def __create_model(self, load_model=True):
hidden_size = 100
self.model = Sequential()
self.model.add(
Dense(hidden_size,
input_shape=(2, ),
activation='relu',
kernel_regularizer=l1(0.01)))
self.model.add(
Dense(hidden_size, activation='relu', kernel_regularizer=l1(0.01)))
# self.model.add(Dense(hidden_size, activation='relu'))
self.model.add(Dense(self.num_actions))
self.model.compile(sgd(lr=0.0001), "mse")
if load_model and os.path.exists("model.dqlearning1"):
self.model.load_weights("model.dqlearning1")
self.model2 = Sequential()
self.model2.add(
Dense(hidden_size,
input_shape=(2, ),
activation='relu',
kernel_regularizer=l1(0.01)))
self.model2.add(
Dense(hidden_size, activation='relu', kernel_regularizer=l1(0.01)))
# self.model2.add(Dense(hidden_size, activation='relu'))
self.model2.add(Dense(self.num_actions))
self.model2.compile(sgd(lr=0.0001), "mse")
if load_model and os.path.exists("model.dqlearning2"):
self.model.load_weights("model.dqlearning2")
def build_model():
'''
Returns three initialized objects: the model, the environment, and the replay.
'''
model = Sequential()
model.add(
Dense(hidden_size, input_shape=(state_space, ),
activation='relu')) #@jen is that comma supposed to be there?
model.add(Dense(hidden_size, activation='relu'))
model.add(Dense(action_space))
model.compile(sgd(lr=.04, clipvalue=3.0), "mse")
agent_model = Sequential()
agent_model.add(
Dense(hidden_size, input_shape=(state_space, ),
activation='relu')) #@jen is that comma supposed to be there?
agent_model.add(Dense(hidden_size, activation='relu'))
agent_model.add(Dense(action_space))
agent_model.compile(sgd(lr=.04, clipvalue=3.0), "mse")
# Define environment/game
env = World(
agent_model
) #JEN: I'm not sure if I can actually just give the untrained model as a starting model and it
#will correctly act as functionally random...
#--> make this a copy
# Initialize experience replay object
exp_replay = ExperienceReplay(max_memory=max_memory)
return model, agent_model, env, exp_replay
def cnn(shape, no_classes):
"""convolutional neural network model"""
model = Sequential()
model.add(Conv2D(4, 3, padding='same', input_shape=shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(8, 3, padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(100))
model.add(Activation('relu'))
model.add(Dense(20))
model.add(Activation('relu'))
if no_classes > 2:
model.add(Dense(no_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=sgd(lr=0.01),
metrics=['categorical_accuracy'])
else:
model.add(Dense(no_classes - 1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=sgd(lr=0.01),
metrics=['accuracy'])
return model
def build_model():
'''
Returns three initialized objects: the model, the environment, and the replay.
