def build_model(state_size, action_size):
# seed_nb = 14
# np.random.seed(seed_nb)
# tf.random.set_seed(seed_nb)
learning_rate = 0.01
# model = Sequential()
# model.add(Dense(64, input_dim = state_size, activation = 'relu', kernel_initializer = initializers.glorot_uniform(seed = seed_nb)))
# model.add(Dense(64, activation = 'relu', kernel_initializer = initializers.glorot_uniform(seed = seed_nb)))
# model.add(Dense(action_size, activation = 'linear', kernel_initializer = initializers.glorot_uniform(seed = seed_nb)))
# model.compile(loss = 'mse', optimizer = Adam(lr = learning_rate))
model = Sequential()
model.add(
Dense(64,
input_dim=state_size,
activation='sigmoid',
kernel_initializer=initializers.Ones()))
# model.add(Dropout(0.3))
model.add(
Dense(64, activation='sigmoid',
kernel_initializer=initializers.Ones()))
# model.add(Dropout(0.3))
model.add(
Dense(action_size,
activation='linear',
kernel_initializer=initializers.Ones()))
model.compile(loss='mse', optimizer=Adam(lr=learning_rate))
# model = Sequential()
# model.add(Dense(64, input_dim = state_size, activation = 'relu', kernel_initializer = initializers.Ones()))
# model.add(Dense(64, activation = 'relu', kernel_initializer = initializers.Ones()))
# model.add(Dense(action_size, activation = 'linear', kernel_initializer = initializers.Ones()))
# model.compile(loss = 'mse', optimizer = Adam(lr = learning_rate))
return model
def ResidualBlockGenerator(x, channels_in, channels_out, stepChanger=False):
#stepChanger is for reducing size of the feature map like 56 x 56 to 28 x 28
if stepChanger:
shortcut = x
group_size = (channels_in) // (cardinality)
groups = []
for i in range(cardinality):
groupsElements = layers.Conv2D(group_size,
kernel_size=(1, 1),
strides=(2, 2),
padding='same')(x)
groupsElements = AddCommonLayers(groupsElements)
groupsElements = layers.Conv2D(group_size,
kernel_size=(3, 3),
padding='same')(groupsElements)
groupsElements = AddCommonLayers(groupsElements)
groups.append(groupsElements)
x = layers.concatenate(groups)
x = layers.Conv2D(channels_out, kernel_size=(1, 1))(x)
x = layers.BatchNormalization()(x)
layer = layers.Conv2D(channels_out,
kernel_size=(2, 2),
strides=(2, 2),
use_bias=False,
kernel_initializer=initializers.Ones())
layer.trainable = False
shortcut = layer(shortcut)
shortcut = layers.BatchNormalization()(shortcut)
x = layers.add([shortcut, x])
x = layers.LeakyReLU(alpha=0.)(x)
else:
shortcut = x
group_size = (channels_in) // (cardinality)
groups = []
for i in range(cardinality):
groupsElements = layers.Conv2D(group_size, kernel_size=(1, 1))(x)
groupsElements = AddCommonLayers(groupsElements)
groupsElements = layers.Conv2D(group_size,
kernel_size=(3, 3),
padding='same')(groupsElements)
groupsElements = AddCommonLayers(groupsElements)
groups.append(groupsElements)
x = layers.concatenate(groups)
x = layers.Conv2D(channels_out, kernel_size=(1, 1))(x)
x = layers.BatchNormalization()(x)
if shortcut.shape[3] != x.shape[3]:
layer = layers.Conv2D(channels_out,
kernel_size=(1, 1),
use_bias=False,
kernel_initializer=initializers.Ones())
layer.trainable = False
shortcut = layer(shortcut)
shortcut = layers.BatchNormalization()(shortcut)
x = layers.add([shortcut, x])
x = layers.LeakyReLU(alpha=0.)(x)
return x
def semiconv_model(function=None):
inputs = Input(shape=(256,256,3))
x = SemiConv2D(3, 3, padding='same', kernel_initializer=initializers.Ones(),
function=function, normalized_position=False)(inputs)
return Model(inputs, x)
def build(self, inputs_shape):
'''
self.w_linear = self.add_weight(name='s2s_w_linear',
shape=(inputs_shape[-1], self.output_dim),
initializer=self.kernel_initializer)
self.b_linear = self.add_weight(name='s2s_b_linear',
shape=(self.output_dim,),
initializer=initializers.Zeros())
'''
self.w_recurrent = self.add_weight(
name='s2s_w_recurrent',
shape=(self.output_dim * 2, self.output_dim * 4),
initializer=self.recurrent_initializer)
self.b_recurrent_a = self.add_weight(name='s2s_b_recurrent_a',
shape=(self.output_dim * 1, ),
initializer=initializers.Zeros())
self.b_recurrent_b = self.add_weight(name='s2s_b_recurrent_b',
shape=(self.output_dim * 1, ),
initializer=initializers.Ones())
self.b_recurrent_c = self.add_weight(name='s2s_b_recurrent_c',
shape=(self.output_dim * 2, ),
initializer=initializers.Zeros())
super(Set2SetS, self).build(inputs_shape)
def bias_initializer(shape, *args, **kwargs):
return K.concatenate([
self.bias_initializer((self.units, ), *args, **kwargs),
initializers.Ones()((self.units, ), *args, **kwargs),
self.bias_initializer((self.units * 3, ), *args,
**kwargs),
])
def regressor_tunning(kernel_initializer='he_uniform',
bias_initializer=initializers.Ones()):
model = Sequential()
if n_hidden == 0:
model.add(
LSTM(units=units,
input_shape=(steps, features_num),
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.2))
else:
model.add(
LSTM(units=units,
input_shape=(steps, features_num),
return_sequences=True,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.2))
model.add(
LSTM(units=units,
input_shape=(steps, features_num),
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.2))
model.add(Dense(1, activation='linear'))
optimizer = optimizers.RMSprop()
model.compile(loss='mse', metrics=['mse', 'mae'], optimizer=optimizer)
return model
def build(self, input_shape):
self.kernel = self.add_weight(name='kernel',
shape=(input_shape, ),
initializer=initializers.Ones(),
trainable=True)
self.output_dim = input_shape
super(balanceGrad, self).build(input_shape)
def bias_initializer(_, *args, **kwargs):
return kb.concatenate([self.bias_initializer(
(self.n_hidden,), *args, **kwargs),
initializers.Ones()((self.n_hidden,),
*args, **kwargs),
self.bias_initializer(
(self.n_hidden * 2,), *args,
**kwargs)])
def bias_initializer(_, *args, **kwargs):
return K.concatenate([
self.bias_initializer((self.filters, ), *args,
**kwargs),
initializers.Ones()((self.filters, ), *args, **kwargs),
self.bias_initializer((self.filters * 2, ), *args,
**kwargs),
])
def build(self, input_shape):
shape = input_shape[2:]
# accumulators
self._mean = self.add_weight('mean', shape, initializer=initializers.Zeros(), trainable=False)
self._var = self.add_weight('var', shape, initializer=initializers.Ones(), trainable=False)
self._count = self.add_weight('count', (1,), initializer=initializers.Zeros(), trainable=False)
self._std = K.sqrt(self._var)
super(RunningMeanStd, self).build(input_shape)
def build(self, input_shape):
self.input_spec = [InputSpec(shape=input_shape)]
output_shape = (int(input_shape[self.axis]), )
gamma_initializer = initializers.Ones()
beta_initializer = initializers.Zeros()
self.gamma = K.variable(gamma_initializer(output_shape))
self.beta = K.variable(beta_initializer(output_shape))
self.trainable_weights = [self.gamma, self.beta]
def build(self, input_shape):
self.gamma = self.add_weight(name='gamma',
shape=input_shape[-1:],
initializer=initializers.Ones(),
trainable=True)
self.beta = self.add_weight(name='beta',
shape=input_shape[-1:],
initializer=initializers.Zeros(),
trainable=True)
super(LayerNormalization, self).build(input_shape)
def controller_initializer(shape, *args, **kwargs):
return K.concatenate([
initializers.Zeros()((self.batch_size, self.memory.shape[0]),
*args, **kwargs),
initializers.Ones()((self.batch_size, self.memory.shape[0]),
*args, **kwargs),
initializers.Zeros()((self.batch_size, self.memory.shape[0]),
*args, **kwargs),
initializers.Zeros()((self.batch_size, self.memory.shape[0]),
*args, **kwargs),
])
def tp1_node_update(graph_node_embs, node_rel, node_rel_weight, max_nodes, max_bi_relations, embed_dim, label):
"""
graph_node_embs has shape (batch_size, max_nodes per graph, embed_dim feats).
