.layers) - \
int(len(self.model.layers) * (fine_tuning_rate / 100.))
for layer in self.model.layers[:last_layers]:
layer.trainable = False
# Without transfer learning
else:
self.model = LIST_OF_ACCEPTABLES_ARCHITECTURES.get(architecture)(
weights=None,
include_top=False,
input_shape=(self.image_height, self.image_width,
self.image_channels))
for layer in self.model.layers:
layer.trainable = True
# Adding the custom Layers
new_custom_layers = self.model.output
new_custom_layers = Flatten()(new_custom_layers)
new_custom_layers = Dense(1024, activation="relu")(new_custom_layers)
new_custom_layers = Dropout(0.5)(new_custom_layers)
new_custom_layers = Dense(1024, activation="relu")(new_custom_layers)
try:
predictions = Dense(self.train_generator.num_classes,
activation="softmax")(new_custom_layers)
except AttributeError:
predictions = Dense(self.train_generator.num_class,
activation="softmax")(new_custom_layers)
# Create the final model
self.model = Model(inputs=self.model.input, outputs=predictions)
# Compile model
self.model.compile(
loss=self.pseudo_label_loss_function,
optimizer=LIST_OF_ACCEPTABLES_OPTIMIZERS.get(optimizer)(
lr=self.learning_rate),
metrics=metrics_list)
def pseudo_label_loss_function(self, y_true, y_pred):
loss_true_label = self.cross_entropy(y_true[:self.batch_size],
y_pred[:self.batch_size])
loss_pseudo_label = (self.cross_entropy(y_true[self.batch_size:],
y_pred[self.batch_size:]))
return (loss_true_label / self.batch_size) + (
self.alpha * (loss_pseudo_label / self.pseudo_label_batch_size))
def cross_entropy(self, targets, predictions, epsilon=1e-12):
predictions = K.clip(predictions, epsilon, 1. - epsilon)
N = predictions.shape[0]
log1 = K.log(predictions + 1e-9)
sum1 = K.sum(targets * log1)
if (predictions.shape[0].value is not None):
return -K.sum(sum1) / N
else:
return -K.sum(sum1)
def make_data_generators(self, use_data_augmentation=False):
"""
Function that initiate the train, validation and test generators with data augumentation
"""
self.train_generator = ImageDataGenerator().flow_from_directory(
self.train_data_directory,
target_size=(self.image_height, self.image_width),
color_mode='rgb',
classes=self.class_labels,
batch_size=self.batch_size,
shuffle=True,
class_mode="categorical")
self.test_generator = ImageDataGenerator().flow_from_directory(
self.test_data_directory,
target_size=(self.image_height, self.image_width),
color_mode='rgb',
batch_size=1,
shuffle=False,
class_mode="categorical")
self.validation_generator = ImageDataGenerator().flow_from_directory(
self.validation_data_directory,
target_size=(self.image_height, self.image_width),
color_mode='rgb',
batch_size=self.batch_size,
shuffle=True,
class_mode="categorical")
self.no_label_generator = ImageDataGenerator().flow_from_directory(
self.no_label_data_directory,
target_size=(self.image_height, self.image_width),
color_mode='rgb',
batch_size=self.pseudo_label_batch_size,
shuffle=False,
class_mode="categorical")
try:
self.no_label_generator.num_classes = self.validation_generator.num_classes
except AttributeError:
self.no_label_generator.num_class = self.validation_generator.num_class
def generate_h5_filename(self):
"""
Generate the .h5 filename. The .h5 file is the file that contains your trained model
"""
if self.fine_tuning_rate == 100:
self.h5_filename = self.architecture + \
'_transfer_learning'
elif self.fine_tuning_rate == None:
self.h5_filename = self.architecture + \
'_without_transfer_learning'
else:
self.h5_filename = self.architecture + \
'_fine_tunning_' + str(self.fine_tuning_rate)
################################################################################
# Semi-supervised - Pseudo label approach
################################################################################
def fit_with_pseudo_label(self,
steps_per_epoch,
validation_steps=None,
use_checkpoints=True,
class_labels=None,
verbose=1,
use_multiprocessing=False,
shuffle=False,
workers=1,
max_queue_size=10):
# Default value if validation steps is none
if (validation_steps == None):
validation_steps = self.validation_generator.samples // self.batch_size
wait_time = 0.01 # in seconds
self.model._make_train_function()
# Create a checkpoint callback
checkpoint = ModelCheckpoint("../models_checkpoints/" +
str(self.h5_filename) + ".h5",
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=True,
mode='auto',
period=1)
# Generate callbacks
callback_list = []
if use_checkpoints:
callback_list.append(checkpoint)
# Init train counters
epoch = 0
validation_data = self.validation_generator
do_validation = bool(validation_data)
self.model._make_train_function()
if do_validation:
self.model._make_test_function()
val_gen = (hasattr(validation_data, 'next')
or hasattr(validation_data, '__next__')
or isinstance(validation_data, Sequence))
if (val_gen and not isinstance(validation_data, Sequence)
and not validation_steps):
raise ValueError('`validation_steps=None` is only valid for a'
' generator based on the `keras.utils.Sequence`'
' class. Please specify `validation_steps` or use'
' the `keras.utils.Sequence` class.')
# Prepare display labels.
out_labels = self.model.metrics_names
callback_metrics = out_labels + ['val_' + n for n in out_labels]
# Prepare train callbacks
self.model.history = cbks.History()
callbacks = [cbks.BaseLogger()] + (callback_list or []) + \
[self.model.history]
if verbose:
callbacks += [cbks.ProgbarLogger(count_mode='steps')]
callbacks = cbks.CallbackList(callbacks)
# it's possible to callback a different model than self:
if hasattr(self.model, 'callback_model') and self.model.callback_model:
callback_model = self.model.callback_model
else:
callback_model = self.model
callbacks.set_model(callback_model)
is_sequence = isinstance(self.train_generator, Sequence)
if not is_sequence and use_multiprocessing and workers > 1:
warnings.warn(
UserWarning('Using a generator with `use_multiprocessing=True`'
' and multiple workers may duplicate your data.'
' Please consider using the`keras.utils.Sequence'
' class.'))
if is_sequence:
steps_per_epoch = len(self.train_generator)
enqueuer = None
val_enqueuer = None
callbacks.set_params({
'epochs': self.epochs,
'steps': steps_per_epoch,
'verbose': verbose,
'do_validation': do_validation,
'metrics': callback_metrics,
})
callbacks.on_train_begin()
try:
if do_validation and not val_gen:
# Prepare data for validation
if len(validation_data) == 2:
val_x, val_y = validation_data
val_sample_weight = None
elif len(validation_data) == 3:
val_x, val_y, val_sample_weight = validation_data
else:
raise ValueError('`validation_data` should be a tuple '
'`(val_x, val_y, val_sample_weight)` '
'or `(val_x, val_y)`. Found: ' +
str(validation_data))
val_x, val_y, val_sample_weights = self.model._standardize_user_data(
val_x, val_y, val_sample_weight)
val_data = val_x + val_y + val_sample_weights
if self.model.uses_learning_phase and not isinstance(
K.learning_phase(), int):
val_data += [0.]