'''
model = Sequential()
model.add(Dense(state_space, input_shape=(state_space,), activation='relu',kernel_regularizer=l2(0.0001)))
model.add(Dense(hidden_size, activation='relu', kernel_regularizer=l2(0.0001)))
model.add(Dense(action_space, kernel_regularizer=l2(0.0001)))
model.compile(sgd(lr=.04, clipvalue = 3.0), "mse")
agent_model = Sequential()
agent_model.add(Dense(state_space, input_shape=(state_space,), activation='relu',kernel_regularizer=l2(0.0001)))
agent_model.add(Dense(hidden_size, activation='relu', kernel_regularizer=l2(0.0001)))
agent_model.add(Dense(action_space, kernel_regularizer=l2(0.0001)))
agent_model.compile(sgd(lr=.04, clipvalue = 3.0), "mse")
# Define environment/game
env = World()
# Initialize experience replay object
exp_replay = ExperienceReplay(max_memory=max_memory)
return model, agent_model, env, exp_replay
def __init__(self, dataset, *args, **kwargs):
super(VGG, self).__init__(*args, **kwargs)
model = VGG16(include_top=False, input_shape=dataset.input_shape)
x = Flatten(name='flatten')(model.output)
x = Dense(1024, activation='relu', name='fc1')(x)
x = Dropout(0.5)(x)
x = Dense(1024, activation='relu', name='fc2')(x)
x = Dropout(0.5)(x)
x = Dense(dataset.output_size,
activation='softmax',
name='predictions')(x)
self.model = Model(inputs=model.input, outputs=x)
opt = optimizers.sgd(lr=0.0001)
self.model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
probabilistic_model = VGG16(include_top=False,
input_shape=dataset.input_shape)
x = Flatten(name='flatten')(probabilistic_model.output)
x = Dense(1024, activation='relu', name='fc1')(x)
x = Dropout(0.5)(x)
x = Dense(1024, activation='relu', name='fc2')(x)
x = Dropout(0.5)(x)
x = Dense(dataset.output_size,
activation='softmax',
name='predictions')(x)
self.probabilistic_model = Model(inputs=probabilistic_model.input,
outputs=x)
opt = optimizers.sgd(lr=0.0001)
self.probabilistic_model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
def __init__(self, dataset, *args, **kwargs):
super(CNN, self).__init__(*args, **kwargs)
model = Sequential()
model.add(
Conv2D(32, (3, 3), padding='same',
input_shape=dataset.input_shape))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(dataset.output_size))
model.add(Activation('softmax'))
# opt = optimizers.rmsprop(lr=0.0001, decay=1e-6)
# opt = optimizers.adam()
opt = optimizers.sgd(lr=0.0001)
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
self.model = model
probabilistic_model = Sequential()
probabilistic_model.add(
Conv2D(32, (3, 3), padding='same',
input_shape=dataset.input_shape))
probabilistic_model.add(Activation('relu'))
probabilistic_model.add(Conv2D(32, (3, 3)))
probabilistic_model.add(Activation('relu'))
probabilistic_model.add(MaxPooling2D(pool_size=(2, 2)))
probabilistic_model.add(BayesianDropout(0.25))
probabilistic_model.add(Conv2D(64, (3, 3), padding='same'))
probabilistic_model.add(Activation('relu'))
probabilistic_model.add(Conv2D(64, (3, 3)))
probabilistic_model.add(Activation('relu'))
probabilistic_model.add(MaxPooling2D(pool_size=(2, 2)))
probabilistic_model.add(BayesianDropout(0.25))
probabilistic_model.add(Flatten())
probabilistic_model.add(Dense(512))
probabilistic_model.add(Activation('relu'))
probabilistic_model.add(BayesianDropout(0.5))
probabilistic_model.add(Dense(dataset.output_size))
probabilistic_model.add(Activation('softmax'))
# opt = optimizers.rmsprop(lr=0.0001, decay=1e-6)
# opt = optimizers.adam()
opt = optimizers.sgd(lr=0.0001)
probabilistic_model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
self.probabilistic_model = probabilistic_model
def tryPerceptron(fName, learningRate, epochs):
data = pd.read_csv("../Data/{}.csv".format(fName))
data.columns = list(range(1, len(data.columns) + 1))
yVals = data.iloc[:, -1] #concrete has 3 outputs,
xVals = data.iloc[:, :-1] # remove those three outputs from x inputs
xVals.insert(loc=0,
column=0,
value=[1 for x in range(0, len(xVals.values))],
allow_duplicates=True)
yVals = pd.DataFrame(preprocessing.scale(yVals.to_numpy().reshape(-1, 1)))
yVals = yVals.iloc[:, -1]
# I FORGOT TO SCALLE
xVals = pd.DataFrame(preprocessing.scale(xVals))
optimizerDict = {
"Adam": optimizers.adam(lr=learningRate),
"SGD": optimizers.sgd(lr=learningRate),
"SGD_Moment": optimizers.sgd(lr=learningRate, momentum=.2)
}
for item in optimizerDict.keys():
for i in range(0, len(activationArray)):
print("\t{} {} {} ".format(item, activationArray[i],
modelsToUse[0]))
#another for loop for models after this
currentParams = {
'model': modelsToUse[0],
'optimizer': optimizerDict[item],
'activation': activationArray[i],
'epochs': epochs,
'lr': learningRate
}
if (item == "SGD" or item
== "SGD_Moment") and activationArray[i] == 'linear':
if item == "SGD_Moment":
currentParams['optimizer'] = optimizers.sgd(
lr=learningRate, momentum=.5, clipnorm=1.)
