def __get_layer(self, layer_num):
layer_size = self.layer_sizes[layer_num]
layer = None
lstm = self.layer_types[layer_num] == "lstm"
if lstm:
if self.first_layer:
self.first_layer = False
if self.multilayer:
layer = LSTM(
layer_size,
return_sequences=True,
input_shape=(None, self.input_size),
bias_initializer='ones',
kernel_initializer=initializers.random_uniform(
minval=-1.5, maxval=1.5))
else:
layer = LSTM(
layer_size,
input_shape=(1, self.input_size),
bias_initializer='ones',
kernel_initializer=initializers.random_uniform(
minval=-1.5, maxval=1.5))
elif not self.last_layer:
self.last_layer == (layer_num + 1) == self.layers
layer = LSTM(layer_size,
return_sequences=True,
bias_initializer='ones',
kernel_initializer=initializers.random_uniform(
minval=-1.5, maxval=1.5))
else:
print("no layer match.")
return layer
def build(self, input_shape):
# kernel.shape was W_shape
# kernel_shape = (150, 450, 1, 1)
print(self.kernel_size)
#kernel_shape = (3, 3, 1, 1)
kernel_shape = self.kernel_size + (self.num_channels, self.num_channels)
# TODO: fiddle with dimensions or better make them automatic/parrams
self.kernel = self.add_weight(name='kernel',
shape=kernel_shape,
initializer=initializers.random_uniform(minval=-1.0, maxval=1.0),
trainable=True)
self.mask = np.zeros(self.kernel.shape) # W_shape must be inherit from Conv2D or layer
assert self.mask.shape[0] == self.mask.shape[1] # assert that mask height = width
filter_size = self.mask.shape[0]
filter_center = filter_size/2
# unmask everything before the center
self.mask[:math.floor(filter_center)] = 1 # unmask rows above
self.mask[:math.ceil(filter_center), :math.floor(filter_center)] = 1 # unmask cols to the left in same row
if not self.mask_current:
self.mask[math.ceil(filter_center), math.ceil(filter_center)] = 1
self.mask = K.variable(self.mask)
self.bias = None
self.built = True
def get_weight_initializer(initializer=None, seed=None):
if initializer is None:
return initializers.he_normal()
elif initializer == "lstm":
return initializers.random_uniform(minval=-0.1, maxval=0.1)
else:
return initializer()
def __init__(self,
observation,
action,
state_size,
action_size,
seed,
fcs1_units=256,
fc2_units=256,
fc3_units=128):
model = Sequential()
lim1 = 1. / np.sqrt(state_size)
lim2 = 1. / np.sqrt(fcs1_units + action_size)
lim3 = 1. / np.sqrt(fc2_units)
model.add(
Dense(fcs1_units,
activation='relu',
input_shape=(state_size, ),
kernel_initializer=initializers.random_uniform(minval=-lim1,
maxval=lim1,
seed=seed)))
ac = Input((action_size, ))
#print(model.type)
out = Concatenate([model, ac])
model.add(
Dense(fc2_units,
activation='relu',
input_shape=(fcs1_units + action_size, ),
kernel_initializer=initializers.random_uniform(minval=-lim2,
maxval=lim2,
seed=seed)))
model.add(
Dense(fc3_units,
activation='relu',
input_shape=(fc2_units, ),
kernel_initializer=initializers.random_uniform(minval=-lim3,
maxval=lim3,
seed=seed)))
model.add(
Dense(1,
activation='tanh',
input_shape=(fc3_units, ),
kernel_initializer=initializers.random_uniform(minval=-3e-3,
maxval=3e-3,
seed=seed)))
self.model = Model(inputs=[action, observation], outputs=model)
def _make_prd_deepleaning_model(self, n_hidden=1, n_unit=5, keep_drop=1.0):
"""ディープラーニング回帰モデル作成"""
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.advanced_activations import PReLU
from keras.initializers import random_uniform
# 四捨五入関数設定
round = lambda x: (x * 2 + 1) // 2
"""ディープラーニングモデル設定"""
estimator = Sequential()
estimator.add(Dense(n_unit, input_dim=self.X_train.shape[1],
kernel_initializer=random_uniform(seed=0), bias_initializer='zeros'))
# estimator.add(BatchNormalization())
estimator.add(PReLU())
estimator.add(Dropout(keep_drop))
# 隠れ層を下るごとに減らすユニット数
n_minus_of_unit = 0
if n_hidden != 0:
n_minus_of_unit = n_unit / n_hidden
if n_minus_of_unit == 0:
n_minus_of_unit = 1
for n in range(n_hidden):
estimator.add(Dense(n_unit, kernel_initializer=random_uniform(seed=0),
bias_initializer='zeros'))
estimator.add(PReLU())
# estimator.add(ActivityRegularization(l1=0.01, l2=0.01))
# estimator.add(BatchNormalization())
estimator.add(Dropout(keep_drop))
"""次階層で使用するユニット数を減らす"""
n_unit = int(round(n_unit - n_minus_of_unit))
if n_unit < 1:
n_unit = 1
estimator.add(Dense(units=1, kernel_initializer=random_uniform(seed=0), bias_initializer='zeros'))
# estimator.add(BatchNormalization())
estimator.add(Activation('linear'))
estimator.compile(loss='mean_squared_error', optimizer='adam')
return estimator