for cbk in callbacks:
cbk.validation_data = val_data
if is_sequence:
enqueuer = OrderedEnqueuer(
self.train_generator,
use_multiprocessing=use_multiprocessing,
shuffle=shuffle)
else:
enqueuer = GeneratorEnqueuer(
self.train_generator,
use_multiprocessing=use_multiprocessing,
wait_time=wait_time)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
# Train the model
# Construct epoch logs.
epoch_logs = {}
# Epochs
while epoch < self.epochs:
callbacks.on_epoch_begin(epoch)
steps_done = 0
batch_index = 0
# Steps per epoch
while steps_done < steps_per_epoch:
generator_output = next(output_generator)
if len(generator_output) == 2:
x, y = generator_output
sample_weight = None
elif len(generator_output) == 3:
x, y, sample_weight = generator_output
else:
raise ValueError('Output of generator should be '
'a tuple `(x, y, sample_weight)` '
'or `(x, y)`. Found: ' +
str(generator_output))
#==========================
# Mini-batch
#==========================
if (self.print_pseudo_generate):
print ''
print 'Generating pseudo-labels...'
verbose = 1
else:
verbose = 0
if self.no_label_generator.samples > 0:
no_label_output = self.model.predict_generator(
self.no_label_generator,
self.no_label_generator.samples,
verbose=verbose)
# One-hot encoded
self.no_label_generator.classes = np.argmax(
no_label_output, axis=1)
# Concat Pseudo labels with true labels
x_pseudo, y_pseudo = next(self.no_label_generator)
x, y = np.concatenate((x, x_pseudo),
axis=0), np.concatenate(
(y, y_pseudo), axis=0)
# build batch logs
batch_logs = {}
if isinstance(x, list):
batch_size = x[0].shape[0]
elif isinstance(x, dict):
batch_size = list(x.values())[0].shape[0]
else:
batch_size = x.shape[0]
batch_logs['batch'] = batch_index
batch_logs['size'] = batch_size
callbacks.on_batch_begin(batch_index, batch_logs)
# Runs a single gradient update on a single batch of data
scalar_training_loss = self.model.train_on_batch(x=x, y=y)
if not isinstance(scalar_training_loss, list):
scalar_training_loss = [scalar_training_loss]
for l, o in zip(out_labels, scalar_training_loss):
batch_logs[l] = o
callbacks.on_batch_end(batch_index, batch_logs)
#==========================
# end Mini-batch
#==========================
batch_index += 1
steps_done += 1
if steps_done >= steps_per_epoch and do_validation:
if val_gen:
val_outs = self.model.evaluate_generator(
validation_data,
validation_steps,
workers=workers,
use_multiprocessing=use_multiprocessing,
max_queue_size=max_queue_size)
else:
# No need for try/except because
# data has already been validated.
val_outs = self.model.evaluate(
val_x,
val_y,
batch_size=batch_size,
sample_weight=val_sample_weights,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
# Epoch finished.
callbacks.on_epoch_end(epoch, epoch_logs)
epoch += 1
finally:
try:
if enqueuer is not None:
enqueuer.stop()
finally:
if val_enqueuer is not None:
val_enqueuer.stop()
callbacks.on_train_end()
return self.model.history
class DDPGAgent(Agent):
def __init__(self,
cluster,
actor,
critic,
tau=0.001,
gamma=0.99,
mode=2,
batch_size=256,
lr=0.015,
decay=1e-5,
clr=None,
clip_tdq=None,
end_gamma=False,
critic_training_cycles=1,
verbose=False,
nbatches=100,
nfrac=0.03):
super().__init__()
self.verbose = verbose
self.cluster = cluster
self.mode = mode
self.actor = actor
self.tau = tau # *50 # scale tau because we update less frequently than ddpg paper
self.gamma = gamma
self.mode = mode
self.target_actor = create_target(actor)
self.target_critic = create_target(critic)
self.critic = critic
self.batch_size = batch_size
self.lrdecay_interval = 100
clr = clr if clr is not None else lr
self.nbatches = nbatches
self.freeze_critic = False
self.freeze_actor = False
self.clip_tdq = clip_tdq
self.end_gamma = end_gamma
self.critic_training_cycles = critic_training_cycles
self.nfrac = nfrac
self.critic.trainable = True
self.critic.compile(optimizer=Adam(lr=clr, clipnorm=1., decay=decay),
loss='mse',
metrics=['mae', 'acc'])
self.critic._make_train_function()
if self.verbose:
self.critic.summary()
print(
"DDPG mode {} gamma={:.3e} tau={:.3e} lr={:.3e} clr={:.3e} decay={:.3e} cycles={} bsz={}"
.format(mode, gamma, tau, lr, clr, decay,
self.critic_training_cycles, self.batch_size))
if self.mode == 1:
self.actor.compile(optimizer=DDPGof(Adam)(self.critic,
self.actor,
batch_size=batch_size,
lr=lr,
clipnorm=1.,
decay=decay),
loss='mse',
metrics=['mae', 'acc'])
elif self.mode == 2:
self.combined = Model([actor.input],
critic([actor.input, actor.output]))
self.combined.layers[-1].trainable = False
self.combined.compile(optimizer=Adam(lr=lr,
clipnorm=1.,
decay=decay),
loss='mse',
metrics=['mae', 'acc'])
self.combined._make_train_function()
else:
cgrad = K.gradients(critic.outputs,
critic.inputs[1]) # grad of Q wrt actions
self.cgradf = K.function(critic.inputs, cgrad)
actor.compile(optimizer=Adam(lr=lr, clipnorm=1., decay=decay),
loss='mse',
metrics=['mae', 'acc'])
#lr and clr are stored in the models
@property
def lr(self):
if self.mode != 2:
return K.get_value(self.actor.optimizer.lre)
else:
return K.get_value(self.combined.optimizer.lr)
@lr.setter
def lr(self, value):
if self.mode != 2:
K.set_value(self.actor.optimizer.lr, value)
else:
K.set_value(self.combined.optimizer.lr, value)
@property
def clr(self):
return K.get_value(self.critic.optimizer.lr)
@clr.setter
def clr(self, value):
K.set_value(self.critic.optimizer.lr, value)
def _train(self,
memory=None,
epochs=100,
nepisodes=None,
nsteps=10000,
fignum=None,
visualize=False,
minsteps=10000,
updates=False):
if memory is None:
memory = ExperienceMemory(env=self.cluster.env, sz=1000000)
#if nepisodes is None:
# nepisodes = self.cluster.nenv
if self.verbose:
print("Train critic")
self.critic.summary()
if hasattr(self, 'combined'):
print("Train combined")
self.combined.summary()
else:
print("Train ddpg actor")
self.actor.summary()
# Each environment requires an explorer instance
explorers = [
OrnstienUhlenbeckExplorer(self.cluster.env.action_space,
theta=.15,
mu=0.,
nfrac=self.nfrac)
for i in range(self.cluster.nenv)
]
generator = memory.obs1generator(batch_size=self.batch_size,
showdone=True)