else:
currentParams['optimizer'] = optimizers.sgd(
lr=learningRate, clipnorm=1.)
myCols, myNumFeat, rVals, rCV, rAdj = GeneralModel.forwardSelectAll(
xVals, yVals, currentParams)
#base case with all features.
print("\t\tColumn Index used:{}".format(myCols))
print("\t\tNumFeatures forwardSelect Chose:{}".format(myNumFeat))
print("\t\t rCVVales: {}".format(rCV))
print("\t\t rBarVaues: {}".format(rAdj))
GeneralModel.graphAttsVRVals(rVals, rCV, rAdj, myNumFeat, item,
currentParams, 0, fName)
def build_model_VAD_M2():
"""
DNN分层（包含age、gender子属性，但不输出），并行学习出AVD三个属性
:return:model
"""
main_input = Input(shape=(1, 6373), name='main_input')
x = Dense(64, activation='sigmoid')(main_input)
x = Dropout(rate=0.5)(x)
x = Flatten()(x)
v = Dense(1, activation='relu', name='v_out')(x)
a = Dense(1, activation='relu', name='a_out')(x)
d = Dense(1, activation='relu', name='d_out')(x)
model = Model(inputs=[main_input], outputs=[v, a, d])
sgd = optimizers.sgd(lr=1e-4)
model.compile(loss={
'v_out': 'mse',
'a_out': 'mse',
'd_out': 'mse'
},
loss_weights={
'v_out': 0.2,
'a_out': 0.6,
'd_out': 0.2
},
optimizer='rmsprop',
metrics=['accuracy'])
model.summary()
return model
def __init__(self, input_dim=0, output_dim=0, lr=0.01):
self.input_dim = input_dim
self.lr = lr
# LSTM 신경망
self.model = Sequential()
self.model.add(
LSTM(256,
input_shape=(1, input_dim),
return_sequences=True,
stateful=False,
dropout=0.5))
self.model.add(BatchNormalization())
self.model.add(
LSTM(256, return_sequences=True, stateful=False, dropout=0.5))
self.model.add(BatchNormalization())
self.model.add(
LSTM(256, return_sequences=False, stateful=False, dropout=0.5))
self.model.add(BatchNormalization())
self.model.add(Dense(output_dim))
self.model.add(Activation('sigmoid'))
self.model.compile(optimizer=sgd(lr=lr), loss='mse')
self.prob = None
def create_model():
nb_filters = 8
nb_conv = 5
model = Sequential()
# add a convolutional model first
model.add(Convolution2D(nb_filters,nb_conv,border_mode='valid',input_shape=(480,640,3)))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(Convolution2D(nb_filters, nb_conv, nb_conv))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('linear')) #regression model, hence linear activation
model.compile(loss='mean_squared_error', optimizer=sgd())
model.summary()
return model
def mlp_binary(x, y, para):
nb_features = x.shape[1]
nb_classes = y.shape[1]
# model
early_stop = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=100,
verbose=1,
mode='auto')
callbacks_list = [early_stop]
model = Sequential(name=para['model_name'])
model.add(Dense(30, input_dim=nb_features, use_bias=True))
model.add(Activation('relu'))
model.add(Dropout(para['drop_rate']))
model.add(Dense(units=nb_classes))
model.add(Activation('softmax'))
model.compile(loss='binary_crossentropy',
optimizer=optimizers.sgd(lr=5e-3, momentum=0.5),
metrics=['accuracy'])
print(model.summary())
history = model.fit(x,
y,
batch_size=para['size_of_batch'],
epochs=para['nb_epoch'],
validation_split=0.33,
shuffle=True,
callbacks=callbacks_list)
return history, model
def getModel(numClasses, train_features, train_labels, validation_features,
validation_labels):
with graph.as_default():
print('getModel()..........numClasses: {}'.format(numClasses))
model = models.Sequential()
model.add(layers.Dense(512, activation='relu', input_dim=1 * 1 * 2048))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(numClasses, activation='softmax'))