"""
dense_dim = embed_dim
x = gather_layer([graph_node_embs, node_rel])
logging.debug('After gather3 shape: {0}'.format(x.shape))
x = Reshape((max_nodes * max_bi_relations, 2 * embed_dim))(x)
x = TimeDistributed(
Dense(
dense_dim,
kernel_initializer=initializers.Ones(),
bias_initializer=initializers.Zeros(),
name=label + '_dense1'))(x)
# TODO: re-enable the batch normalization.
# x = BatchNormalization(axis=2, name=label + '_bn1')(x)
x = Activation('relu')(x)
x = TimeDistributed(
Dense(
dense_dim,
kernel_initializer=initializers.Ones(),
bias_initializer=initializers.Zeros(),
name=label + '_dense2'))(x)
# x = BatchNormalization(axis=2, name=label + '_bn2')(x)
x = Activation('relu')(x)
normalizer = Reshape((max_nodes * max_bi_relations,))(node_rel_weight)
normalizer = RepeatVector(dense_dim)(normalizer)
normalizer = Permute((2, 1))(normalizer)
x = Multiply()([x, normalizer])
x = Reshape((max_nodes, max_bi_relations, dense_dim))(x)
x = Lambda(
lambda xin: K.sum(xin, axis=2),
output_shape=(None, max_nodes * max_bi_relations, dense_dim),
name=label + '_integrate')(x)
return x
def build(self, input_shape):
self.num_layers = input_shape[1]
self.W = self.add_weight(shape=(self.num_layers, ),
initializer=initializers.Zeros(),
regularizer=regularizers.get(
regularizers.l2(self.l2_coef)),
name='{}_w'.format(self.name))
if self.scale:
self.gamma = self.add_weight(shape=(1, ),
initializer=initializers.Ones(),
name='{}_gamma'.format(self.name))
super(WeightedAverage, self).build(input_shape)
def build(self, input_shape):
# input_shape (None,40)
print(input_shape)
input_dim = input_shape[1]
if self.H == 'Glorot':
self.H = np.float32(np.sqrt(1.5 / (int(input_dim) + self.units)))
#print('Glorot H: {}'.format(self.H))
if self.w_lr_multiplier == 'Glorot':
self.w_lr_multiplier = np.float32(
1. / np.sqrt(1.5 / (int(input_dim) + self.units)))
#print('Glorot learning rate multiplier: {}'.format(self.kernel_lr_multiplier))
self.w_constraint = Clip(-self.H, self.H)
#self.w_initializer = initializers.RandomUniform(-self.H, self.H)
self.w_initializer = initializers.Ones()
self.w_regularizer = regularizers.l2(0.01)
self.w = self.add_weight(shape=(input_dim, self.units),
initializer=self.w_initializer,
name='w',
regularizer=self.w_regularizer,
constraint=self.w_constraint)
#self.bw=self.add_weight(shape=(input_dim,self.units),
# initializer=self.w_initializer,
# name='bw',
# regularizer=self.w_regularizer,
# constraint=self.w_constraint)
#self.bw = binarize(self.w, H=self.H)
if self.use_bias:
self.lr_multipliers = [
self.w_lr_multiplier, self.bias_lr_multiplier
]
self.bias = self.add_weight(
shape=(self.units,
), # is this shape right??? # 假设这个weight每一个都不一样好了先！
initializer=self.w_initializer,
name='bias')
#regularizer=self.bias_regularizer,
#constraint=self.bias_constraint)
else:
self.lr_multipliers = [self.w_lr_multiplier]
self.bias = None
self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
self.built = True
self.binary = binarize(self.w, H=self.H)
def cross_validation(model, X_train, X_val, y_train, y_val):
def get_model(dropout=0.1, learning=0.1, kernel='uniform'):
return create_model(X_train,
dropout=dropout,
learning=learning,
kernel=kernel)
# create the sklearn model for the network
model_init_batch_epoch_CV = KerasRegressor(build_fn=get_model, verbose=1)
# we choose the initializers that came at the top in our previous cross-validation!!
zero = initializers.Zeros()
ones = initializers.Ones()
constant = initializers.Constant(value=0)
rand = initializers.RandomNormal(
mean=0.0, stddev=0.05, seed=None
) # cannot use this option for the moment, need to find the correct syntax
uniform = 'uniform'
kernel = [zero, ones, uniform]
batches = [1000, 5000, 10000]
epochs = [10, 30]
dropout = [0.1, 0.2, 0.5]
learning = [0.01, 0.001, 0.0001]
# grid search for initializer, batch size and number of epochs
param_grid = dict(batch_size=batches,
epochs=epochs,
dropout=dropout,
kernel=kernel,
learning=learning)
grid = GridSearchCV(estimator=model_init_batch_epoch_CV,
param_grid=param_grid,
cv=3,
n_jobs=-1)
grid_result = grid.fit(X_train, y_train)
# printresults
print(
f'Best Accuracy for {grid_result.best_score_:.4} using {grid_result.best_params_}'
)
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print(f'mean={mean:.4}, std={stdev:.4} using {param}')
def build_model(self):
# Define input layer
states = layers.Input(shape=(self.state_size, ), name='states')
# Add hidden layer
net_states = layers.Dense(units=1,
kernel_initializer=initializers.Ones(),
bias_initializer=initializers.Zeros(),
activation=None)(states)
# Add final output
#net_states = layers.BatchNormalization()(net_states)
value = layers.Activation('relu')(net_states)
# Create Keras model
self.model = models.Model(inputs=states, outputs=value)
# Define optimizer and compile model for training with built-in loss function
optimizer = optimizers.Adam()
self.model.compile(optimizer=optimizer, loss='mse')
def build(self, input_shape):
self.theta_p = self.add_weight(
name='theta_p',
shape=(self.n_centroids, ),
initializer=initializers.Constant(1 / self.n_centroids),
trainable=True,
)
self.u_p = self.add_weight(
name='u_p',
shape=(self.latent_dims, self.n_centroids),
initializer=initializers.he_uniform(),
trainable=True,
)
self.lambda_p = self.add_weight(
name='lambda_p',
shape=(self.latent_dims, self.n_centroids),
initializer=initializers.Ones(),
trainable=True,
constraint=NonZero(),
)
super(GMMLayer_2, self).build(input_shape)
def _init_model(self):
in_me = layers.Input(shape=(2, ))
me = layers.Dense(4, activation='relu')(in_me)
me_out = layers.Dense(1, activation='tanh')(me)
in_opponent = layers.Input(shape=(2, ))
opponent = layers.Dense(4, activation='relu')(in_opponent)
opponent_out = layers.Dense(1, activation='tanh')(opponent)
merged = layers.subtract([me_out, opponent_out])
out = layers.Dense(1,
kernel_initializer=initializers.Ones(),
activation='tanh')(merged)
model = models.Model(inputs=[in_me, in_opponent], outputs=out)
model.compile(loss='mse', optimizer=Adam(lr=self.rate))
model.summary()
# initialize known states
model.fit(self._reshape((0, 10, 150, 20)), [[-1]])
model.fit(self._reshape((10, 0, 20, 150)), [[1]])
return model
model = Sequential()
model.add(
Dense(count,
input_dim=input_dim,
kernel_initializer=wi.Zeros(),
bias_initializer=wi.Zeros()))
plot_weights(weights=model.get_weights(),
x=np.arange(0, count, 1),
title='Zeros')
model = Sequential()
model.add(
Dense(count,
input_dim=input_dim,