#explorers = [None]*self.cluster.nenv
# sample timing test...
# 0.1 ms per next(generator)
# 1.2 ms per tdq
# 4 ms per train critic
# 4ms per train actor
# 3ms per update weights
# 1.2s for plotting callbacks
self.cluster.rollout(policy=self.target_actor,
nsteps=minsteps,
memory=memory,
exploration=explorers,
visualize=visualize)
for i_epoch in range(epochs):
start = time.perf_counter()
for i_batch in range(self.nbatches):
if not self.freeze_critic:
for i_qtrain in range(self.critic_training_cycles):
obs0, a0, r0, obs1, done = next(generator)
tdq = TD_q(self.target_actor,
self.target_critic,
self.gamma,
obs1,
r0,
done,
end_gamma=self.end_gamma)
if self.clip_tdq is not None:
tdq = np.clip(tdq,
self.clip_tdq / (1 - self.gamma), 0)
self.critic.train_on_batch([obs0, a0], tdq)
if not self.freeze_actor:
if self.mode == 1:
self.actor.train_on_batch(obs0, a0)
elif self.mode == 2:
self.combined.train_on_batch(
obs0, np.zeros_like(r0)) # loss = -q
else:
# update the actor : critic.grad()*actor.grad()
actions = self.actor.predict(obs0)
grads = self.cgradf([obs0, actions])[0]
ya = actions + 0.1 * grads # nudge action in direction that improves Q
self.actor.train_on_batch(obs0, ya)
update_target(self.target_actor, self.actor, self.tau)
update_target(self.target_critic, self.critic, self.tau)
self._epoch_end({})
end = time.perf_counter()
# add some data with latest policy into the memory
self.cluster.rollout(policy=self.target_actor,
nepisodes=nepisodes,
nsteps=nsteps,
memory=memory,
exploration=explorers,
visualize=visualize)
# print("RL Train {} {:0.3f} sec, epochs {} of {}".format(self.cluster.envname,
# end - start, self.epoch, epochs))
#if self.verbose:
print("epoch {} tau {} gamma {} lr {} clr {}".format(
i_epoch, self.tau, self.gamma, self.lr, self.clr))
print(memory.summary())
class PseudoLabel:
"""
Pseudo-label Class
"""
def __init__(self,
image_width=256,
image_height=256,
train_data_directory="../data/train",
validation_data_directory="../data/validation",
test_data_directory="../data/test",
no_label_data_directory="../data/no_label",
batch_size=8,
pseudo_label_batch_size=16,
epochs=1,
class_names=[],
architecture="VGG16",
image_channels=3,
learnin_rate=0.001,
save_heights=False,
transfer_learning={
'use_transfer_learning': False,
'fine_tuning': None
},
optimizer='SGD',
metrics_list=['acc'],
loss_function='categorical_crossentropy',
class_labels=None):
"""
Pseudo-label class construtor
"""
# Atributes declarations
self.image_width = image_width
self.image_height = image_height
self.train_data_directory = train_data_directory
self.validation_data_directory = validation_data_directory
self.test_data_directory = test_data_directory
self.no_label_data_directory = no_label_data_directory
self.batch_size = batch_size
self.pseudo_label_batch_size = pseudo_label_batch_size
self.epochs = epochs
self.class_names = class_names
self.architecture = architecture
self.image_channels = image_channels
self.learning_rate = learnin_rate
self.use_transfer_learning = transfer_learning.get(
'use_transfer_learning')
self.fine_tuning_rate = transfer_learning.get('fine_tuning')
self.optimizer = optimizer
self.metrics_list = metrics_list
self.loss_function = loss_function
self.model = None
self.train_generator = None
self.validation_generator = None
self.h5_filename = None
self.class_labels = class_labels
# Make your model and dataset
self.make_data_generators()
self.make_model(architecture=self.architecture,
use_transfer_learning=self.use_transfer_learning,
fine_tuning_rate=self.fine_tuning_rate,
optimizer=self.optimizer,
metrics_list=self.metrics_list,
loss_function=self.loss_function)
self.generate_h5_filename() if save_heights else None
def make_model(self,
architecture=None,
use_transfer_learning=False,
fine_tuning_rate=None,
optimizer='SGD',
metrics_list=['accuracy'],
loss_function='categorical_crossentropy'):
"""
Create your CNN keras model
Arguments:
architecture (str): architecture of model
"""
# Validations
for metric in metrics_list:
if metric not in LIST_OF_ACCEPTABLES_METRICS:
raise ValueError("The specified metric \'" + metric +
"\' is not supported")
if loss_function not in LIST_OF_ACCEPTABLES_LOSS_FUNCTION:
raise ValueError("The specified loss function \'" + loss_function +
"\' is not supported!")
if optimizer not in LIST_OF_ACCEPTABLES_OPTIMIZERS.keys():
raise ValueError("The specified optimizer \'" + optimizer +
"\' is not supported!")
if architecture not in LIST_OF_ACCEPTABLES_ARCHITECTURES.keys():
raise ValueError("The specified architecture \'" + architecture +
"\' is not supported!")
else:
if use_transfer_learning and not 0 <= fine_tuning_rate <= 100:
raise ValueError(
"The fine tuning rate must be beetween 0 and 100!")
if use_transfer_learning and fine_tuning_rate == None:
raise ValueError(
"You need to specify a fine tuning rate if you're using transfer learning!"
)
# With transfer learning
if use_transfer_learning:
self.model = LIST_OF_ACCEPTABLES_ARCHITECTURES.get(architecture)(
weights="imagenet",
include_top=False,
input_shape=(self.image_height, self.image_width,
self.image_channels))
last_layers = len(self.model.layers) - \
int(len(self.model.layers) * (fine_tuning_rate / 100.))