model.summary()
model.compile(
loss='categorical_crossentropy',
# optimizer=optimizers.RMSprop(lr=2e-4),
optimizer=optimizers.sgd(),
metrics=['acc'])
early_stopping = EarlyStopping(patience=15,
mode='auto',
monitor='val_loss')
history = model.fit(train_features,
train_labels,
epochs=500,
batch_size=200,
validation_data=(validation_features,
validation_labels),
callbacks=[early_stopping])
model.save(MODEL_NAME)
return model
def get_model(num_inputs, num_actions, hidden_size):
model = Sequential()
model.add(Dense(hidden_size, input_dim=num_inputs, activation='relu'))
model.add(Dense(hidden_size, activation='relu'))
model.add(Dense(num_actions))
model.compile(sgd(lr=.00001), loss="mse")
return model
def train_canterpillar_with_generator(name):
model = canterpillar_net()
model.summary()
optimiser = sgd(momentum=0.9, nesterov=True)
model.compile(optimizer=optimiser, loss=mean_squared_error)
x_train, x_test = prepare_data_for_canterpillar(segment_len=None)
batch_size = 20
steps_per_epoch = 15
print("батчей за эпоху будет:" + str(steps_per_epoch))
print("в одном батче " + str(batch_size) + " кардиограмм.")
train_generator = ecg_batches_generator(segment_len=ecg_segment_len,
batch_size=batch_size,
ecg_dataset=x_train)
test_generator = ecg_batches_generator(segment_len=ecg_segment_len,
batch_size=batch_size,
ecg_dataset=x_test)
tb_callback = TensorBoard(log_dir='./caterpillar_logs',
histogram_freq=5,
write_graph=True,
write_grads=True)
y_test = next(test_generator)
history = model.fit_generator(generator=train_generator,
steps_per_epoch=steps_per_epoch,
epochs=50,
validation_data=y_test,
validation_steps=2,
callbacks=[tb_callback])
save_history(history, name)
model.save(name + '.h5')
return model
def get_hyperparameter(self):
hyper = dict()
############################
'''
(1) 파라미터 값들을 수정해주세요
'''
hyper['batch_size'] = 16 # 배치 사이즈 16
hyper['epochs'] = 20 # epochs은 최대 20 설정 !!
#hyper['learning_rate'] = 1.0 # 학습률
#hyper['learning_rate'] = 0.1 # 학습률
#hyper['learning_rate'] = 0.01
#hyper['learning_rate'] = 0.001
hyper['learning_rate'] = 0.0001 # 학습률
# 최적화 알고리즘 선택 [sgd, rmsprop, adagrad, adam 등]
#hyper['optimizer'] = optimizers.Adadelta(lr=hyper['learning_rate']) # default: SGD
hyper['optimizer'] = optimizers.sgd(lr=hyper['learning_rate'],
decay=1e-6)
#hyper['optimizer'] = optimizers.RMSprop(lr=hyper['learning_rate'], decay=1e-6)
#hyper['optimizer'] = optimizers.Adamax(lr=hyper['learning_rate'])
#hyper['optimizer'] = optimizers.Nadam(lr=hyper['learning_rate'])
#hyper['optimizer'] = optimizers.Adagrad(lr=hyper['learning_rate'])
############################
return hyper
def train_model(model_name):
model, name=model_name
if name=='emotion_bn':
# model.compile(loss=losses.categorical_crossentropy,\
# optimizer=optimizers.sgd(lr=1e-4), \
# metrics=['accuracy'])
model.compile(loss=losses.categorical_crossentropy,\
optimizer=optimizers.sgd(lr=1e-4), \
metrics=['accuracy'])
checkpoint = ModelCheckpoint(filepath='/Users/nex03343/Desktop/CS231N/project/model_emotion_custom.hdf5', monitor='val_acc',\
verbose=1, save_best_only=True, mode='max')
csv_logger = CSVLogger('/Users/nex03343/Desktop/CS231N/project/training_emotion_custom.log')
history=model.fit(x_train,y_train,
epochs=epochs ,
batch_size=batch_size,
shuffle=True,
verbose=1,
validation_data=(x_val,y_val),
callbacks=[csv_logger, checkpoint])
return history
def main():
# load dataset
print('Loading dataset...')