kernel_initializer=wi.Ones(),
bias_initializer=wi.Ones()))
plot_weights(weights=model.get_weights(),
x=np.arange(0, count, 1),
title='Ones')
model = Sequential()
model.add(
Dense(count,
input_dim=input_dim,
kernel_initializer=wi.Constant(value=3.0),
bias_initializer=wi.Constant(value=3.0)))
plot_weights(weights=model.get_weights(),
x=np.arange(0, count, 1),
title='Constant(value=3.0)')
)
def test_parameters_by_signature(instance, signature_filter, params):
assert parameters_by_signature(instance, signature_filter) == params
##################################################
# `keras_initializer_to_dict` Scenarios
##################################################
@pytest.mark.parametrize(
["initializer", "initializer_dict"],
[
#################### Normal Initializers ####################
pytest.param(initializers.zeros(), dict(class_name="zeros"), id="zero_0"),
pytest.param(initializers.Zeros(), dict(class_name="zeros"), id="zero_1"),
pytest.param(initializers.ones(), dict(class_name="ones"), id="one_0"),
pytest.param(initializers.Ones(), dict(class_name="ones"), id="one_1"),
pytest.param(initializers.constant(), dict(class_name="constant", value=0), id="c_0"),
pytest.param(initializers.Constant(5), dict(class_name="constant", value=5), id="c_1"),
pytest.param(
initializers.RandomNormal(0.1),
dict(class_name="random_normal", mean=0.1, stddev=0.05, seed=None),
id="rn_0",
),
pytest.param(
initializers.random_normal(mean=0.2, stddev=0.003, seed=42),
dict(class_name="random_normal", mean=0.2, stddev=0.003, seed=42),
id="rn_1",
),
pytest.param(
initializers.RandomUniform(maxval=0.1),
dict(class_name="random_uniform", minval=-0.05, maxval=0.1, seed=None),
def build(self,
dafm_type="dafm-afm",
optimizer="rmsprop",
learning_rate=0.01,
activation="linear",
Q_jk_initialize=0,
section="",
section_count=0,
model1="",
stateful=False,
theta_student="False",
student_count=0,
binary="False"):
skills = np.shape(Q_jk_initialize)[1]
steps = np.shape(Q_jk_initialize)[0]
self.activation = activation
if '-' in self.activation:
activation = self.custom_activation
if dafm_type.split("_")[-1] == "different":
skills = int(float(dafm_type.split("_")[-2]) * skills)
dafm_type = dafm_type.split('_')[0]
if dafm_type.split("_")[0] == "round-fine-tuned":
try:
self.round_threshold = float(dafm_type.split("_")[-1])
dafm_type = dafm_type.split("_")[0]
except:
pass
q_jk_size = skills
if '^' in dafm_type:
q_jk_size = skills
skills = int(float(dafm_type.split('^')[-1]) * skills)
dafm_type = dafm_type.split('^')[0]
self.dafm_type = dafm_type
if dafm_type == "random-uniform" or dafm_type == "random-normal":
qtrainable, finetuning, randomize = True, False, True
self.random_init = dafm_type.split('-')[-1]
elif dafm_type == "dafm-afm":
qtrainable, finetuning, randomize = False, False, False
elif dafm_type == "fine-tuned":
qtrainable, finetuning, randomize = True, True, False
elif dafm_type == "kcinitialize":
qtrainable, finetuning, randomize = True, False, False
elif dafm_type == "round-fine-tuned":
# if not self.round_threshold == -1:
# rounded_Qjk = np.abs(Q_jk1 - Q_jk_initialize)
# Q_jk1[rounded_Qjk <= self.round_threshold] = Q_jk_initialize[rounded_Qjk <= self.round_threshold]
# Q_jk1[rounded_Qjk > self.round_threshold] = np.ones(np.shape(Q_jk_initialize[rounded_Qjk > self.round_threshold])) - Q_jk_initialize[rounded_Qjk > self.round_threshold]
# else:
Q_jk1 = model1.get_layer("Q_jk").get_weights()[0]
Q_jk1 = np.minimum(
np.ones(np.shape(Q_jk1)),
np.maximum(np.round(Q_jk1), np.zeros(np.shape(Q_jk1))))
model1.get_layer("Q_jk").set_weights([Q_jk1])
return model1
elif dafm_type == "qjk-dense":
qtrainable, finetuning, randomize = False, False, False
activation_dense = activation
elif dafm_type == "random-qjk-dense-normal" or dafm_type == "random-qjk-dense-uniform":
qtrainable, finetuning, randomize = False, False, True
self.random_init = dafm_type.split('-')[-1]
activation_dense = activation
else:
print("No Valid Model Found")
sys.exit()
if section == "onehot":
section_input = Input(batch_shape=(None, None, section_count),
name='section_input')
if not theta_student == "False":
student_input = Input(batch_shape=(None, None, student_count),
name='student_input')
virtual_input1 = Input(batch_shape=(None, None, 1),
name='virtual_input1')
if finetuning:
B_k = TimeDistributed(Dense(
skills,
activation='linear',
kernel_initializer=self.f(
model1.get_layer("B_k").get_weights()[0]),
use_bias=False),
name="B_k")(virtual_input1)
T_k = TimeDistributed(Dense(
skills,
activation='linear',
kernel_initializer=self.f(
model1.get_layer("T_k").get_weights()[0]),
use_bias=False),
name="T_k")(virtual_input1)
bias_layer = TimeDistributed(Dense(
1,
activation='linear',
use_bias=False,
kernel_initializer=self.f(
model1.get_layer("bias").get_weights()[0]),
trainable=True),
name="bias")(virtual_input1)
else:
B_k = TimeDistributed(Dense(skills,
activation='linear',
use_bias=False,
trainable=True),
name="B_k")(virtual_input1)
T_k = TimeDistributed(Dense(skills,
activation='linear',
use_bias=False,
trainable=True),
name="T_k")(virtual_input1)
bias_layer = TimeDistributed(Dense(
1,
activation='linear',
use_bias=False,
kernel_initializer=initializers.Zeros(),
trainable=True),
name="bias")(virtual_input1)
step_input = Input(batch_shape=(None, None, steps), name='step_input')
if randomize:
if binary == "False":
Q_jk = TimeDistributed(Dense(
q_jk_size,
use_bias=False,
activation=activation,
kernel_initializer=self.custom_random),
trainable=qtrainable,
name="Q_jk")(step_input)
else:
Q_jk = TimeDistributed(BinaryDense(
q_jk_size,
use_bias=False,
activation=activation,
kernel_initializer=self.custom_random),
trainable=qtrainable,
name="Q_jk")(step_input)
else:
if binary == "False":
Q_jk = TimeDistributed(Dense(
skills,
activation=activation,
kernel_initializer=self.f(Q_jk_initialize),
use_bias=False,
trainable=qtrainable),
trainable=qtrainable,
name="Q_jk")(step_input)
else:
Q_jk = TimeDistributed(BinaryDense(
skills,
activation=activation,
kernel_initializer=self.f(Q_jk_initialize),
trainable=qtrainable,
use_bias=False),
name="Q_jk",
trainable=qtrainable)(step_input)
if dafm_type == "random-qjk-dense-normal" or dafm_type == "random-qjk-dense-uniform":
if binary == "False":
Q_jk = TimeDistributed(Dense(
skills,
activation=activation_dense,
use_bias=False,
kernel_initializer=self.custom_random,
trainable=True),
name="Q_jk_dense")(Q_jk)
else:
Q_jk = TimeDistributed(BinaryDense(
skills,
activation=activation_dense,