for layer in self.model.layers[:last_layers]:
layer.trainable = False
# Without transfer learning
else:
self.model = LIST_OF_ACCEPTABLES_ARCHITECTURES.get(architecture)(
weights=None,
include_top=False,
input_shape=(self.image_height, self.image_width,
self.image_channels))
for layer in self.model.layers:
layer.trainable = True
# Adding the custom Layers
new_custom_layers = self.model.output
new_custom_layers = Flatten()(new_custom_layers)
new_custom_layers = Dense(1024, activation="relu")(new_custom_layers)
new_custom_layers = Dropout(0.5)(new_custom_layers)
new_custom_layers = Dense(1024, activation="relu")(new_custom_layers)
predictions = Dense(self.train_generator.num_classes,
activation="softmax")(new_custom_layers)
# Create the final model
self.model = Model(inputs=self.model.input, outputs=predictions)
# Compile model
self.model.compile(
loss=loss_function,
optimizer=LIST_OF_ACCEPTABLES_OPTIMIZERS.get(optimizer)(
lr=self.learning_rate),
metrics=metrics_list)
def make_data_generators(self, use_data_augmentation=False):
"""
Function that initiate the train, validation and test generators with data augumentation
"""
train_datagen = ImageDataGenerator(rescale=1. / 255,
horizontal_flip=True,
fill_mode="nearest",
zoom_range=0.3,
width_shift_range=0.3,
height_shift_range=0.3,
rotation_range=30)
self.train_generator = train_datagen.flow_from_directory(
self.train_data_directory,
target_size=(self.image_height, self.image_width),
color_mode='rgb',
classes=self.class_labels,
batch_size=self.batch_size,
shuffle=True,
class_mode="categorical")
test_datagen = ImageDataGenerator(rescale=1. / 255,
horizontal_flip=True,
fill_mode="nearest",
zoom_range=0.3,
width_shift_range=0.3,
height_shift_range=0.3,
rotation_range=30)
self.validation_generator = test_datagen.flow_from_directory(
self.validation_data_directory,
target_size=(self.image_height, self.image_width),
color_mode='rgb',
batch_size=self.batch_size,
shuffle=True,
class_mode="categorical")
no_label_datagen = ImageDataGenerator()
self.no_label_generator = no_label_datagen.flow_from_directory(
self.no_label_data_directory,
target_size=(self.image_height, self.image_width),
color_mode='rgb',
batch_size=self.pseudo_label_batch_size,
shuffle=False,
class_mode="categorical")
self.no_label_generator.num_classes = self.validation_generator.num_classes
def generate_h5_filename(self):
"""
Generate the .h5 filename. The .h5 file is the file that contains your trained model
"""
if self.fine_tuning_rate == 100:
self.h5_filename = self.architecture + \
'_transfer_learning'
elif self.fine_tuning_rate == None:
self.h5_filename = self.architecture + \
'_without_transfer_learning'
else:
self.h5_filename = self.architecture + \
'_fine_tunning_' + str(self.fine_tuning_rate)
################################################################################
# Semi-supervised - Pseudo label approach
################################################################################
def fit_with_pseudo_label(self,
steps_per_epoch,
use_checkpoints=False,
class_labels=None,
verbose=1,
use_multiprocessing=False,
shuffle=False,
workers=1,
max_queue_size=10):
wait_time = 0.01 # in seconds
self.model._make_train_function()
# Create a checkpoint callback
checkpoint = ModelCheckpoint("../models_checkpoints/" +
str(self.h5_filename) + ".h5",
monitor='val_acc',
verbose=1,
save_best_only=True,
save_weights_only=True,
mode='auto',
period=1)
# Generate callbacks
callback_list = []
if use_checkpoints:
callback_list.extend(checkpoint)
# Init train counters
epoch = 0
# Prepare display labels.
out_labels = self.model._get_deduped_metrics_names()
callback_metrics = out_labels + ['val_' + n for n in out_labels]
# Prepare train callbacks
self.model.history = cbks.History()
callbacks = [cbks.BaseLogger()] + (callback_list or []) + \
[self.model.history]
if verbose:
callbacks += [cbks.ProgbarLogger(count_mode='steps')]
callbacks = cbks.CallbackList(callbacks)
# it's possible to callback a different model than self:
if hasattr(self.model, 'callback_model') and self.model.callback_model:
callback_model = self.model.callback_model
else:
callback_model = self.model
callbacks.set_model(callback_model)
is_sequence = isinstance(self.train_generator, Sequence)
if not is_sequence and use_multiprocessing and workers > 1:
warnings.warn(
UserWarning('Using a generator with `use_multiprocessing=True`'
' and multiple workers may duplicate your data.'
' Please consider using the`keras.utils.Sequence'
' class.'))
if is_sequence:
steps_per_epoch = len(self.train_generator)
enqueuer = None
callbacks.set_params({
'epochs': self.epochs,
'steps': steps_per_epoch,
'verbose': verbose,
'do_validation': True,
'metrics': callback_metrics,
})
callbacks.on_train_begin()
try:
if is_sequence:
enqueuer = OrderedEnqueuer(
self.train_generator,
use_multiprocessing=use_multiprocessing,
shuffle=shuffle)
else:
enqueuer = GeneratorEnqueuer(
self.train_generator,
use_multiprocessing=use_multiprocessing,
wait_time=wait_time)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
# Train the model
# Epochs
while epoch < self.epochs:
callbacks.on_epoch_begin(epoch)
steps_done = 0
batch_index = 0
# Steps per epoch
while steps_done < steps_per_epoch:
generator_output = next(output_generator)
if len(generator_output) == 2:
x, y = generator_output
sample_weight = None
elif len(generator_output) == 3:
x, y, sample_weight = generator_output
else:
raise ValueError('Output of generator should be '
'a tuple `(x, y, sample_weight)` '
'or `(x, y)`. Found: ' +
str(generator_output))
#==========================
# Mini-batch
#==========================
print ''
print 'Generating pseudo-labels...'
no_label_output = self.model.predict_generator(
self.no_label_generator,
None, # because the model is instance of sequence
verbose=1)
# One-hot encoded
self.no_label_generator.classes = np.argmax(
no_label_output, axis=1)
# Concat Pseudo labels with true labels
x_pseudo, y_pseudo = next(self.no_label_generator)
x, y = np.concatenate(
(x, x_pseudo), axis=0), np.concatenate((y, y_pseudo),
axis=0)
if len(generator_output) == 2:
x, y = generator_output
sample_weight = None
elif len(generator_output) == 3:
x, y, sample_weight = generator_output
else:
raise ValueError('Output of generator should be '
'a tuple `(x, y, sample_weight)` '
'or `(x, y)`. Found: ' +
str(generator_output))
# build batch logs
batch_logs = {}
if isinstance(x, list):
batch_size = x[0].shape[0]
elif isinstance(x, dict):
batch_size = list(x.values())[0].shape[0]
else:
batch_size = x.shape[0]
batch_logs['batch'] = batch_index
batch_logs['size'] = batch_size
callbacks.on_batch_begin(batch_index, batch_logs)
# Runs a single gradient update on a single batch of data
scalar_training_loss = self.model.train_on_batch(x=x, y=y)
if not isinstance(scalar_training_loss, list):
scalar_training_loss = [scalar_training_loss]
for l, o in zip(out_labels, scalar_training_loss):
batch_logs[l] = o
callbacks.on_batch_end(batch_index, batch_logs)