(x_train, y_train), (x_test, y_test) = load_data()
# build model
print('Building model...')
model = build_model()
# compile model
print('Compiling model...')
optimizer = sgd(0.01, 0.9, 0.0005, nesterov=True)
model.compile(optimizer=optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
#output = model.layers[1].output
#output = output.eval(session=K.get_session())
# train model
for i in range(20):
print(i + 1)
print('Training model...')
model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True)
# evaluate model
print('Evaluating model...')
score = model.evaluate(x_test, y_test)
print('Test accuracy: ', score[1])
print('Test loss: ', score[0])
def setup_model(args):
''' Build and compile the models. '''
# Build the models.
gen, dis, full = build_model(args)
# The full model is to train the generator, so freeze the discriminator.
# Due to a bug in preserving the trainable state of a sub model after
# saving and loading a model, each layer has to be set to untrainable
# instead of just setting the discriminator sub model to be untrainable.
for layer in full.get_layer('discriminator').layers:
layer.trainable = False
full.get_layer('discriminator').set_weights(dis.get_weights())
# Compile the models for training.
dis.compile(optimizer=adam(lr=args.dis_lr), loss=discriminator_loss)
full.compile(optimizer=sgd(lr=args.gen_lr), loss=generator_loss)
# Show the model architectures.
print('discriminator')
dis.summary()
print('stacked')
full.summary()
return gen, dis, full
def __init__(self,
name=None,
width=4,
height=4,
weights_directory="model_weights"):
"""Initialization: properties of the model"""
self.epsilon = .1 # exploration
self.num_actions = 4 # [up, right, down, left]
self.hidden_size = 100
self.name = name
self.model = Sequential()
self.model.add(
Dense(self.hidden_size,
input_shape=(width * height, ),
activation='relu'))
self.model.add(Dense(self.hidden_size, activation='relu'))
self.model.add(Dense(self.num_actions))
self.model.compile(sgd(lr=.2), "mse")
self.weights_directory = weights_directory
if name:
suffix = f"-{name}"
else:
suffix = ""
self.model_file = os.path.join(weights_directory,
f"model_weights{suffix}.h5")
def get_optimizer(opt_name):
if opt_name == 'sgd':
return optimizers.sgd(lr=SGD_LEARNING_RATE, momentum=0.5)
elif opt_name == 'adam':
return optimizers.adam(lr=ADAM_LEARNING_RATE)
else:
return opt_name
def build_model_emotion1():
"""
构建模型, 三个隐藏层, 使用的是sigmoid激活函数
最后一层使用softmax
:return: model
"""
main_input = Input(shape=(1, 6373), name='main_input')
x = Dense(64, activation='sigmoid')(main_input)
x = Dense(64, activation='sigmoid')(x)
x = Dense(64, activation='sigmoid')(x)
x = Flatten()(x)
y_emotion = Dense(8, activation='softmax')(x)
model = Model(inputs=[main_input], outputs=[y_emotion])
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
sgd = optimizers.sgd(lr=0.01)
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model.summary()
return model
def get_model(img_x, img_y):
model = Sequential()
model.add(
Conv2D(64,
kernel_size=(5, 5),
padding='same',
activation='relu',
input_shape=(img_x, img_y, 1)))
model.add(MaxPool2D(pool_size=(2, 2), strides=(1, 1)))
model.add(
Conv2D(128, kernel_size=(5, 5), padding='same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(1, 1)))
model.add(
Conv2D(256, kernel_size=(5, 5), padding='same', activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2), strides=(1, 1)))
model.add(Flatten())
model.add(Dense(300, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(8, activation='softmax'))
adam = optimizers.sgd(lr=0.01, momentum=0.9)
model.compile(
optimizer=adam,
loss='categorical_crossentropy',
# loss='mse',
metrics=['accuracy'])
return model
def main():
# load dataset
print('Loading dataset...')