def __init__(self, state_size, action_size, seed, fc_units=256):
model = Sequential()
lim = 1. / np.sqrt(state_size)
model.add(
Dense(fc_units,
activation='relu',
input_shape=(state_size, ),
kernel_initializer=initializers.random_uniform(minval=-lim,
maxval=lim,
seed=seed)))
model.add(
Dense(action_size,
activation='tanh',
input_shape=(fc_units, ),
kernel_initializer=initializers.random_uniform(minval=-3e-3,
maxval=3e-3,
seed=seed)))
self.model = model
def __init__(self, num_capsule, dim_vector, num_routing, **kwargs):
self.num_capsule = num_capsule
self.dim_vector = dim_vector
self.num_routing = num_routing
#self.kernel_initializer = initializers.get('glorot_uniform')
self.kernel_initializer = initializers.random_uniform(
-1, 1) # With too small weights loss will be nan
super(CapsuleLayer, self).__init__(**kwargs)
def build(self, input_shape):
assert len(input_shape) == 2
input_dim = input_shape[1]
self.gamma_elm = self.add_weight(
name='gamma_elm',
shape=(1, ),
initializer=initializers.random_uniform(-2, -1))
super(GaussianKernel2,
self).build(input_shape) # Be sure to call this somewhere!
def build(self, input_shape):
self.kernel = self.add_weight(name='kernel',
shape=(self.kernel_size, input_shape[-1], self.filters),
initializer=initializers.random_uniform(seed=self.seed),
trainable=True)
self.bias = self.add_weight(name='bias',
shape=(self.filters,),
initializer=initializers.Zeros(),
trainable=True)
super(ConvLayer, self).build(input_shape)
def build_network(self):
with tf.variable_scope(self.name):
self.input = tf.placeholder(tf.float32,
shape=[None, *self.input_dimensions],
name='inputs')
self.action_gradient = tf.placeholder(
tf.float32, shape=[None, self.action_numbers])
f1 = 1 / np.sqrt(self.function1_dimensions)
dense1 = tf.layers.dense(self.input,
units=self.function1_dimensions,
kernel_initializer=random_uniform(
-f1, f1),
bias_initializer=random_uniform(-f1, f1))
batch1 = tf.layers.batch_normalization(dense1)
layer1_activation = tf.nn.relu(batch1)
f2 = 1 / np.sqrt(self.function2_dimensions)
dense2 = tf.layers.dense(layer1_activation,
units=self.function2_dimensions,
kernel_initializer=random_uniform(
-f2, f2),
bias_initializer=random_uniform(-f2, f2))
batch2 = tf.layers.batch_normalization(dense2)
f3 = 0.003
mu = tf.layers.dense(layer1_activation,
units=self.action_numbers,
activation='tanh',
kernel_initializer=random_uniform(-f3, f3),
bias_initializer=random_uniform(-f3, f3))
self.mu = tf.multiply(mu, self.action_bound)
def build_model(self):
"""Build an actor (policy) network that maps states -> actions."""
# Define input layer (states)
states = layers.Input(shape=(self.state_size, ), name='states')
# Add hidden layers
net = layers.Dense(units=256, activation=None)(states)
net = layers.BatchNormalization()(net)
net = layers.Activation('relu')(net)
net = layers.Dense(units=256, activation=None)(net)
net = layers.BatchNormalization()(net)
net = layers.Activation('relu')(net)
raw_actions = layers.Dense(
units=self.action_size,
activation='sigmoid',
kernel_initializer=initializers.random_uniform(minval=-1e-3,
maxval=1e-3),
kernel_regularizer=regularizers.l2(0.02),
name='raw_actions')(net)
# Scale [0, 1] output for each action dimension to proper range
actions = layers.Lambda(lambda x:
(x * self.action_range) + self.action_low,
name='actions')(raw_actions)
# Create Keras model
self.model = models.Model(inputs=states, outputs=actions)
# Define loss function using action value (Q value) gradients
action_gradients = layers.Input(shape=(self.action_size, ))
loss = K.mean(-action_gradients * actions)
# Incorporate any additional losses here (e.g. from regularizers)
# Define optimizer and training function
optimizer = optimizers.Adam(lr=0.00001)
updates_op = optimizer.get_updates(params=self.model.trainable_weights,
loss=loss)
self.train_fn = K.function(
inputs=[self.model.input, action_gradients,
K.learning_phase()],
outputs=[],
updates=updates_op)
def agent_init():
optimizer_map = {
'Adam': Adam(lr=a_globs.ALPHA),
'RMSprop': RMSprop(lr=a_globs.ALPHA),
'Adagrad': Adagrad(lr=a_globs.ALPHA),
'SGD': SGD(lr=a_globs.ALPHA)
}
initializer_map = {
'random': random_uniform(),
'glorot': glorot_normal(),
'he': he_normal()
}
a_globs.cur_epsilon = a_globs.EPSILON
#The main buffer contains all of the sub buffers used to store different types of states, to support biased sampling
a_globs.generic_buffer = []
a_globs.buffer_container = [a_globs.generic_buffer]