#==========================
# end Mini-batch
#==========================
batch_index += 1
steps_done += 1
# Epoch finished.
epoch += 1
finally:
if enqueuer is not None:
enqueuer.stop()
callbacks.on_train_end()
return self.model.history
class DragoonNet(UnitNet):
_in_channel = 10
_out_channel = 6
def __init__(self, loading=False):
#global common_graph, common_session
self._in_channel = DragoonNet._in_channel
self._out_channel = DragoonNet._out_channel
#self.session=common_session
#self.graph=common_graph
self.graph = tf.Graph()
self.session = tf.Session(graph=self.graph)
with self.session.as_default():
with self.graph.as_default():
self.inp = Input((WINDOW_SIZE, WINDOW_SIZE, self._in_channel),
dtype='float32')
self.conv1 = Conv2D(32, (1, 1),
activation='linear',
padding='same')(self.inp)
self.pool1 = MaxPooling2D((2, 2))(self.conv1)
self.conv2 = conv_block(self.pool1, 1)
self.pool2 = MaxPooling2D((2, 2))(self.conv2)
self.conv3 = conv_block(self.pool2, 1)
self.pool3 = MaxPooling2D((2, 2))(self.conv3)
self.deconv1 = deconv_block(self.pool3, 2)
self.up1 = UpSampling2D((2, 2))(self.deconv1)
self.deconv2 = deconv_block(
Concatenate(axis=3)([self.up1, self.conv3]), 1)
self.up2 = UpSampling2D((2, 2))(self.deconv2)
self.deconv3 = deconv_block(
Concatenate(axis=3)([self.up2, self.conv2]), 1)
self.up3 = UpSampling2D((2, 2))(self.deconv3)
self.deconv4 = Conv2DTranspose(64, (3, 3),
activation='relu',
padding='same')(self.up3)
self.out = Conv2DTranspose(self._out_channel, (3, 3),
activation='linear',
padding='same')(self.deconv4)
self.model = Model(inputs=self.inp, outputs=self.out)
self.model.compile(optimizer='adam', loss='MSE')
self.model._make_predict_function()
self.model._make_test_function()
self.model._make_train_function()
if (loading and os.path.isfile('DragoonNet.h5')):
self.model = load_model('DragoonNet.h5')
def save(self):
with self.session.as_default():
with self.graph.as_default():
self.model.save('DragoonNet.h5')
@staticmethod
def msg2state(disGame, msg):
x, y = msg.myInfo.coord
X, Y = disGame.regions.shape
ax = max(0, WINDOW_SIZE // 2 - x)
ay = max(0, WINDOW_SIZE // 2 - y)
ans = numpy.zeros([WINDOW_SIZE, WINDOW_SIZE, DragoonNet._in_channel])
ans[ax:min(WINDOW_SIZE, X - x + WINDOW_SIZE // 2),
ay:min(WINDOW_SIZE, Y - y + WINDOW_SIZE // 2),
0] = disGame.regions[
max(0, x - WINDOW_SIZE // 2):min(x + WINDOW_SIZE // 2, X),
max(0, y - WINDOW_SIZE // 2):min(y + WINDOW_SIZE // 2, Y)]
for u in msg.enemies: #enemy
nx = u.coord[0] - x + WINDOW_SIZE // 2
ny = u.coord[1] - y + WINDOW_SIZE // 2
ans[nx, ny, 2] = 1
ans[nx, ny, 5] = u.HP + u.shield
ans[nx, ny, 6] = u.damageTo
ans[nx, ny, 7] = u.damagaFrom
for u in msg.allies: #ally
nx = u.coord[0] - x + WINDOW_SIZE // 2
ny = u.coord[1] - y + WINDOW_SIZE // 2
ans[nx, ny, 1] = 1
ans[nx, ny, 4] = u.HP + u.shield
ans[:, :, 3] = msg.myInfo.HP + msg.myInfo.shield
for i in range(WINDOW_SIZE * 32 // X):
for j in range(WINDOW_SIZE * 32 // X):
#print(ans[i::WINDOW_SIZE*32//X,j::WINDOW_SIZE*32//X,8].shape,msg[6].shape)
ans[i::WINDOW_SIZE * 32 // X, j::WINDOW_SIZE * 32 // X,
8] = msg.explored
ans[x * WINDOW_SIZE // X, y * WINDOW_SIZE // Y, 9] = 1
return ans
@staticmethod
def msg2mask(disGame, msg):
ans = numpy.zeros([WINDOW_SIZE, WINDOW_SIZE, DragoonNet._out_channel])
x, y = msg.myInfo.coord
X, Y = disGame.regions.shape
ax = max(0, WINDOW_SIZE // 2 - x)
ay = max(0, WINDOW_SIZE // 2 - y)
ans[WINDOW_SIZE // 2, WINDOW_SIZE // 2, 0] = 1
ans[ax:min(WINDOW_SIZE, X - x + WINDOW_SIZE // 2),
ay:min(WINDOW_SIZE, Y - y + WINDOW_SIZE // 2),
1] = disGame.regions[
max(0, x - WINDOW_SIZE // 2):min(x + WINDOW_SIZE // 2, X),
max(0, y - WINDOW_SIZE // 2):min(y + WINDOW_SIZE // 2, Y)]
for u in msg.enemies:
nx = u.coord[0] - x + WINDOW_SIZE // 2
ny = u.coord[1] - y + WINDOW_SIZE // 2
ans[nx, ny, 2] = 1
top, bot, left, right = u.bounds
#ans[shrinkScr(top - x + WINDOW_SIZE // 2):shrinkScr(bot - x + WINDOW_SIZE // 2),
# shrinkScr(left - y + WINDOW_SIZE // 2):shrinkScr(right - x + WINDOW_SIZE // 2),1] = 0
for u in msg.allies:
top, bot, left, right = u.bounds
#ans[shrinkScr(top - x + WINDOW_SIZE // 2):shrinkScr(bot - x + WINDOW_SIZE // 2),
# shrinkScr(left - y + WINDOW_SIZE // 2):shrinkScr(right - x + WINDOW_SIZE // 2),1] = 0
return ans
class VultureNet(UnitNet):
_in_channel = 8
_out_channel = 3
def __init__(self, loading=False, output_type='linear'):
self._in_channel = VultureNet._in_channel
self._out_channel = VultureNet._out_channel
self.session = KTF.get_session()
self.graph = tf.get_default_graph()
with self.session.as_default():
with self.graph.as_default():
self.inp = Input((WINDOW_SIZE, WINDOW_SIZE, self._in_channel),
dtype='float32')
self.conv1 = LeakyReLU()(Conv2D(128, (3, 3),
padding='same')(self.inp))
self.conv1 = conv_block(self.conv1, 2)
self.pool1 = MaxPooling2D((2, 2))(self.conv1)
self.conv2 = conv_block(self.pool1, 2)
self.pool2 = MaxPooling2D((2, 2))(self.conv2)
self.conv3 = conv_block(self.pool2, 2)
self.pool3 = MaxPooling2D((2, 2))(self.conv3)
self.deconv1 = deconv_block(self.pool3, 1)
self.up1 = UpSampling2D((2, 2))(self.deconv1)
self.deconv2 = deconv_block(
Concatenate(axis=3)([self.up1, self.conv3]), 1)
self.up2 = UpSampling2D((2, 2))(self.deconv2)
self.deconv3 = deconv_block(
Concatenate(axis=3)([self.up2, self.conv2]), 1)
self.up3 = UpSampling2D((2, 2))(self.deconv3)
self.deconv4 = LeakyReLU()(Conv2DTranspose(
128, (3, 3),
padding='same')(Concatenate(axis=3)([self.up3,
self.conv1])))
if (output_type == 'softmax'):
self.out = Conv2DTranspose(self._out_channel, (3, 3),
padding='same')(self.deconv4)
self.out = Flatten()(self.out)
self.out = Activation('softmax')(self.out)
self.out = Reshape(