use_bias=False,
kernel_initializer=self.custom_random,
trainable=True),
name="Q_jk_dense")(Q_jk)
elif dafm_type == "qjk-dense":
if binary == 'False':
Q_jk = TimeDistributed(Dense(
skills,
activation=activation_dense,
use_bias=False,
kernel_initializer=initializers.Identity(),
trainable=True),
name="Q_jk_dense")(Q_jk)
else:
Q_jk = TimeDistributed(BinaryDense(
skills,
activation=activation_dense,
use_bias=False,
kernel_initializer=initializers.Identity(),
trainable=True),
name="Q_jk_dense")(Q_jk)
else:
pass
Qjk_mul_Bk = multiply([Q_jk, B_k])
sum_Qjk_Bk = TimeDistributed(Dense(
1,
activation='linear',
trainable=False,
kernel_initializer=initializers.Ones(),
use_bias=False),
trainable=False,
name="sum_Qjk_Bk")(Qjk_mul_Bk)
P_k = SimpleRNN(skills,
kernel_initializer=initializers.Identity(),
recurrent_initializer=initializers.Identity(),
use_bias=False,
trainable=False,
activation='linear',
return_sequences=True,
name="P_k")(Q_jk)
Qjk_mul_Pk_mul_Tk = multiply([Q_jk, P_k, T_k])
sum_Qjk_Pk_Tk = TimeDistributed(
Dense(1,
activation='linear',
trainable=False,
kernel_initializer=initializers.Ones(),
use_bias=False),
trainable=False,
name="sum_Qjk_Pk_Tk")(Qjk_mul_Pk_mul_Tk)
Concatenate = concatenate([bias_layer, sum_Qjk_Bk, sum_Qjk_Pk_Tk])
if not (theta_student == "False"):
if finetuning:
theta = TimeDistributed(Dense(
1,
activation="linear",
use_bias=False,
kernel_initializer=self.f(
model1.get_layer("theta").get_weights()[0])),
name='theta')(student_input)
else:
theta = TimeDistributed(Dense(1,
activation="linear",
use_bias=False),
name='theta')(student_input)
Concatenate = concatenate([Concatenate, theta])
if section == "onehot":
if finetuning:
S_k = TimeDistributed(Dense(
1,
activation="linear",
use_bias=False,
kernel_initializer=self.f(
model1.get_layer("S_k").get_weights()[0])),
name='S_k')(section_input)
else:
S_k = TimeDistributed(Dense(1,
activation="linear",
use_bias=False),
name='S_k')(section_input)
Concatenate = concatenate([Concatenate, S_k])
output = TimeDistributed(Dense(1,
activation="sigmoid",
trainable=False,
kernel_initializer=initializers.Ones(),
use_bias=False),
trainable=False,
name="output")(Concatenate)
if section == "onehot" and not (theta_student == "False"):
model = Model(inputs=[
virtual_input1, step_input, section_input, student_input
],
outputs=output)
elif section == "onehot" and theta_student == "False":
model = Model(inputs=[virtual_input1, step_input, section_input],
outputs=output)
elif not (section == "onehot") and not (theta_student == "False"):
model = Model(inputs=[virtual_input1, step_input, student_input],
outputs=output)
else:
model = Model(inputs=[virtual_input1, step_input], outputs=output)
d_optimizer = {
"rmsprop": optimizers.RMSprop(lr=learning_rate),
"adam": optimizers.Adam(lr=learning_rate),
"adagrad": optimizers.Adagrad(lr=learning_rate)
}
model.compile(optimizer=d_optimizer[optimizer], loss=self.custom_bce)
return model
def AddResidualBlock(x, each_channel_size, stepchanger = False):
if stepchanger:
shortcut = x
x = layers.Conv2D(each_channel_size, kernel_size=common_filter_size, strides=(2,2), padding='same', activation='relu')(x)
x = bn(x)
x = layers.Conv2D(each_channel_size, kernel_size=common_filter_size, strides=(1,1), padding='same', activation='relu')(x)
x = bn(x)
# Took some time to fogure out how to zero-pad when increase in channel
layer = layers.Conv2D(x.shape[3], kernel_size=2, strides=(2,2), use_bias=False, kernel_initializer=initializers.Ones())
# Not learned!
layer.trainable = False
shortcut = layer(shortcut)
shortcut = bn(shortcut)
x = layers.add([shortcut,x])
else:
shortcut = x
x = layers.Conv2D(each_channel_size, kernel_size=common_filter_size, strides=(1,1), padding='same', activation='relu')(x)
x = bn(x)
x = layers.Conv2D(each_channel_size, kernel_size=common_filter_size, strides=(1,1), padding='same', activation='relu')(x)
x = bn(x)
x = layers.add([shortcut, x])
return x
def build_classifier(self):
input = Input(shape=(dim_less, ))
model = Dense(units=1, activation='sigmoid',
init=keras_init.Ones())(input)
# model = Dense(units=1, activation='sigmoid')(input)
return Model(input, model)
from keras.callbacks import History
history = History()
print("[INFO] loading dataset...")
X_train = np.load('X_train.npy')
X_val = np.load('X_val.npy')
#X_test = np.load('X_test.npy')
y_train = np.load('y_train.npy')
y_val = np.load('y_val.npy')
#y_test = np.load('y_test.npy')
print("[INFO] build and compiling model...")
zero = initializers.Zeros()
ones = initializers.Ones()
#constant = initializers.Constant(value=0
#rand = initializers.RandomNormal(mean=0.0, stddev=0.05, seed=None))
#model.add(layers.BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True,
# beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros',
# moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None,
# beta_constraint=None, gamma_constraint=None))
relu = 'relu'
#ly = LeakyReLU(alpha=0.05)
#activation=ly,
model = tf.keras.Sequential()
model.add(
def create_model(X_train1, y_train1, X_train2, y_train2, X_train3, y_train3,
X_train4, y_train4, X_train5, y_train5, X_train6, y_train6,
X_train7, y_train7, X_train8, y_train8):
# L1 = [{'batch_size': 1000, 'dropout': 0.2, 'epochs': 30, 'kernel': <keras.initializers.Ones object at 0x7f2532713128>, 'learning': 0.01}] # 1JHC
ones = initializers.Ones()
model1 = tf.keras.Sequential()
model1.add(
layers.Dense(64,
input_dim=X_train1.shape[1],
kernel_initializer=ones,
activation='relu'))
model1.add(layers.Dropout(0.2))
model1.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model1.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model1.add(layers.Dropout(0.2))
model1.add(layers.Dense(1))
# compile model
rms = RMSprop(lr=0.01, rho=0.9, epsilon=None, decay=0.0)
model1.compile(loss='mse', optimizer=rms, metrics=['mae'])
model1.output_shape
model1.summary()
model1.get_config()
model1.get_weights()
print("[INFO] training model 1...")
history = model1.fit(X_train1,
y_train1,
epochs=30,
verbose=1,
batch_size=1000)
# list all data in history
print(history.history.keys())
# we choose the initializers that came at the top in our previous cross-validation!!
# zero = initializers.Zeros()
# ones = initializers.Ones()
# constant = initializers.Constant(value=0)
# rand = initializers.RandomNormal(mean=0.0, stddev=0.05, seed=None) # cannot use this option for the moment, need to find the correct syntax
# uniform = 'uniform'
model1.save('model1.h5')
print("[INFO] Preparing model 2...")