#Initialize the neural network
a_globs.model = Sequential()
init_weights = initializer_map[a_globs.INIT]
a_globs.model.add(
Dense(a_globs.NUM_NERONS_LAYER_1,
activation='relu',
kernel_initializer=init_weights,
input_shape=(a_globs.FEATURE_VECTOR_SIZE, )))
a_globs.model.add(
Dense(a_globs.NUM_NERONS_LAYER_2,
activation='relu',
kernel_initializer=init_weights))
a_globs.model.add(
Dense(a_globs.NUM_ACTIONS,
activation='linear',
kernel_initializer=init_weights))
a_globs.model.compile(loss='mse',
optimizer=optimizer_map[a_globs.OPTIMIZER])
summarize_model(a_globs.model, a_globs.AGENT)
#Create the target network
a_globs.target_network = clone_model(a_globs.model)
a_globs.target_network.set_weights(a_globs.model.get_weights())
def build_model(self):
"""Build a critic (value) network that maps (state, action) pairs -> Q-values."""
# Define input layers
states = layers.Input(shape=(self.state_size, ), name='states')
actions = layers.Input(shape=(self.action_size, ), name='actions')
# Add hidden layer(s) for state pathway
net_states = layers.Dense(units=32, activation='relu')(states)
net_states = layers.Dense(units=64, activation='relu')(net_states)
# Add hidden layer(s) for action pathway
net_actions = layers.Dense(units=32, activation='relu')(actions)
net_actions = layers.Dense(units=64, activation='relu')(net_actions)
# Try different layer sizes, activations, add batch normalization, regularizers, etc.
# Combine state and action pathways
net = layers.Add()([net_states, net_actions])
net = layers.Activation('relu')(net)
# Add more layers to the combined network if needed
net = layers.normalization.BatchNormalization()(net)
# Add final output layer to prduce action values (Q values)
Q_values = layers.Dense(units=1,
name='q_values',
kernel_initializer=initializers.random_uniform(
minval=-0.0003, maxval=0.0003))(net)
# Create Keras model
self.model = models.Model(inputs=[states, actions], outputs=Q_values)
# Define optimizer and compile model for training with built-in loss function
optimizer = optimizers.Adam(lr=0.001)
self.model.compile(optimizer=optimizer, loss='mse')
# Compute action gradients (derivative of Q values w.r.t. to actions)
action_gradients = K.gradients(Q_values, actions)
# Define an additional function to fetch action gradients (to be used by actor model)
self.get_action_gradients = K.function(
inputs=[*self.model.input, K.learning_phase()],
outputs=action_gradients)
def __init__(self,
filters,
kernel_size,
sum_constant=1.0,
max_constant=0.5,
strides=(1, 1),
padding='valid',
data_format=None,
dilation_rate=(1, 1),
activation=None,
use_bias=False,
kernel_initializer=initializers.random_uniform(minval=0.0,
maxval=1.0),
bias_initializer='zeros',
kernel_regularizer=regularizers.l1(0.01),
bias_regularizer=None,
activity_regularizer=None,
bias_constraint=None,
**kwargs):
super(Cooc2D,
self).__init__(rank=2,
filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
dilation_rate=dilation_rate,
activation=activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
kernel_constraint=SumNorm(sum_const=sum_constant,
max_const=max_constant,
axis=[0, 1, 2]),
bias_constraint=bias_constraint,
**kwargs)
self.input_spec = InputSpec(ndim=4)
def build_model(self):
#Input layers
states = layers.Input(shape=(self.state_size, ), name='states')
actions = layers.Input(shape=(self.action_size, ), name='actions')
#Hidden layers for states
h_states = layers.Dense(units=32, activation='relu')(states)
h_states = layers.Dense(units=64, activation='relu')(h_states)
#Hidden layers for actions
h_actions = layers.Dense(units=32, activation='relu')(actions)
h_actions = layers.Dense(units=64, activation='relu')(h_actions)
#Combine states and actions
network = layers.Add()([h_states, h_actions])
network = layers.Activation('relu')(network)
#Add batch normlization layer
network = layers.normalization.BatchNormalization()(network)
#Add output layer to produce action values
Q_values = layers.Dense(units=1,
name='q_values',
kernel_initializer=initializers.random_uniform(
minval=-0.0005, maxval=0.0005))(network)
#Create Model
self.model = models.Model(inputs=[states, actions], outputs=Q_values)
#Compile Model
self.model.compile(optimizer=optimizers.Adam(lr=0.001), loss='mse')
#compute action gradients
action_gradients = kb.gradients(Q_values, actions)
self.get_action_gradients = kb.function(
inputs=[*self.model.input, kb.learning_phase()],
outputs=action_gradients)
def build_network(self):
with tf.variable_scope(self.name):