[-1, WINDOW_SIZE, WINDOW_SIZE,
self._out_channel])(self.out)
else:
self.out = Conv2DTranspose(self._out_channel, (3, 3),
padding='same')(self.deconv4)
#optz=SGD(lr=0.001,momentum=0.9)
self.model = Model(inputs=self.inp, outputs=self.out)
self.optz = Adam()
self.model.compile(optimizer=self.optz, loss='MSE')
self.model._make_predict_function()
self.model._make_test_function()
self.model._make_train_function()
if (os.path.isfile('VultureNet.h5') and loading):
self.model = load_model("VultureNet.h5")
def save(self):
with self.session.as_default():
with self.graph.as_default():
self.model.save('VultureNet.h5')
@staticmethod
def msg2state(disGame, msg):
#[enemyBound, myBound, region, myCooldown, myRange, enemyCooldown, myHp, enemyCenter]
x, y = msg.myInfo.coord
X, Y = disGame.regions.shape
ans = numpy.zeros([WINDOW_SIZE, WINDOW_SIZE, VultureNet._in_channel])
for u in msg.enemies:
if (abs(u.coord[0] - x) < WINDOW_SIZE // 2
and abs(u.coord[1] - y) < WINDOW_SIZE // 2):
ans[shrinkScr(u.bounds[0] - x +
WINDOW_SIZE // 2):shrinkScr(u.bounds[1] - x +
WINDOW_SIZE // 2),
shrinkScr(u.bounds[2] - y +
WINDOW_SIZE // 2):shrinkScr(u.bounds[3] - y +
WINDOW_SIZE // 2),
1] = 1
ans[u.coord[0] - x + WINDOW_SIZE // 2,
u.coord[1] - y + WINDOW_SIZE // 2, 7] = 1
ans[u.coord[0] - x + WINDOW_SIZE // 2,
u.coord[1] - y + WINDOW_SIZE // 2, 5] = u.canFireGround
ax = max(0, WINDOW_SIZE // 2 - x)
ay = max(0, WINDOW_SIZE // 2 - y)
ans[(msg.myInfo.bounds[0] - x) +
WINDOW_SIZE // 2:msg.myInfo.bounds[1] - x + WINDOW_SIZE // 2,
(msg.myInfo.bounds[2] - y) +
WINDOW_SIZE // 2:msg.myInfo.bounds[3] - y + WINDOW_SIZE // 2,
2] = 1
ans[ax:min(WINDOW_SIZE, X - x + WINDOW_SIZE // 2),
ay:min(WINDOW_SIZE, Y - y + WINDOW_SIZE // 2), 0] = 1
ans[:, :, 3] = msg.myInfo.canFireGround
rng = msg.myInfo.rangeGround[1]
for i in range(-rng, rng + 1):
for j in range(-int(numpy.sqrt(rng * rng - i * i)),
1 + int(numpy.sqrt(rng * rng - i * i))):
ans[WINDOW_SIZE // 2 + i, WINDOW_SIZE // 2 + j, 4] = 1
ans[:, :, 6] = msg.myInfo.HP / 16.0
fstate = open('state.txt', 'wb')
pickle.dump(ans, fstate)
fstate.close()
return ans
@staticmethod
def msg2mask(disGame, msg):
x, y = msg.myInfo.coord
X, Y = disGame.regions.shape
ans = numpy.zeros([WINDOW_SIZE, WINDOW_SIZE, VultureNet._out_channel])
ax = max(0, WINDOW_SIZE // 2 - x)
ay = max(0, WINDOW_SIZE // 2 - y)
#ans[WINDOW_SIZE//2,WINDOW_SIZE//2,0]=1
ans[ax:min(WINDOW_SIZE, X - x + WINDOW_SIZE // 2),
ay:min(WINDOW_SIZE, Y - y + WINDOW_SIZE // 2), 1] = 1
myPos = msg.myInfo.coord
if (msg.myInfo.canFireGround == 0):
for u in msg.enemies:
if (abs(u.coord[0] - myPos[0]) < WINDOW_SIZE // 2
and abs(u.coord[1] - myPos[1]) < WINDOW_SIZE // 2):
ans[u.coord[0] - myPos[0] + WINDOW_SIZE // 2,
u.coord[1] - myPos[1] + WINDOW_SIZE // 2, 2] = 1
return ans
## OPTIMIZER
dis_op = Adam(lr=0.0002, beta_1=0.5, beta_2=0.999, epsilon=1e-08)
cls_op = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
gen_op = Adam(lr=0.0002, beta_1=0.5, beta_2=0.999, epsilon=1e-08)
#gen_op = RMSprop(lr=0.001, rho=0.9, epsilon=1e-06)
## MODEL
(gen, (dis, cls)) = get_models(dataset,smallmodel)
dis.trainable = False
x = Input(shape=(gen_dim,))
h = gen(x)
y = dis(h)
disgen = Model(input=x, output=y)
disgen.compile(loss='binary_crossentropy', optimizer=gen_op, metrics=['accuracy'])
disgen._make_train_function()
disgen._make_test_function()
disgen._make_predict_function()
#noise_batch = np.random.uniform(-1, 1, (batch_size, gen_dim)).astype('float32')
#labels2 = np.ones((batch_size, 1)).astype('float32')
#g_loss = disgen.train_on_batch(noise_batch, labels2)
dis.trainable = True
dis.compile(loss='binary_crossentropy', optimizer=dis_op, metrics=['accuracy'])
if classify:
cls.trainable = True
cls.compile(loss='categorical_crossentropy', optimizer=cls_op, metrics=['accuracy'])
gen.compile(loss='mean_squared_error', optimizer=gen_op)
class myHandler(BaseHTTPRequestHandler):
"""
Custom functions to process incoming and outgoing signals
"""
def build_model(self, X_train, y_train):
inputs = Input(shape=(128, ))
inner = Dense(50, activation='tanh')(inputs)
inner = Dropout(0.7)(inner)
pred = Dense(1, activation='sigmoid')(inner)
self.model = Model(inputs=inputs, outputs=pred)
self.model.compile(optimizer='adam',
loss='mean_squared_error',
metrics=['accuracy'])
self.model._make_predict_function()
self.model._make_test_function()
self.model._make_train_function()
self.model.fit(X_train, y_train, epochs=100)
def train_and_test(self, answers):
global encode_arr
global encode_arr_test
answers = answers.split(',')
answers = np.array(answers)
X = encode_arr
y = answers
X_train = X[:90, :]
y_train = y[:90, ].reshape((90, 1))
X_test = X[90:, :]
y_test = y[90:, ].reshape((10, 1))
with tf.Session(graph=tf.Graph()) as sess:
K.set_session(sess)
self.build_model(X_train, y_train)
preds1 = self.model.evaluate(X_test, y_test)
print("Loss = " + str(preds1[0]))
print("Test Accuracy = " + str(preds1[1]))
#predict test data
preds_test1 = self.model.predict(encode_arr_test)
# divide them to positive results and negative results
results_pos = {}
results_neg = {}
ccnter = 0
for i in preds_test1:
if i[0] < 0.35:
results_neg[ccnter] = str((1 - i[0]) * 100) + '%'
else:
results_pos[ccnter] = str(i[0] * 100) + '%'
ccnter += 1
# sorting the pos and neg results in descending order
oo_pos = OrderedDict(
sorted(results_pos.items(), key=lambda x: x[1], reverse=True))
oo_neg = OrderedDict(
sorted(results_neg.items(), key=lambda x: x[1], reverse=True))
oo_total_result = {'positive': oo_pos, 'negative': oo_neg}
return json.dumps(oo_total_result)
"""
End of Custom
"""
#Handler for the GET requests
def do_GET(self):
print('do GET')
#pprint (vars(self))