# L2 = [{'batch_size': 1000, 'dropout': 0.2, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 2JHH
model2 = tf.keras.Sequential()
model2.add(
layers.Dense(64,
input_dim=X_train2.shape[1],
kernel_initializer='uniform',
activation='relu'))
model2.add(layers.Dropout(0.2))
model2.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model2.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model2.add(layers.Dropout(0.2))
model2.add(layers.Dense(1))
# compile model
rms = RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
model2.compile(loss='mse', optimizer=rms, metrics=['mae'])
model2.output_shape
model2.summary()
model2.get_config()
model2.get_weights()
print("[INFO] training model 2...")
history = model2.fit(X_train2,
y_train2,
epochs=30,
verbose=1,
batch_size=1000)
# list all data in history
print(history.history.keys())
model2.save('model2.h5')
print("[INFO] Preparing model 3...")
# L3 = [{'batch_size': 1000, 'dropout': 0.2, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 1JHN
# L4 = [{'batch_size': 1000, 'dropout': 0.1, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 2JHN
# L5 = [{'batch_size': 1000, 'dropout': 0.1, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 2JHC
# L6 = [{'batch_size': 1000, 'dropout': 0.1, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 3JHH
# L7 = [{'batch_size': 1000, 'dropout': 0.1, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 3JHC
# L8 = [{'batch_size': 1000, 'dropout': 0.1, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 3JHN
model3 = tf.keras.Sequential()
model3.add(
layers.Dense(64,
input_dim=X_train3.shape[1],
kernel_initializer='uniform',
activation='relu'))
model3.add(layers.Dropout(0.2))
model3.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model3.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model3.add(layers.Dropout(0.2))
model3.add(layers.Dense(1))
# compile model
rms = RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
model3.compile(loss='mse', optimizer=rms, metrics=['mae'])
model3.output_shape
model3.summary()
model3.get_config()
model3.get_weights()
print("[INFO] training model 3...")
history = model3.fit(X_train3,
y_train3,
epochs=30,
verbose=1,
batch_size=1000)
# list all data in history
print(history.history.keys())
model3.save('model3.h5')
print("[INFO] Preparing model 4...")
# L4 = [{'batch_size': 1000, 'dropout': 0.1, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 2JHN
# L5 = [{'batch_size': 1000, 'dropout': 0.1, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 2JHC
# L6 = [{'batch_size': 1000, 'dropout': 0.1, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 3JHH
# L7 = [{'batch_size': 1000, 'dropout': 0.1, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 3JHC
# L8 = [{'batch_size': 1000, 'dropout': 0.1, 'epochs': 30, 'kernel': 'uniform', 'learning': 0.001}] # 3JHN
model4 = tf.keras.Sequential()
model4.add(
layers.Dense(64,
input_dim=X_train4.shape[1],
kernel_initializer='uniform',
activation='relu'))
model4.add(layers.Dropout(0.1))
model4.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model4.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model4.add(layers.Dropout(0.1))
model4.add(layers.Dense(1))
# compile model
rms = RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
model4.compile(loss='mse', optimizer=rms, metrics=['mae'])
model4.output_shape
model4.summary()
model4.get_config()
model4.get_weights()
print("[INFO] training model 4...")
history = model4.fit(X_train4,
y_train4,
epochs=30,
verbose=1,
batch_size=1000)
# list all data in history
print(history.history.keys())
print("[INFO] Preparing model 5...")
model4.save('model4.h5')
model5 = tf.keras.Sequential()
model5.add(
layers.Dense(64,
input_dim=X_train5.shape[1],
kernel_initializer='uniform',
activation='relu'))
model5.add(layers.Dropout(0.1))
model5.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model5.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model5.add(layers.Dropout(0.1))
model5.add(layers.Dense(1))
# compile model
rms = RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
model5.compile(loss='mse', optimizer=rms, metrics=['mae'])
model5.output_shape
model5.summary()
model5.get_config()
model5.get_weights()
print("[INFO] training model 5...")
history = model5.fit(X_train5,
y_train5,
epochs=30,
verbose=1,
batch_size=1000)
# list all data in history
print(history.history.keys())
model5.save('model5.h5')
print("[INFO] Preparing model 6...")
model6 = tf.keras.Sequential()
model6.add(
layers.Dense(64,
input_dim=X_train6.shape[1],
kernel_initializer='uniform',
activation='relu'))
model6.add(layers.Dropout(0.1))
model6.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model6.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model6.add(layers.Dropout(0.1))
model6.add(layers.Dense(1))
# compile model
rms = RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
model6.compile(loss='mse', optimizer=rms, metrics=['mae'])
model6.output_shape
model6.summary()
model6.get_config()
model6.get_weights()
print("[INFO] training model 6...")
history = model6.fit(X_train6,
y_train6,
epochs=30,
verbose=1,
batch_size=1000)
# list all data in history
print(history.history.keys())
model6.save('model6.h5')
print("[INFO] Preparing model 7...")
model7 = tf.keras.Sequential()
model7.add(
layers.Dense(64,
input_dim=X_train7.shape[1],
kernel_initializer='uniform',
activation='relu'))
model7.add(layers.Dropout(0.1))
model7.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model7.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model7.add(layers.Dropout(0.1))
model7.add(layers.Dense(1))
# compile model
rms = RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
model7.compile(loss='mse', optimizer=rms, metrics=['mae'])
model7.output_shape
model7.summary()
model7.get_config()
model7.get_weights()
print("[INFO] training model 7...")
history = model7.fit(X_train7,
y_train7,
epochs=30,
verbose=1,
batch_size=1000)
# list all data in history
print(history.history.keys())
model7.save('model7.h5')
print("[INFO] Preparing model 8...")
model8 = tf.keras.Sequential()
model8.add(
layers.Dense(64,
input_dim=X_train8.shape[1],
kernel_initializer='uniform',
activation='relu'))
model8.add(layers.Dropout(0.1))
model8.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model8.add(
layers.Dense(64, kernel_initializer='uniform', activation='relu'))
model8.add(layers.Dropout(0.1))
model8.add(layers.Dense(1))
# compile model
rms = RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)
model8.compile(loss='mse', optimizer=rms, metrics=['mae'])
model8.output_shape
model8.summary()
model8.get_config()
model8.get_weights()
print("[INFO] training model 8...")