self.input = tf.placeholder(tf.float32,
shape=[None, *self.input_dimensions],
name='inputs')
self.actions = tf.placeholder(tf.float32,
shape=[None, self.action_numbers],
name='actions')
self.q_target = tf.placeholder(tf.float32,
shape=[None, 1],
name='targets')
f1 = 1 / np.sqrt(self.function1_dimensions)
dense1 = tf.layers.dense(self.input,
units=self.function1_dimensions,
kernel_initializer=random_uniform(
-f1, f1),
bias_initializer=random_uniform(-f1, f1))
batch1 = tf.layers.batch_normalization(dense1)
layer1_activation = tf.nn.relu(batch1)
f2 = 1 / np.sqrt(self.function2_dimensions)
dense2 = tf.layers.dense(layer1_activation,
units=self.function2_dimensions,
kernel_initializer=random_uniform(
-f2, f2),
bias_initializer=random_uniform(-f2, f2))
batch2 = tf.layers.batch_normalization(dense2)
action_in = tf.layers.dense(self.actions,
units=self.function2_dimensions,
activation='relu')
state_actions = tf.add(batch2, action_in)
state_actions = tf.nn.relu(state_actions)
f3 = 0.003
self.q = tf.layers.dense(
state_actions,
units=1,
kernal_initializer=random_uniform(-f3, f3),
bias_initializer=random_uniform(-f3, f3),
kernel_regularizer=tf.keras.regularizers.l2(0.01))
self.loss = tf.losses.mean_squared_error(self.q_target, self.q)
def create_model(optimizer=Adam(lr=0.01),
kernel_initializer=random_uniform(seed=12),
dropout=0.1,
TIME_PERIODS=24,
num_channels=65):
model = Sequential()
model.add(
Conv1D(32,
3,
activation='relu',
input_shape=(TIME_PERIODS, num_channels),
padding='same',
strides=1))
model.add(Conv1D(32, 3, activation='relu', padding='same', strides=1))
model.add(Flatten())
model.add(Dropout(0.1))
model.add(
Dense(16, activation='relu', kernel_initializer=kernel_initializer))
model.add(Dropout(dropout))
model.add(Dense(1, kernel_initializer=kernel_initializer))
model.compile(loss=huber_loss, optimizer=optimizer, metrics=['mae'])
return model
def build_model(X, y, width, depth, epochs, select):
model = Sequential()
if select is 0:
activ = 'relu'
init = initializers.he_normal(seed=None)
elif select is 1:
activ = 'tanh'
init = initializers.glorot_normal(seed=None)
elif select is 2:
activ = 'sigmoid'
init = initializers.random_uniform(seed=None)
model.add(Dense(width, input_dim=X.shape[1], activation=activ, kernel_initializer=init))
for i in range(1, depth):
model.add(Dense(width, activation=activ, kernel_initializer=init))
model.add(Dense(1, activation='sigmoid', kernel_initializer=init))
model.compile(optimizer='adam',
loss = 'binary_crossentropy',
metrics = ['accuracy'])
return model.fit(X, y, epochs=epochs), model
def train_tiny_imagenet(hardware='cpu', batch_size=100, num_epochs=25,
num_classes=10, lr=0.001, decay=0.00, wnids='',
resize='False', load='', normalize='False'):
# Load data
x_train, y_train, x_val, y_val, wnids_path = process_images(wnids, resize, num_classes, normalize)
# Choose seleted hardware, default to CPU
if hardware == 'gpu':
devices = ['/gpu:0']
elif hardware == '2gpu':
devices = ['/gpu:0', '/gpu:1']
else:
devices = ['/cpu:0']
# Run on chosen processors
for d in devices:
with tf.device(d):
# Load saved model and check its accuracy if optional arg passed
if load != '':
model = load_model(load)
# Run validation set through loaded network
score = model.evaluate(x_val, y_val, batch_size=batch_size)
#print("%s: %.2f%%" % (model.metrics_names[1], score[1]*100))
return str(score[1]*100)
# Otherwise train new network
else:
model = Sequential()
"""Block 1"""
model.add(Conv2D(32, (5, 5), strides=(1,1), padding='same',
kernel_initializer=initializers.random_uniform(minval=-0.01, maxval=0.01),
bias_initializer='zeros',
input_shape=x_train.shape[1:]))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2), padding='same'))
model.add(Activation('relu'))
"""Block 2"""
model.add(Conv2D(32, (5, 5), strides=(1,1), padding='same',
kernel_initializer=initializers.random_uniform(minval=-0.05, maxval=0.05),
bias_initializer='zeros'))
model.add(Activation('relu'))
model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2), padding='same'))
"""Block 3"""
model.add(Conv2D(64, (5, 5), strides=(1,1), padding='same',
kernel_initializer=initializers.random_uniform(minval=-0.05, maxval=0.05),
bias_initializer='zeros'))
model.add(Activation('relu'))
model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2), padding='same'))
"""Fully Connected Layer and ReLU"""
model.add(Flatten())
model.add(Activation('relu'))
"""Output Layer"""
model.add(Dense(num_classes,