self.send_response(200)
print(self.path)
if self.path == '/favicon.ico':
return
# Send the html message
if not os.path.isdir(self.path[1:]):
with open(self.path[1:], 'rb') as reader:
content = reader.read()
else:
con = os.listdir(self.path[1:])
len_con = len(con)
if con[0] == '.DS_Store':
len_con -= 1
content = str(len_con).encode()
self.send_header('Access-Control-Allow-Origin', '*')
self.end_headers()
self.wfile.write(content)
return
#Handler for the POST requests
def do_POST(self):
pprint(vars(self))
length = int(self.headers['Content-Length'])
form = cgi.FieldStorage(fp=self.rfile,
headers=self.headers,
environ={'REQUEST_METHOD': 'POST'})
answers = form['answers'].value
print(answers)
output = self.train_and_test(answers)
# Send back response after the data is processed
self.send_response(200)
self.send_header('Access-Control-Allow-Origin', '*')
self.send_header('Content-type', 'text/html')
self.end_headers()
# Send the html message
self.wfile.write(output.encode())
return
def do_OPTIONS(self):
self.send_response(200, "ok")
self.send_header('Access-Control-Allow-Origin', '*')
self.send_header('Access-Control-Allow-Methods', 'GET, OPTIONS')
self.send_header("Access-Control-Allow-Headers", "X-Requested-With")
self.send_header("Access-Control-Allow-Headers", "Content-Type")
self.end_headers()
return
class DroneNet(UnitNet):
_in_channel=5
_out_channel=2
def __init__(self,loading=False, output_type='linear'):
self._in_channel=DroneNet._in_channel
self._out_channel=DroneNet._out_channel
self.session = KTF.get_session()
self.graph = tf.get_default_graph()
with self.session.as_default():
with self.graph.as_default():
self.inp=Input((WINDOW_SIZE,WINDOW_SIZE,self._in_channel),dtype='float32')
self.conv1 = LeakyReLU()(Conv2D(128, (3, 3), padding='same')(self.inp))
self.conv1= conv_block(self.conv1,2)
self.pool1 = MaxPooling2D((2, 2))(self.conv1)
self.conv2 = conv_block(self.pool1, 2)
self.pool2 = MaxPooling2D((2, 2))(self.conv2)
self.conv3=conv_block(self.pool2, 2)
self.pool3=MaxPooling2D((2,2))(self.conv3)
self.deconv1 = deconv_block(self.pool3, 1)
self.up1 = UpSampling2D((2, 2))(self.deconv1)
self.deconv2 = deconv_block(Concatenate(axis=3)([self.up1, self.conv3]), 1)
self.up2=UpSampling2D((2,2))(self.deconv2)
self.deconv3 = deconv_block(Concatenate(axis=3)([self.up2, self.conv2]), 1)
self.up3 = UpSampling2D((2, 2))(self.deconv3)
self.deconv4 = LeakyReLU()(Conv2DTranspose(128, (3, 3), padding='same')(Concatenate(axis=3)([self.up3, self.conv1])))
if(output_type=='softmax'):
self.out=Conv2DTranspose(DroneNet._out_channel, (3, 3), padding='same')(
self.deconv4)
self.out=Flatten()(self.out)
self.out=Activation('softmax')(self.out)
self.out=Reshape([-1,WINDOW_SIZE,WINDOW_SIZE,self._out_channel])(self.out)
else:
self.out = Conv2DTranspose(DroneNet._out_channel, (3, 3), padding='same')(
self.deconv4)
#optz=SGD(lr=0.001,momentum=0.9)
self.model = Model(inputs=self.inp, outputs=self.out)
self.optz=Adam()
self.model.compile(optimizer=self.optz, loss='MSE')
self.model._make_predict_function()
self.model._make_test_function()
self.model._make_train_function()
if (os.path.isfile('DroneNet.h5') and loading):
self.model = load_model("DroneNet.h5")
self.mse=keras.losses.mean_squared_error
def save(self):
with self.session.as_default():
with self.graph.as_default():
self.model.save('DroneNet.h5')
def gradient(self, target, X):
with self.session.as_default():
with self.graph.as_default():
return tf.gradients(target-self.out, self.model.trainable_weights)
#return KTF.gradients(self.mse(target, self.model.predict(X.reshape([-1, WINDOW_SIZE,WINDOW_SIZE,self._in_channel]))), self.model.trainable_weights)
@staticmethod
def msg2state(disGame, msg):
ans=numpy.zeros([WINDOW_SIZE,WINDOW_SIZE,DroneNet._in_channel])
x, y = msg.myInfo.coord
X, Y = disGame.regions.shape
#print(disGame.name, disGame.regions.shape,x,y)
ax=max(0,WINDOW_SIZE//2-x)
ay=max(0,WINDOW_SIZE//2-y)
ans[ax:min(WINDOW_SIZE,X-x+WINDOW_SIZE//2),
ay:min(WINDOW_SIZE,Y-y+WINDOW_SIZE//2),0]=disGame.regions[max(0,x-WINDOW_SIZE//2):min(x+WINDOW_SIZE//2,X),
max(0,y-WINDOW_SIZE//2):min(y+WINDOW_SIZE//2,Y)]
ans[WINDOW_SIZE//2,WINDOW_SIZE//2,1]=1
for u in msg.allies:
if(u.coord[0]!=x or u.coord[1]!=y):
if(u.type!='Zerg_Overlord'):
if(u.type=='Zerg_Drone'):
ans[shrinkScr(u.bounds[0] - x + WINDOW_SIZE // 2):shrinkScr(u.bounds[1] - x + WINDOW_SIZE // 2),
shrinkScr(u.bounds[2] - y + WINDOW_SIZE // 2): shrinkScr(u.bounds[3] - y + WINDOW_SIZE // 2), 2]=1
else:
ans[shrinkScr(u.bounds[0] - x + WINDOW_SIZE // 2):shrinkScr(u.bounds[1] - x + WINDOW_SIZE // 2),
shrinkScr(u.bounds[2] - y + WINDOW_SIZE // 2): shrinkScr(u.bounds[3] - y + WINDOW_SIZE // 2), 3]=1
for u in msg.resources:
#print(u.type)
if(u.type=='Resource_Vespene_Geyser'):
#ans[shrinkScr(u.coord[0]-x+WINDOW_SIZE//2),shrinkScr(u.coord[1]-y+WINDOW_SIZE//2),4]=1
ans[shrinkScr(u.bounds[0] - x + WINDOW_SIZE // 2):shrinkScr(u.bounds[1] - x + WINDOW_SIZE // 2),
shrinkScr(u.bounds[2] - y + WINDOW_SIZE // 2) : shrinkScr(u.bounds[3] - y + WINDOW_SIZE // 2), 4] = 1
else:
ans[shrinkScr(u.bounds[0] - x + WINDOW_SIZE // 2):shrinkScr(u.bounds[1] - x + WINDOW_SIZE // 2),
shrinkScr(u.bounds[2] - y + WINDOW_SIZE // 2): shrinkScr(u.bounds[3] - y + WINDOW_SIZE // 2), 3]=1
fstate=open('state.txt','wb')
pickle.dump(ans, fstate)
fstate.close()
return ans
@staticmethod
def msg2mask(disGame, msg):
x,y=msg.myInfo.coord
X, Y = disGame.regions.shape
ax=max(0,WINDOW_SIZE//2-x)
ay=max(0,WINDOW_SIZE//2-y)
ans=numpy.zeros([WINDOW_SIZE,WINDOW_SIZE,DroneNet._out_channel])
ans[WINDOW_SIZE//2,WINDOW_SIZE//2,0]=1
ans[ax:min(WINDOW_SIZE,X-x+WINDOW_SIZE//2),