history = model8.fit(X_train8,
y_train8,
epochs=30,
verbose=1,
batch_size=1000)
# list all data in history
print(history.history.keys())
model8.save('model8.h5')
def testing_network(
world_size: [int], target_num: int, drone_goal: str, drone_num: int,
extra_drone_num: int, world_gain_peak_range: [float],
world_gain_var_range: [float], world_evolution_speed: [float],
drone_comm: float, drone_view: float, drone_memory: int,
drone_battery: float, action_step: int, max_age: int,
lookahead_step: int, malus: float, final_bonus: float, malus_sm: float,
random_episode: bool, alpha: float, alpha_dec: float, epsilon: float,
epsilon_dec: float, temperature: float, temperature_dec: float,
state_MR: int, limit_MR: bool, three_MR: bool, prioritized: bool,
perc_pos_MR: float, perc_neg_MR1: float, perc_neg_MR2: float,
pretrain_episode: int, train_episode: int, test_episode: int,
step_num: int, testing_update: int, model_update: int,
batch_update: int, batch_size: int, learning_rate: float,
neurons_fully: int, drop_rate: float, dueling: bool, epochs_num: int,
gamma: float, steps_per_epoch: int, verbose: bool, version: int):
random.seed(100)
initializers.Ones()
env: DronesDiscreteEnv = gym.make('DronesDiscrete-v0')
# Generate a world-map, where a groups of target is moving
world = WorldMap(world_size, target_num, world_gain_peak_range,
world_gain_var_range, world_evolution_speed)
# Define the log file
output_log = "Log_files/"
# Initialize relay memory
memory_replay = []
mem2 = []
prob_MR = []
pos_MR = []
neg_MR1 = []
neg_MR2 = []
mem_replay_with_loss = create_mem_replay_with_loss()
# Initialize the success data vector
success_vec = []
success_vec1 = []
success_episodes = []
Ac0 = []
Ac1 = []
Ac2 = []
Ac3 = []
Ac4 = []
A_0 = 0
A_1 = 0
A_2 = 0
A_3 = 0
A_4 = 0
SMR = [0, 0, 0, 0, 0]
GSMR = []
AM = [0, 0, 0]
AAM = []
VARIATIONS = []
VAR = []
VARQ = 0
MINQ = []
MINQV = 0
MAXQ = []
MAXQV = 0
MEANQ = []
MEANQV = 0
Mappa = np.zeros((world_size[0], world_size[0]))
LOSSES = np.zeros((4, 5000))
# Setting neural network
step_model = create_step_model(world_size[0], 5, learning_rate,
neurons_fully, drop_rate, dueling)
#step_model.load_weights('target_model23_v0.h5')
#print (step_model.summary())
target_model = keras.models.clone_model(step_model)
target_model.set_weights(step_model.get_weights())
#print (target_model.summary())
plot_model(step_model,
to_file='model_plot.pdf',
show_shapes=True,
show_layer_names=True)
# Setting early stopping and history
#early = EarlyStopping(monitor='val_loss', patience=patience, verbose=0, mode='auto')
history = LossHistory()
H = []
# ------------------------ PRE-PROCESSING PHASE ------------------------
# Pre-training phase:
# Use a random policy to choose actions
# Save the sample [state, action, reward, new_state] in the replay memory
# No training is carried out
if prioritized == True:
update_MR = True
else:
update_MR = False
if state_MR == 0:
pretraining_done = True
else:
pretraining_done = False
if pretraining_done == True:
#print("INITIALIZATION MEMORY-REPLAY...")
for episode_index in tqdm(range(pretrain_episode)):
counter = 0
#print("Episode index: ", episode_index)
# Generate a random episode
if random_episode:
# Generate new map
world = WorldMap(world_size, target_num, world_gain_peak_range,
world_gain_var_range, world_evolution_speed)
#print ("0")
log_name = output_log + "env_pretrain_" + str(
episode_index) + ".txt"
log_file = open(log_name, "w+")
# Configure new environment
train = True
env.configure_environment(world, drone_goal, drone_num,
extra_drone_num, drone_comm, drone_view,
drone_memory, drone_battery, action_step,
max_age, lookahead_step, malus,
final_bonus, log_file, verbose, train,
malus_sm)
#print ("1")
# Get the initial state of the system
# If needed, normalize the state as you desire
state = env.get_state()
z = 0
#print ("2")
for step in range(step_num):
for j in range(drone_num):
own_map = state[j]
others_map = np.zeros((world_size[0], world_size[0]))
for i in range(drone_num):
if i != j:
others_map += state[i]
#print ("3")
Mappa[int(np.argmax(state[0]) / world_size[0]),
np.argmax(state[0]) % world_size[0]] += 1
Mappa[int(np.argmax(state[1]) / world_size[0]),
np.argmax(state[1]) % world_size[0]] += 1
number = env.get_available_targets()
if number == 0:
AM[0] += 1
elif number == 1:
AM[1] += 1
else:
AM[2] += 1
model_input_state = state[[0, 1, drone_num + 1], :]
model_input_state[0] = own_map
model_input_state[1] = others_map
action = env.get_random_direction()
# Random action
for i in range(drone_num):
if i != j:
action[i] = 0
#print (action)
env.action_direction(action)
# Carry out a new action
new_state = env.get_state()
# New system state
#print ("4")
model_input_newstate = new_state[[0, 1, drone_num + 1], :]
model_input_newstate[0] = new_state[j]
model_input_newstate[1] = others_map
explore_reward, exploit_reward = env.get_reward(
) # Obtained reward (exploit + explore rewards)
reward = exploit_reward[j] # Overall reward (only exploit)
if reward == -1:
SMR[0] += 1
if reward == -0.4:
SMR[1] += 1
if reward == 1 and np.mean(exploit_reward) < 1:
SMR[3] += 1
if np.mean(exploit_reward) == 1:
SMR[4] += 1
if exploit_reward[0] > -0.4 and exploit_reward[
0] < 1 and exploit_reward[
1] > -0.4 and exploit_reward[1] < 1:
SMR[2] += 1
sample = [
model_input_state, [int(action[j])],
model_input_newstate, reward
] # Sample to be saved in the memory
memory_replay.append(sample)
prob_MR.append([1, 0, 5])
state = new_state
#print (counter)
log_file.close()
np.save('memory_replay', memory_replay)
np.save('prob_MR', prob_MR)
else:
#print ("LOADING MEMORY REPLAY...")
memory_replay = np.load('memory_replay.npy', allow_pickle=True)
memory_replay = memory_replay.tolist()
prob_MR = np.load('prob_MR.npy', allow_pickle=True)
prob_MR = prob_MR.tolist()
# Training phase
# Make actions according to a epsilon greedy or softmax policy
# Periodically train the neural network with batch taken from the replay memory
#print("TRAINING PHASE...")
AVG = []
MAX = []
def epsilon_func(x):
e = (1 - 1 / (1 + np.exp(-x / 100))) * 0.8 + 0.2
if x > 400:
e = e * ((499 - x) / 100)
return e
e = []
for i in range(1000):
e.append(epsilon_func(i - 500))
st = []
for ep in range(1000):
st.append(5000 / (ep + 1)**(1 / 3))
batch_size = 50
#print ("TRAINING PHASE")
# ------------------------ PROCESSING PHASE ------------------------
negative_r1 = []
pos_r = []
pos_r2 = []
negative_r2 = []
null_r = []
counter1 = 0
counter2 = 0
counter3 = 0
counter4 = 0
counter5 = 0
COUNTS = [0, 0, 0, 0]
VAR_COUNTS = [0, 0, 0, 0]
for episode_index in tqdm(range(train_episode)):
CMR0 = 0
CMR1 = 0
CMR2 = 0
CMR3 = 0
if episode_index % 5 == 0:
COUNTS = [0, 0, 0, 0]
VAR_COUNTS = [0, 0, 0, 0]
AAM.append(AM)
AM = [0, 0, 0]
mem_replay_with_loss = create_mem_replay_with_loss()
fit_input_temp = []
fit_output_temp = []
fit_actions_temp = []
counter_MR = 0
epsilon = epsilon_func(episode_index - 500)
avg_avg_loss = 0
avg_max_loss = 0
iter_avg = 0
worst_states = []
GSMR.append(SMR)
SMR = [0, 0, 0, 0]
#print("\n Epsilon: ",epsilon)
# ------------------------ TRAINING PHASE ------------------------
#print("Training episode ", episode_index, " with epsilon ", epsilon)