kernel_initializer=initializers.random_uniform(minval=-0.05, maxval=0.05),
bias_initializer='zeros'))
"""Loss Layer"""
model.add(Activation('softmax'))
"""Optimizer"""
model.compile(loss=losses.categorical_crossentropy,
optimizer=optimizers.adam(lr=lr, decay=decay),
metrics=['accuracy'])
# check model checkpointing callback which saves only the "best" network according to the 'criteria' optional argument
sets_index = wnids_path.find('sets')
outpath = os.path.join(train_path, wnids_path[sets_index:])
# Naming file by hyperparameters
resize = resize.title()
normalize = normalize.title()
prefix = '%d_%d_%d_%.5f_%.2f_%s_%s_best_%s_' % (batch_size, num_epochs, num_classes, lr, decay, resize, normalize, criteria)
model_outfile = os.path.join(outpath, prefix + 'model.hdf5')
csv_outfile = os.path.join(outpath, prefix + 'log.csv')
if not os.path.exists(outpath):
os.makedirs(outpath)
# Save network state from the best <criteria> of all epochs ran
model_checkpoint = ModelCheckpoint(model_outfile, monitor=criteria, save_best_only=True)
# Log information from each epoch to a csv file
logger = CSVLogger(csv_outfile)
callback_list = [model_checkpoint, logger]
if not data_augmentation:
print('Not using data augmentation.')
# Use the defined 'model_checkpoint' callback
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=num_epochs,
validation_data=(x_val, y_val),
shuffle=True,
callbacks=callback_list)
else:
print('Using real-time data augmentation.')
# This will do preprocessing and realtime data augmentation:
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
# Compute quantities required for feature-wise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(x_train)
# Fit the model on the batches generated by datagen.flow().
# Use the defined 'model_checkpoint' callback
model.fit_generator(datagen.flow(x_train, y_train,
batch_size=batch_size),
steps_per_epoch=x_train.shape[0] // batch_size,
epochs=num_epochs,
validation_data=(x_val, y_val),
callbacks=callbacks)
return 'New network trained!'
from keras import optimizers
from keras import initializers
import numpy as np
from numpy import genfromtxt
import matplotlib.pyplot as plt
import math
number_input = 64
number_classes = 10
hidden_layers = 1
index_layer = 0
neurons_hidden = 53
funct_activation = 'relu'
funct_activation_output = 'softmax'
initializer_kernel = initializers.random_uniform()
initializer_bias = 'ones'
learning_rate = 0.005
loss_function = 'categorical_crossentropy'
net_metrics = ['accuracy']
epochs_number = 4
validation_split = 3
##################READ DATABASE - TRAIN#####################
train_read = genfromtxt('dataset/train.csv', delimiter=',')
tmp_train_data = np.array([])
tmp_train_label = np.array([])
for i in range(0, train_read.shape[0]):
last = train_read[i][-1]
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),
id="ru_0",
),
pytest.param(
initializers.random_uniform(minval=-0.2, seed=42),
dict(class_name="random_uniform", minval=-0.2, maxval=0.05, seed=42),
id="ru_1",
),
pytest.param(
initializers.TruncatedNormal(0.1),
dict(class_name="truncated_normal", mean=0.1, stddev=0.05, seed=None),
id="tn_0",
),
pytest.param(
initializers.truncated_normal(mean=0.2, stddev=0.003, seed=42),
dict(class_name="truncated_normal", mean=0.2, stddev=0.003, seed=42),
id="tn_1",
),
pytest.param(
initializers.Orthogonal(1.1),
# Feature Scaling
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Reshape data in 3 dimensions (height width, canal = 1)
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
#print("X_train shape:", X_train.shape[0:3])
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)
#print("X_test shape:", X_test.shape[0:3])
#hyperparameters
input_dimension = 30
learning_rate = 0.0025
momentum = 0.85
hidden_initializer = random_uniform(seed=SEED)
dropout_rate = 0.15
kernel_initializer = 'uniform'
kernel_regularizer = regularizers.l2(0.0001)
# define path to save model
model_path = 'fm_cnn_BN.h5'
#CNN modele defini dans l'article
def create_model():
conv = Sequential()
conv.add(
Conv1D(nb_filter=15,
filter_length=3,
from keras.layers import Input, Dense, Lambda, concatenate
from keras.constraints import non_neg, unit_norm
from keras.models import Model
from keras.initializers import random_uniform
RND_UNI = random_uniform(minval=0.05, maxval=1.05)
class EAR(object):
def __init__(self, args):
super(EAR, self).__init__()
self.look_back = args['look_back']
def make_model(self):