(x_train, y_train), (x_test, y_test) = load_data()
# build model
print('Building model...')
model = build_model()
#plot_model(model, to_file='model.png')
#exit()
# compile model
print('Compiling model...')
optimizer = sgd(0.01, 0.9, 0.0005)
#optimizer = keras.optimizers.rmsprop(lr=0.0001,decay=1e-6)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
# train model
for i in range(20):
print(i+1)
print('Training model...')
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True)
# evaluate model
print('Evaluating model...')
score = model.evaluate(x_test, y_test)
print('Test accuracy: ', score[1])
print('Test loss: ', score[0])
def ann(x_data, y_data):
nb_feartures = x_data.shape[1]
nb_classes = 2
batch_size = int(round(x_data.shape[0]*0.1, 0))
nb_neuron_1 = int(round(nb_feartures*2/3))
nb_neuron_2 = int(round(nb_neuron_1*2/3))
nb_neuron_3 = int(round(nb_neuron_2*2/3))
early_stop = EarlyStopping(monitor='val_loss', min_delta=0, patience=100, verbose=1, mode='auto')
callbacks_list = [early_stop]
model = Sequential()
model.add(Dense(nb_neuron_1, input_dim=nb_feartures))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_neuron_2))
model.add(Activation('relu'))
model.add(Dropout(0.5))
# model.add(Dense(nb_neuron_3))
# model.add(Activation('tanh'))
model.add(Dense(nb_classes, activation='softmax'))
model.compile(optimizer=optimizers.sgd(lr=1e-2), loss=losses.mse, metrics=['accuracy'])
history = model.fit(x_data, to_categorical(y_data),
batch_size=batch_size,
epochs=500,
shuffle=True,
validation_split=0.33,
callbacks=callbacks_list)
return model, history
def __init__(self,
allowed_actions,
height,
width,
name=None,
learning_rate=0.01,
epsilon_decay=0.9999):
self.action_size = len(allowed_actions)
self.actions = allowed_actions
self.state_size = 400
self.gamma = 0.9
self.epsilon = 1.0
self.epsilon_min = 0.01
self.epsilon_decay = epsilon_decay
self.learning_rate = learning_rate
if name is not None and os.path.isfile("model-" + name):
model = load_model("model-" + name)
else:
model = Sequential()
model.add(
Dense(24, input_shape=(self.state_size, ), activation='relu'))
model.add(Dense(24, activation="relu"))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss='mse', optimizer=sgd(lr=self.learning_rate))
self.model = model
def _build_model(self):
model = Sequential()
model.add(Dense(100, input_dim=self.state_size, activation='relu')) # changed layer count from 24
model.add(Dense(80, activation='relu'))
model.add(Dense(self.action_size, activation='linear')) # changed this from linear
model.compile(loss='mse', optimizer=sgd(lr=self.learning_rate))
return model