# Generate a random episode
if random_episode:
# Generate new map
world = WorldMap(world_size, target_num, world_gain_peak_range,
world_gain_var_range, world_evolution_speed)
log_name = output_log + "env_train_" + str(episode_index) + ".txt"
log_file = open(log_name, "w")
# Configure new environment
train = True
env.configure_environment(world, drone_goal, drone_num,
extra_drone_num, drone_comm, drone_view,
drone_memory, drone_battery, action_step,
max_age, lookahead_step, malus, final_bonus,
log_file, verbose, train, malus_sm)
# Get the initial state of the system
# If needed, normalize the state as you desire
state = env.get_state()
for step in range(step_num):
for j in range(drone_num):
model_input = state # The input might be different than the environment state
model_input = np.asarray(model_input)
number = env.get_available_targets()
if number == 0:
AM[0] += 1
elif number == 1:
AM[1] += 1
else:
AM[2] += 1
others_map = np.zeros((world_size[0], world_size[0]))
for i in range(drone_num):
if i != j:
others_map += state[i]
model_input = model_input[[0, 1, drone_num + 1], :]
model_input[0] = state[j]
model_input[1] = others_map
model_input_state = copy.deepcopy(model_input)
#model_input=np.asarray(model_input)
model_input = model_input.reshape(1, 3, world_size[0],
world_size[0])
#print (model_input)
action = np.ndarray((1, 5))
for i in range(5):
action[0, i] = 1
greedy_action = np.zeros(drone_num)
greedy_action[j] = np.argmax(
target_model.predict([model_input,
action])) # Greedy action
random_action = env.get_random_direction()
# Random action
for i in range(drone_num):
if i != j:
random_action[i] = 0
action_type = 0
if np.random.uniform(0, 1) < epsilon:
action1 = random_action
action = []
for i in range(drone_num):
action.append(int(action1[i]))
else:
action = greedy_action
action_type = 1
env.action_direction(action)
# Carry out a new action
new_state = env.get_state()
# New system state
explore_reward, exploit_reward = env.get_reward()
# Obtained reward (exploit + explore rewards)
reward = exploit_reward[j] # Overall reward (only exploit)
model_input_new_state = copy.deepcopy(model_input_state)
model_input_new_state[0] = new_state[j]
model_input_new_state[drone_num] = new_state[drone_num + 1]
sample = [
model_input_state, [int(action[j])], model_input_new_state,
reward
] # Sample to be saved in the memory
memory_replay.append(sample)
prob_MR.append([1, 0, 5])
state = new_state
if (
step + 1
) % batch_update == 0: # Each "batch_update" steps, train the "step_model" NN
# Choose a set of samples from the memory and insert them in the "samples" list:
probability = np.random.rand(1)
type_training = 0
if probability < 1.1 or len(mem2) < batch_size:
samples_indexes = random.sample(
list(range(len(memory_replay))), batch_size)
for i in range(len(samples_indexes)):
mem2.append(memory_replay[samples_indexes[i]])
samples = []
for s_index in samples_indexes:
samples.append(memory_replay[s_index])
else:
choices = np.arange(len(memory_replay))
probabilities = []
somma = 0
for i in range(len(prob_MR)):
probabilities.append(prob_MR[i][0])
somma += prob_MR[i][0]
probabilities = probabilities / somma
samples_indexes = np.random.choice(choices,
batch_size,
p=probabilities)
type_training = 1
samples = []
for s_index in samples_indexes:
samples.append(memory_replay[s_index])
# Deep Q-learning approach
fit_input = [] # Input batch of the model
fit_output = [] # Desired output batch for the input
fit_actions = []
fit_actions_predictions = []
for sample in samples:
sample_state = sample[0] # Previous state
sample_action = sample[1] # Action made
sample_new_state = sample[2] # Arrival state
sample_reward = sample[3] # Obtained reward
sample_new_state = sample_new_state.reshape(
1, 3, world_size[0], world_size[0])
action = np.ndarray((1, 5))
for i in range(5):
action[0, i] = 1
sample_goal = sample_reward + gamma * np.max(
target_model.predict([sample_new_state, action]))
sample_state = np.asarray(sample_state)
sample_state = sample_state.reshape(
1, 3, world_size[0], world_size[0])
act = np.ndarray((1, 5))
for i in range(5):
if i == sample_action[0]:
act[0, i] = 1
else:
act[0, i] = 0
sample_output = step_model.predict(
[np.asarray(sample_state), action])[0]
#print (sample_action)
for i in range(5):
if i == sample_action[0]:
sample_output[i, 0] = (1 - alpha) * sample_output[
sample_action] + alpha * sample_goal
else:
sample_output[i, 0] = 0
#print (sample_state[0])
#print (sample_output)
#print (act[0])
fit_input.append(sample_state[0]) # Input of the model
fit_input_temp.append(sample_state[0])
fit_output.append(sample_output) # Output of the model
fit_output_temp.append(sample_output)
fit_actions.append(act[0])
fit_actions_temp.append(act[0])
fit_actions_predictions.append(action[0])
# Fit the model with the given batch
step_model.fit(
[np.asarray(fit_input),
np.asarray(fit_actions)],
np.asarray(fit_output),
batch_size=None,
epochs=epochs_num,
steps_per_epoch=steps_per_epoch,
callbacks=[history],
verbose=0)
mean_loss = np.mean(history.losses)
#LOSSES[MR_type,episode_index]+=mean_loss
H.append(history.losses)
output = step_model.predict(
[np.asarray(fit_input),
np.asarray(fit_actions)])
total_output = step_model.predict([
np.asarray(fit_input),
np.asarray(fit_actions_predictions)
])
loss = []
for i in range(batch_size):
loss.append(
(output[i][np.argmax(np.asarray(fit_actions[i]))] -
np.asarray(fit_output[i][np.argmax(
np.asarray(fit_actions[i]))]))**2)
for i in range(batch_size):
prob_MR[samples_indexes[i]][0] = loss[i][0]
if prob_MR[samples_indexes[i]][2] != 5:
if memory_replay[samples_indexes[i]][3] == -1:
COUNTS[0] += 1
if np.argmax(total_output[i]) != prob_MR[
samples_indexes[i]][2]:
VAR_COUNTS[0] += 1
prob_MR[samples_indexes[i]][2] = np.argmax(
total_output[i])
elif memory_replay[samples_indexes[i]][3] == -0.4:
COUNTS[1] += 1
if np.argmax(total_output[i]) != prob_MR[
samples_indexes[i]][2]:
VAR_COUNTS[1] += 1
prob_MR[samples_indexes[i]][2] = np.argmax(
total_output[i])
elif memory_replay[samples_indexes[i]][3] == 1:
COUNTS[3] += 1
if np.argmax(total_output[i]) != prob_MR[
samples_indexes[i]][2]:
VAR_COUNTS[3] += 1
prob_MR[samples_indexes[i]][2] = np.argmax(
total_output[i])
else:
COUNTS[2] += 1
if np.argmax(total_output[i]) != prob_MR[
samples_indexes[i]][2]:
VAR_COUNTS[2] += 1
prob_MR[samples_indexes[i]][2] = np.argmax(
total_output[i])
else:
prob_MR[samples_indexes[i]][2] = np.argmax(
total_output[i])
counter_MR += batch_size
if (
step + 1
) % model_update == 0: # Each "model_update" steps, substitute the target_model with the step_model
target_model.set_weights(step_model.get_weights())
log_file.close()
# Testing phase
# Make actions ONLY according to the NN model output (temperature is 0)
# No training is carried out
# The results are compared with the lookahead policy implemented in the environment
# ------------------------ TESTING PHASE ------------------------
#if episode_index%5==0 and episode_index>0:
#VARIATIONS.append([VAR_COUNTS[0]/COUNTS[0],VAR_COUNTS[1]/COUNTS[1],VAR_COUNTS[2]/COUNTS[2],VAR_COUNTS[3]/COUNTS[3]])
if episode_index % testing_update == 0:
#print("TESTING PHASE...")