x = Input(shape=(self.look_back, ))
ar_output = Dense(units=1,
kernel_initializer='uniform',
kernel_constraint=unit_norm(),
name='ar-weights')(x)
pre_point = Lambda(lambda k: k[:, -1:])(x)
merged_output = concatenate([ar_output, pre_point])
outputs = Dense(units=1,
kernel_initializer=RND_UNI,
use_bias=False,
kernel_constraint=non_neg(),
name='contrib-weights')(merged_output)
def get_initializer(self, min_val, max_val, inputs=10):
if Config.WEIGHT_INITIALIZER == "uniform":
return random_uniform(min_val, max_val)
if Config.WEIGHT_INITIALIZER == "normal":
return random_normal(min_val, 1 /
np.sqrt(inputs)) # Stddev = 1/sqrt(inputs)
def create_model():
initializer = initializers.random_uniform(minval=0.00,
maxval=1.00,
seed=None)
model = Sequential()
model.add(
Conv2D(filters=32,
kernel_size=(2, 2),
padding='same',
input_shape=(1, 25, 25),
kernel_initializer=initializer,
bias_initializer='zero',
kernel_regularizer=regularizers.l2(0.001),
activity_regularizer=regularizers.l2(0.001)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(
Conv2D(filters=32,
kernel_size=(2, 2),
padding='same',
kernel_regularizer=regularizers.l2(0.001),
activity_regularizer=regularizers.l2(0.001)))
model.add(BatchNormalization())
model.add(Activation('relu'))
# model.add(Dropout(0.25))
# model.add(MaxPooling2D((2, 2), padding='same'))
#
# model.add(Conv2D(filters=32, kernel_size=(2, 2), padding='same',
# kernel_regularizer=regularizers.l2(0.001),
# activity_regularizer=regularizers.l2(0.001)
# ))
# model.add(BatchNormalization())
# model.add(Activation('relu'))
#
# model.add(Conv2D(filters=32, kernel_size=(2, 2), padding='same',
# kernel_regularizer=regularizers.l2(0.001),
# activity_regularizer=regularizers.l2(0.001)
# ))
# model.add(BatchNormalization())
# model.add(Activation('relu'))
# # model.add(Dropout(0.25))
#
# model.add(MaxPooling2D((2, 2), padding='same'))
model.add(Flatten())
# model.add(Dropout(0.25))
model.add(
Dense(256,
activation='relu',
kernel_regularizer=regularizers.l2(0.001),
activity_regularizer=regularizers.l2(0.001)))
# model.add(Dropout(0.25))
model.add(
Dense(64,
activation='relu',
kernel_regularizer=regularizers.l2(0.001),
activity_regularizer=regularizers.l2(0.001)))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=optimizers.adam(lr=0.001),
metrics=['accuracy'])
return model
def __init__(self,
model=None,
valorDama=None,
listaSigmas=None,
geracao=0,
genealogia=[],
debug=False):
self.listaSigmas = []
listaWeights = []
self.currentPoints = 0
self.totalPoints = 0
self.numeroDeGeracoesVivo = 1
self.geracao = geracao
if (valorDama is None):
self.valorDama = 2.0
else:
self.valorDama = valorDama
if (model is None):
initializer = initializers.random_uniform(minval=(-1) *
Jogador.peakVal,
maxval=Jogador.peakVal)
self.model = Sequential()
self.model.add(
Dense(40,
input_dim=32,
kernel_initializer=initializer,
activation='tanh'))
# self.model.add (Dense (40, kernel_initializer=initializer, activation = 'tanh'))
self.model.add(
Dense(10, kernel_initializer=initializer, activation='tanh'))
self.model.add(
Dense(1, kernel_initializer=initializer, activation='tanh'))
self.model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
self.model._make_predict_function()
else:
self.model = model
self.model._make_predict_function()
if (listaSigmas is None):
for layerIndex in range(self.numeroDeLayers):
layer = self.model.get_layer(index=layerIndex)
layerWeights = layer.get_weights()
arrayWeights = np.zeros(
(layerWeights[0].shape[0],
layerWeights[0].shape[1])) + self.sigma
arrayBiases = np.zeros(layerWeights[1].shape[0]) + self.sigma
listaWeights.clear()
listaWeights.append(arrayWeights)
listaWeights.append(arrayBiases)
self.listaSigmas.append(copy.deepcopy(listaWeights))
else:
self.listaSigmas = listaSigmas
self.nomeJogador = "Jogador_" + str(uuid.uuid4()) + ".h5"
self.genealogia = copy.deepcopy(genealogia)
listaNomeJogador = []
listaNomeJogador.append(self.nomeJogador)
listaGeracaoJogador = []
listaGeracaoJogador.append(self.geracao)
listaGeral = []
listaGeral.append(listaNomeJogador)
listaGeral.append(listaGeracaoJogador)
self.genealogia.append(listaGeral)
self.debug = debug
def keras_cnn_1d(self, data):
"""
Description:
setup Keras hyper_parameters, Kernel_initializer 's parameter and find regression_value
for (multiple) label(s)
:param data: pandas.core.frame.DataFrame
:return:
Reference:
https://keras.io/optimizers/
https://keras.io/initializers/
https://keras.io/layers/core/#dropout