def _build_model(self):
model = Sequential()
model.add(Dense(24, input_dim=self.state_size, activation='relu'))
model.add(Dense(24, activation='relu'))
model.add(Dense(self.action_size, activation='linear'))
model.compile(loss='mse', optimizer=sgd(lr=self.learning_rate))
return model
def __init__(self, input_dim, output_dim=0, lr=0.01):
self.input_dim = input_dim
self.lr = lr
# LSTM 신경망
self.model = Sequential()
self.model.add(
LSTM(256,
input_shape=(5, 15),
return_sequences=True,
stateful=False,
dropout=0.5))
# 기존 LSTM 모델
# self.model.add(LSTM(256, input_shape=input_dim,
# return_sequences=True, stateful=False, dropout=0.5))
self.model.add(BatchNormalization())
self.model.add(
LSTM(256, return_sequences=True, stateful=False, dropout=0.5))
self.model.add(BatchNormalization())
self.model.add(
LSTM(256, return_sequences=False, stateful=False, dropout=0.5))
self.model.add(BatchNormalization())
self.model.add(Dense(3))
# self.model.add(Dense(units=3, activation='softmax'))
self.model.add(Activation('linear'))
self.model.compile(optimizer=sgd(lr=lr),
loss='mse',
metrics=['accuracy'])
self.prob = None
def __init__(self, input_dim=0, output_dim=0, lr=0.01):
self.input_dim = input_dim
self.lr = lr
#LSTM 신경망
self.model = Sequential()
self.model.add(
LSTM(256,
input_shape=(1, input_dim),
return_sequences=True,
stateful=False,
dropout=0.5))
self.model.add(BatchNormalization())
self.model.add(
LSTM(256, return_sequences=True, stateful=False, dropout=0.5))
self.model.add(BatchNormalization())
self.model.add(
LSTM(256, return_sequences=False, stateful=False, dropout=0.5))
self.model.add(Dense(output_dim))
self.model.add(Activation('sigmoid'))
# print("help")
# print(help(Adadelta))
# adadelta = Adadelta(lr=lr, rho=0.95, epsilon=None, decay=0.0)
# adam = Adam(lr=self.lr,beta_1=0.9,beta_2=0.999)
# self.model.compile(optimizer=Adam(lr=self.lr,beta_1=0.9,beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),metrics=['accuracy'],loss='mse')
self.model.compile(optimizer=sgd(lr=lr), loss='mse')
self.prob = None
def __init__(self):
# "name": behavior instance
self.agents = {}
# Internal episode counter.
self.epcount = 1
# Go through agent_config and create an agent with the corresponding model.
for key in agent_config.keys():
# Create NN:
# This is a bad model. I just want to see if instantiating this works.
# I should also probably clean this up. A lot.
nn = Sequential()
nn.add(Dense(20, input_shape=(16, ), activation="relu"))
nn.add(Dense(20, activation="tanh"))
nn.add(Dense(20, activation="tanh"))
nn.add(Dense(3, activation="linear"))
nn.compile(sgd(lr=0.01), "mse")
agent_instance = agent(nn)