if episode_index > 0:
Ac0.append(A_0 / 100)
Ac1.append(A_1 / 100)
Ac2.append(A_2 / 100)
Ac3.append(A_3 / 100)
Ac4.append(A_4 / 100)
A_0 = 0
A_1 = 0
A_2 = 0
A_3 = 0
A_4 = 0
success_vec.append([])
success_vec1.append([])
mean_reward = 0
PERCENTS = 0
for test_index in range(test_episode):
REWARD = 0
# Generate a random episode
if random_episode:
# Generate new map
world = WorldMap(world_size, target_num,
world_gain_peak_range,
world_gain_var_range,
world_evolution_speed)
log_name = output_log + "env_test_" + str(
episode_index) + ".txt"
log_file = open(log_name, "w")
# Configure new environment
train = True
env.configure_environment(world, drone_goal, drone_num,
extra_drone_num, drone_comm,
drone_view, drone_memory,
drone_battery, action_step, max_age,
lookahead_step, malus, final_bonus,
log_file, verbose, train, malus_sm)
# Get the initial state of the system
# If needed, normalize the state as you desire
state = env.get_state()
for step in range(40):
# env.render()
# Choose always the greedy action using the target_model
for j in range(drone_num):
REWARD1 = 0
actions = np.ndarray((1, 5))
for i in range(5):
actions[0, i] = 1
model_input = state
own_map = state[j]
model_input = np.asarray(model_input)
model_input = model_input[[0, 1, drone_num + 1], :]
others_map = np.zeros((world_size[0], world_size[0]))
for i in range(drone_num):
if i != j:
others_map += state[i]
model_input[1] = others_map
model_input[0] = own_map
model_input = model_input.reshape(
1, 3, world_size[0], world_size[0])
action = np.zeros(drone_num)
action[j] = np.argmax(
target_model.predict([model_input, actions]))
env.action_direction(action)
# Carry out a new action
if action[0] == 0:
A_0 += 1
elif action[0] == 1:
A_1 += 1
elif action[0] == 2:
A_2 += 1
elif action[0] == 3:
A_3 += 1
else:
A_4 += 1
new_state = env.get_state()
# New system state
explore_reward, exploit_reward = env.get_reward()
reward = np.mean(exploit_reward)
if np.mean(exploit_reward) == 1:
counter2 += drone_num
for i in range(drone_num):
if exploit_reward[i] == -malus_sm:
counter1 += 1
for i in range(drone_num):
if exploit_reward[i] == -1:
counter3 += 1
for i in range(drone_num):
if exploit_reward[i] == 1 and np.mean(
exploit_reward) < 1:
counter4 += 1
for i in range(drone_num):
if exploit_reward[i] < 1 and exploit_reward[i] > 0:
counter5 += 1
REWARD += reward
REWARD1 += reward
state = new_state # Substitute the previous state with the new state
#print (REWARD/30)
success_vec[-1].append(REWARD / 80)
if REWARD1 == 1:
success_vec1[-1].append(1)
PERCENTS += 1
else:
success_vec1[-1].append(0)
mean_reward += REWARD / 80
log_file.close()
#print ("\n Success rate: \n")
print(mean_reward / 100)
#print (PERCENTS)
negative_r1.append(counter1)
pos_r.append(counter2)
negative_r2.append(counter3)
pos_r2.append(counter4)
null_r.append(counter5)
success_episodes.append(PERCENTS)
print(PERCENTS)
counter1 = 0
counter2 = 0
counter3 = 0
counter4 = 0
counter5 = 0
#print("\n Mean success ratio: ", np.mean(success_vec[-1]))
# Decrease system temperatures
log_name = output_log + "example" + str(episode_index) + ".txt"
log_file = open(log_name, "w")
env.close()
log_file.close()
target_model.save('target_model' + str(drone_num) + str(target_num) +
'_v' + str(version) + '.h5')
del step_model
del target_model
return success_vec, success_vec1, H, negative_r1, pos_r, negative_r2, pos_r2, null_r, Ac0, Ac1, Ac2, Ac3, Ac4, SMR, GSMR, AM, AAM, Mappa, VAR, MINQ, MAXQ, MEANQ, LOSSES, success_episodes, VARIATIONS
] #,'seasonEndYear','seasonWeek', 'N1', 'E1', 'SC3', 'SC2', 'D1', 'B1', 'I2', 'G1', 'E3', 'T1', 'EC','D2', 'F1', 'I1', 'P1', 'SP2', 'SP1', 'E2', 'SC0', 'E0', 'F2', 'SC1']
train_x = combined_data[predictors][
combined_data['seasonEndYear'] <= train_to_season].values
train_y = ((combined_data['FTR'] == 'H') *
1)[combined_data['seasonEndYear'] <= train_to_season].values
test_x = combined_data[predictors][combined_data['seasonEndYear'] == (
train_to_season + 1)].values
input_dimension = len(predictors)
model = Sequential()
model.add(
Dense(input_dimension * 2,
input_dim=input_dimension,
activation='relu',
init=initializers.Ones()))
model.add(Dense(input_dimension, input_dim=input_dimension, activation='relu'))
model.add(Dense(input_dimension, input_dim=input_dimension, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
optim_rmsprop = optimizers.RMSprop(lr=0.001, rho=0.9)
optim_sgd = optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.5, nesterov=True)
optim_adagrad = optimizers.Adagrad(lr=0.01)
model.compile(loss='binary_crossentropy',
optimizer=optim_adagrad,
metrics=['accuracy'])
model.fit(train_x, train_y, epochs=40, batch_size=10)
nn_combined_outcomes = combined_data[[
def build(self, input_shape):
input_dim = input_shape[-1]
self.kernel = self.add_weight(shape=(input_dim, self.memdim * 3),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.recurrent_kernel = self.add_weight(
shape=(self.memdim, self.memdim * 3),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint)
self.W_q = self.add_weight(shape=(self.memdim, self.memdim),
name='W_q',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.W_k = self.add_weight(shape=(self.memdim, self.memdim),
name='W_k',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.W_v = self.add_weight(shape=(self.memdim, self.memdim),
name='W_v',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.mlp_kernel_1 = self.add_weight(shape=(self.memdim, self.memdim),
name='mlp_kernel_1',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.mlp_kernel_2 = self.add_weight(shape=(self.memdim, self.memdim),
name='mlp_kernel_2',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.mlp_gain_1 = self.add_weight(shape=(self.memdim,),
name='mlp_gain_1',
initializer=initializers.Ones(),
regularizer=None,
constraint=None)
self.mlp_gain_2 = self.add_weight(shape=(self.memdim,),
name='mlp_gain_2',
initializer=initializers.Ones(),
regularizer=None,
constraint=None)
self.mlp_bias_1 = self.add_weight(shape=(self.memdim,),
name='mlp_bias_1',
initializer=initializers.Zeros(),
regularizer=None,
constraint=None)
self.mlp_bias_2 = self.add_weight(shape=(self.memdim,),
name='mlp_bias_2',
initializer=initializers.Zeros(),
regularizer=None,
constraint=None)
if self.use_bias:
if self.unit_forget_bias:
def bias_initializer(_, *args, **kwargs):
return K.concatenate([
self.bias_initializer((self.memdim,), *args, **kwargs),
initializers.Ones()((self.memdim,), *args, **kwargs),
self.bias_initializer((self.memdim,), *args, **kwargs),
])
else:
bias_initializer = self.bias_initializer
self.bias = self.add_weight(shape=(self.memdim * 3,),
name='bias',
initializer=bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias = None
self.kernel_i = self.kernel[:, :self.memdim]
self.kernel_f = self.kernel[:, self.memdim: self.memdim * 2]
self.kernel_o = self.kernel[:, self.memdim * 2:]
self.recurrent_kernel_i = self.recurrent_kernel[:, :self.memdim]
self.recurrent_kernel_f = self.recurrent_kernel[:, self.memdim: self.memdim * 2]
self.recurrent_kernel_o = self.recurrent_kernel[:, self.memdim * 2:]
if self.use_bias:
self.bias_i = self.bias[:self.memdim]
self.bias_f = self.bias[self.memdim: self.memdim * 2]
self.bias_o = self.bias[self.memdim * 2:]
else:
self.bias_i = None
self.bias_f = None
self.bias_o = None
self.built = True