:return:
"""
# Convert to the right dimension row, column, 1-channel in Numpy array
X_data = data.drop(self.dependent_label, axis=1)
Y_data = data[self.dependent_label]
# Normalizing data
scaler = MinMaxScaler()
scaler.fit(X_data)
X_data = pd.DataFrame(data=scaler.transform(X_data), columns=X_data.columns, index=X_data.index)
# Split data into training_data and evaluation_data
x_train, x_test, y_train, y_test = train_test_split(X_data, Y_data, test_size=0.2, random_state=101)
# Number of feature columns in training_data
input_dimension = len(x_train.columns) # input_dimension = self.input_units
x_train = x_train.values.reshape(x_train.shape[0], x_train.shape[1], 1)
y_train = y_train.as_matrix()
# Init hyper parameter
np.random.seed(self.seed)
hidden_initializer = random_uniform(seed=self.seed)
# self.n_filters = 32 # reset number of filters (default is 32)
# Create model and add hidden layers
model = Sequential()
# Conv1D needs 2 dimension -> input_shape=(input_dimension, 1), similarly Conv2D needs 3 dim, Conv3D needs 4 dim
# Output dim has the same size as Input dim(default is 'same')
model.add(Conv1D(input_shape=(input_dimension, 1), activation=self.activation_fn, filters=self.n_filters,
kernel_size=self.kernel_size, padding='same'))
model.add(Conv1D(activation=self.activation_fn, filters=self.n_filters, kernel_size=self.kernel_size))
# Flattens the input
model.add(Flatten())
# Regularization technique to prevent Neural Networks from Overfitting
model.add(Dropout(rate=self.drop_out_rate))
# Use output of CNN as input of ANN, e.g. units = np.array([1000, 500, 250, 3])
model.add(Dense(units=self.input_units, input_dim=input_dimension, kernel_initializer=hidden_initializer,
activation=self.activation_fn))
# Hidden layers, number of layers and neurons in every layers (in numpy array e.g. [1000, 500, 250, 3])
for unit in self.hidden_units:
model.add(Dense(units=unit, kernel_initializer=hidden_initializer, activation=self.activation_fn))
# Output signal = output_units
# output_activation_fn = 'softmax', no need activation in this case?
model.add(Dense(units=self.output_units, kernel_initializer=hidden_initializer))
# reset optimizer
sgd = SGD(lr=self.learning_rate, momentum=self.momentum, decay=self.decay_rate)
self.optimizer = sgd
# compile and train model with training_data
model.compile(loss=self.loss, optimizer=self.optimizer, metrics=['accuracy'])
model.fit(x_train, y_train, epochs=self.nr_epochs, batch_size=self.batch_size)
# Evaluate the model on the training and testing set
scores = model.evaluate(x_train, y_train, verbose=0)
print("\nTraining Accuracy: Model %s: Scores: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
scores = model.evaluate(x_test, y_test, verbose=0)
print("\nTesting Accuracy: Model %s: Scores: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
# Predict value
y_pred = pd.DataFrame(data=model.predict(x_test.values.reshape(x_test.shape[0], x_test.shape[1], 1)),
columns=y_test.columns)
return model, y_test, y_pred
def create_actor_network(self, state_size, action_size):
print('now we build the actor model')
# left_branch = Sequential()
# left_branch.add(Dense(300,input_dim=state_size,activation='relu'))
#
# right_branch = Sequential()
# right_branch.add(Dense(300,input_dim=fenli_size,activation='relu'))
#
# merged = Merge([left_branch,right_branch],mode='concat')
#
# model = Sequential()
# model.add(merged)
# model.add(Dense(300,activation='relu'))
# model.add(BatchNormalization())
# model.add(Dropout(0.5))
# model.add(Dense(action_size,activation='softmax'))
# adam = Adam(lr=self.LEARNING_RATE)
# model.compile(loss='mse',optimizer=adam)
# model = Sequential()
# model.add(Dense(600, input_dim=state_size, init='uniform'))
# model.add(Activation('sigmoid'))
# model.add(Dense(300,init='uniform'))
# model.add(Activation('relu'))
# model.add(Dense(action_size, init='uniform'))
# model.add(Activation('sigmoid'))
# S = Input(shape=[state_size])
# h0 = Dense(16, init='uniform', activation='tanh')(S)
# h1 = Dense(16, init='uniform', activation='tanh')(h0)
# h2 = Dense(16, init='uniform', activation='tanh')(h1)
# V = Dense(action_size,activation='sigmoid')(h2)
# model = Model(input=S,output=V)
# S = Input(shape=[state_size])
# # x = Embedding(input_dim=state_size,output_dim=1024,input_length=257,init='normal')(S)
# x1 = LSTM(output_dim=512,input_shape=(4,257), activation='tanh')(S)
# x2 = LSTM(output_dim=512,activation='sigmoid',init='normal')(x1)
# x3 = Dense(action_size,activation='sigmoid')(x2)
# model = Model(input=S, output=x3)
S = Input(shape=[3, state_size // 3])