ay:min(WINDOW_SIZE,Y-y+WINDOW_SIZE//2),1]=disGame.regions[max(0,x-WINDOW_SIZE//2):min(x+WINDOW_SIZE//2,X),
max(0,y-WINDOW_SIZE//2):min(y+WINDOW_SIZE//2,Y)]
for u in msg.allies:
if(u.type!='Zerg_Overlord'):
top,bot,left,right=u.bounds
ans[shrinkScr(top - x + WINDOW_SIZE // 2):shrinkScr(bot - x + WINDOW_SIZE // 2),
shrinkScr(left - y + WINDOW_SIZE // 2):shrinkScr(right - x + WINDOW_SIZE // 2),1] = 0
for u in msg.resources:
if(abs(u.coord[0] - x) < WINDOW_SIZE // 2 and abs(u.coord[1] - y) < WINDOW_SIZE // 2):
top,bot,left,right=u.bounds
ans[shrinkScr(top - x + WINDOW_SIZE // 2):shrinkScr(bot - x + WINDOW_SIZE // 2),
shrinkScr(left - y + WINDOW_SIZE // 2):shrinkScr(right - x + WINDOW_SIZE // 2),1] = 0
fstate=open('mask.txt','wb')
pickle.dump(ans, fstate)
fstate.close()
return ans
class Wide_ResNet(object):
'''
Modified from:
https://gist.github.com/kashif/0ba0270279a0f38280423754cea2ee1e
'''
def __init__(self, config):
self.verbose = config['verbose']
self.size = config['size']
self.rank = config['rank']
self.name = 'Wide_ResNet'
# data
from data.cifar10 import Cifar10_data
self.data = Cifar10_data(verbose=False)
self.build_model()
# iter related
self.current_t = 0
self.last_one_t=False
self.current_v = 0
self.last_one_v=False
self.n_subb = 1
self.n_epochs = nb_epoch
self.epoch=0
def build_model(self):
img_input = Input(shape=(img_channels, img_rows, img_cols))
# one conv at the beginning (spatial size: 32x32)
x = ZeroPadding2D((1, 1))(img_input)
x = Convolution2D(16, nb_row=3, nb_col=3)(x)
# Stage 1 (spatial size: 32x32)
x = bottleneck(x, n, 16, 16 * k, dropout=0.3, subsample=(1, 1))
# Stage 2 (spatial size: 16x16)
x = bottleneck(x, n, 16 * k, 32 * k, dropout=0.3, subsample=(2, 2))
# Stage 3 (spatial size: 8x8)
x = bottleneck(x, n, 32 * k, 64 * k, dropout=0.3, subsample=(2, 2))
x = BatchNormalization(mode=0, axis=1)(x)
x = Activation('relu')(x)
x = AveragePooling2D((8, 8), strides=(1, 1))(x)
x = Flatten()(x)
preds = Dense(nb_classes, activation='softmax')(x)
self.model = Model(input=img_input, output=preds)
self.keras_get_params()
def compile_iter_fns(self, *args, **kwargs):
try:
sync_type = kwargs['sync_type']
except:
raise RuntimeError('Keras wresnet needs sync_type keyword argument')
if sync_type != 'avg':
raise RuntimeError('currently wresnet only support compiling with sync_type avg, add BSP_SYNC_TYPE=avg in your session cfg')
import time
start = time.time()
self.model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
self.shared_lr = self.model.optimizer.lr
self.base_lr = self.shared_lr.get_value()
if self.verbose: print('Compiling......')
self.model._make_train_function()
self.model._make_test_function()
if self.verbose: print('Compile time: %.3f s' % (time.time()-start))
self.data.batch_data(self.model, batch_size)
self.data.extend_data(rank=self.rank, size=self.size)
self.data.shuffle_data(mode='train', common_seed=1234)
self.data.shuffle_data(mode='val')
self.data.shard_data(mode='train', rank=self.rank, size=self.size) # to update data.n_batch_train
self.data.shard_data(mode='val', rank=self.rank, size=self.size) # to update data.n_batch_val
def train_iter(self, count, recorder):
if self.current_t ==0:
self.data.shuffle_data(mode='train',common_seed=self.epoch)
self.data.shard_data(mode='train',rank=self.rank, size=self.size)
recorder.start()
cost, acc = self.model.train_function(self.data.train_batches_shard[self.current_t])
recorder.train_error(count, cost, 1.0-acc)
recorder.end('calc')
if self.current_t == self.data.n_batch_train - 1:
self.last_one_t = True
else:
self.last_one_t = False
if self.last_one_t == False:
self.current_t+=1
else:
self.current_t=0
def val_iter(self, count, recorder):
if self.current_v==0:
self.data.shuffle_data(mode='val')
self.data.shard_data(mode='val',rank=self.rank, size=self.size)
cost, acc = self.model.test_function(self.data.val_batches_shard[self.current_v])
recorder.val_error(count, cost, 1.0-acc, 0)
if self.current_v == self.data.n_batch_val - 1:
self.last_one_v = True
else:
self.last_one_v = False
if self.last_one_v == False:
self.current_v+=1
else:
self.current_v=0
def reset_iter(self, mode):
'''used at the begininig of another mode'''
if mode=='train':
self.current_t = 0
self.subb_t=0
self.last_one_t = False
else:
self.current_v = 0
self.subb_v=0
self.last_one_v = False
def adjust_hyperp(self, epoch):
'to be called once per epoch'
if lr_policy == 'step':
if epoch in lr_step:
tuned_base_lr = self.shared_lr.get_value() /10.
self.shared_lr.set_value(np.float32(tuned_base_lr))
def scale_lr(self, size):
self.shared_lr.set_value(np.array(self.base_lr*size, dtype='float32'))
def cleanup(self):
pass
def keras_get_params(self):
self.params=[]
for l in self.model.layers:
self.params.extend(l.trainable_weights)
P = Lambda(lambda x: K.square(K.exp(x)),
name='P')(Dense(1, activation='linear')(h2))
mu = Lambda(lambda x: 3 * x, name='mu')(Dense(1, activation='tanh')(h2))
mu_neg = Lambda(lambda x: -x)(mu)
mu_rel = Add()([a, mu_neg])
mu_rel_sq = Lambda(lambda x: -0.5 * K.square(x))(mu_rel)
A = Multiply(name='A')([P, mu_rel_sq])
Q = Add(name='Q')([V, A])
model = Model(input=inp, output=Q)
model.summary()
model.compile(loss='mse', optimizer=Adam(lr=0.001))
# Must be in this order to enable automatic discovery
model._make_train_function()
model._make_predict_function()
# Make sure weight assign placeholders are created
weights = model.get_weights()
model.set_weights(weights)
tf.train.write_graph(get_session().graph.as_graph_def(),
'./',
'pendulum_naf.pb',
as_text=False)
#writer = tf.summary.FileWriter("/tmp/tensorflow", get_session().graph)
#
#data = np.genfromtxt('transitions.csv', delimiter=',')
#x = data[:, 4:7]