bl1 = Bidirectional(
LSTM(100,
return_sequences=True,
input_shape=(3, 257),
kernel_initializer=initializers.random_uniform(minval=-0.5,
maxval=0)))(S)
# bl1 = Bidirectional(LSTM(100, return_sequences=True, input_shape=(3, 257), kernel_initializer='lecun_uniform'))(S)
# kernel_initializer='random_uniform'
bl2 = Bidirectional(
LSTM(100,
return_sequences=False,
input_shape=(3, 257),
kernel_initializer=initializers.random_uniform(
minval=0, maxval=0.5)))(bl1)
# bl2 = Bidirectional(LSTM(100, return_sequences=False, input_shape=(3, 257), kernel_initializer='lecun_uniform'))(bl1)
# bl2 = BatchNormalization(beta_initializer='random_uniform',
# gamma_initializer='random_uniform',
# moving_mean_initializer='random_uniform',
# moving_variance_initializer='random_uniform')(bl2)
# output_mask_layer = Dense(action_size, kernel_initializer='lecun_uniform')(bl2)
output_mask_layer = Dense(
action_size,
kernel_initializer=initializers.random_normal(-1, 0.5))(bl2)
# kernel_initializer=initializers.random_normal(stddev=0.05)
# output_mask_layer = BatchNormalization()(output_mask_layer)
output_mask_layer = Activation('sigmoid')(output_mask_layer)
model = Model(input=S, output=output_mask_layer)
return model, model.trainable_weights, S
def keras_sequential(self, data, output_signals=1):
"""
Description: setup Keras and run the Sequential method to predict value
References:
https://keras.io/getting-started/sequential-model-guide/
https://stackoverflow.com/questions/44747343/keras-input-explanation-input-shape-units-batch-size-dim-etc#
:param data: pandas.core.frame.DataFrame
:param output_signals: default 1 (when there's only 1 categorical column)
:return: Keras-Sequential object, the actual (true) value, the predicted value
"""
# split data
x_train, x_test, y_train, y_test = StartMod.split_data(data, dependent_label=self.dependent_label)
# Initialising the ANN
model = Sequential() # model = models.Sequential()
# tbd: use parameter tuning to find the exact number of nodes in the hidden-layer
# Number of layers and neurons in every layers (in numpy array e.g. [1000, 500, 250, 3])
hidden_units = self.hidden_units
# Init hyper parameter
# np.random.seed(10)
hidden_initializer = random_uniform(seed=self.seed) # initializers.RandomUniform(seed=self.seed)
input_dimension = self.input_units
# default = number of features, input_signals = x_train.shape[1] = hidden_units[0]
# Adding the input layer and the first hidden layer, activation function as rectifier function
model.add(Dense(units=self.input_units, input_dim=input_dimension, kernel_initializer=hidden_initializer,
activation=self.activation_fn, bias_initializer=self.bias_initializer))
# Adding the second hidden layer, activation function as rectifier function
# print(hidden_units)
for i in range(len(hidden_units)):
# print(i, hidden_units[i])
model.add(Dense(activation=self.activation_fn, units=hidden_units[i], kernel_initializer=hidden_initializer))
# Adding the output layer (in case of there's only one dependent_label),
# n_classes = 2 (binary), then activation function = sigmoid
# n_classes > 2 (not binary), then activation function = softmax
if self.n_classes == 2:
output_activation = "sigmoid"
else:
output_activation = "softmax"
model.add(Dense(units=self.output_units, kernel_initializer=hidden_initializer, activation=output_activation))
# Compiling the ANN: optional optimizer='adam', loss='categorical_crossentropy'
model.compile(optimizer=self.optimizer, loss=self.loss, metrics=['accuracy'])
# Fit the keras_model to the training_data and see the real time training of model on data with result of loss
# and accuracy. The smaller batch_size and higher epochs, the better the result. However, slow_computing!
print(x_train.shape, y_train.shape)
model.fit(x_train, y_train, validation_split=0.3, batch_size=self.batch_size, epochs=self.nr_epochs)
# Predictions and evaluating the model
y_pred = model.predict(x_test)
# Evaluate the model
scores, accuracy = model.evaluate(x_train, y_train)
print("\nModel %s: Scores: %.2f%%, Accuracy: %.2f%%" % (model, scores*100, accuracy*100))
# make class predictions with the model
predictions = model.predict_classes(x_test)
# summarize the first 5 cases
for i in range(5):
print('%s => %d (expected %d)' % (x_test[i].tolist(), predictions[i], y_test[i]))
return model, y_test, y_pred