# Warm up the agent and immediately reset:
# TODO: maybe fix act()? During training we might not necessarily want
# to act on an actual stock.
agent_instance.act(1, np.ones((1, 16)), np.ones((1, 16)))
agent_instance.reset()
behavior_instance = agent_config[key]()
self.agents[key] = (agent_instance, behavior_instance)
def baseline_model(grid_size, num_actions, hidden_size):
# setting up the model with keras
model = Sequential()
model.add(Dense(hidden_size, input_shape=(grid_size,), activation='relu'))
model.add(Dense(hidden_size, activation='relu'))
model.add(Dense(num_actions))
model.compile(sgd(lr=.01), "mse")
return model
def get_model ():
m = Sequential ()
m.add (Dense (hidden_size, input_shape=(4,), activation='relu'))
m.add (Dense (hidden_size, activation='relu'))
m.add (Dense (hidden_size, activation='relu'))
m.add (Dense (num_actions))
m.compile(sgd(lr=.01), "mse")
return m
def compile(self, opt):
"""
Optimization and Loss definition
"""
self.model.compile(
optimizer=sgd(),
loss=["mse", "categorical_crossentropy"]
)
def train(images, labels, weights):
model = Sequential()
model.add(Dense(32, input_dim=64, init='uniform', weights=weights))
model.add(Activation('relu'))
model.add(Dense(output_dim=10))
model.add(Activation('softmax'))
_sgd = sgd(lr=0.0228)
model.compile(loss='categorical_crossentropy', optimizer=_sgd, metrics=['accuracy'])
model.fit(images, labels, validation_split=0.3, nb_epoch=1488)
def auto_encode():
model = Sequential()
model.add(Dense(32, input_dim=64, init='uniform'))
model.add(Activation('relu'))
model.add(Dense(output_dim=64))
model.add(Activation('relu'))
_sgd = sgd(lr=0.0228)
model.compile(loss='mse', optimizer=_sgd, metrics=['accuracy'])
return model
def __init__ (self, model, max_memory=2000, discount=0.7, unfreeze_count=5):
self.max_memory = max_memory
self.discount = discount
self.unfreeze_count = unfreeze_count
self.memory = []
self.buffer = []
self.frozen_model = Sequential.from_config (model.get_config ())
self.frozen_model.compile (sgd(lr=.01), "mse")
def get_model (self):
m = Sequential ()
m.add (Dense (self.hidden_size, input_shape=(self.num_obs,), activation='relu'))
m.add (Dense (self.hidden_size, activation='tanh'))
m.add (Dense (self.num_actions))
#with open("model.json", "r") as outfile:
# model = model_from_json (json.load (outfile))
#model.load_weights ("model.h5")
m.compile(sgd(lr=.003), "mse")
return m
def train(images, labels):
model = Sequential()
model.add(Dense(64, input_dim=64, init='uniform'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dense(output_dim=10))
model.add(BatchNormalization())
model.add(Activation('softmax'))
_sgd = sgd(lr=0.0228, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=_sgd, metrics=['accuracy'])
model.fit(images, labels, validation_split=0.3, nb_epoch=1488, batch_size=750)
def __init__ (self, model, max_memory, discount, unfreeze_count, num_actions):
self.max_memory = max_memory
self.discount = discount
self.unfreeze_count = unfreeze_count
self.num_actions = num_actions
self.memory = list ()
# TODO dont assume sequential model
# note taining algo has no affect because frozen model is never trianed
self.frozen_model = Sequential.from_config (model.get_config ())
self.frozen_model.compile (sgd(lr=.01), "mse")
def __init__(self, regressor=None, gamma = 0.99):
self.na = 3 # set when calling the update function the first time
self.nEpochs = 3000
self.hidden_size = 8
if regressor is None:
regressor = Sequential()
regressor.add(Dense(self.hidden_size, input_shape=(2,), activation='tanh', init='glorot_uniform')) #ici je triche un peu je sais qu'il y en a 2
regressor.add(Dense(self.hidden_size, activation='tanh', init='glorot_uniform'))
regressor.add(Dense(3)) # je triche pcq je sais deja le nb d actions
regressor.compile(sgd(lr=0.000001 ,momentum=0.9, nesterov=True), "mse")
self.Q = regressor
self.gamma = gamma
self.t = 0 # current iteration
def get_optimizer(config):
if(config['optimizer'] == 'rmsprop'):
opti = optimizers.rmsprop(lr=config['learning_rate'],
clipvalue=config['grad_clip'],
decay=config['decay_rate'])
return opti
elif(config['optimizer'] == 'adadelta'):
opti = optimizers.adadelta(lr=config['learning_rate'],
clipvalue=config['grad_clip'])
return opti
elif(config['optimizer'] == 'sgd'):
opti = optimizers.sgd(lr=config['learning_rate'],
momentum=config['momentum'],
decay=config['learning_rate_decay'])
return opti
else:
raise KeyError('optimizer name error')
def run():
global ACTION_LIST
# parameters
epsilon = .1 # exploration
num_actions = len(ACTION_LIST) # [buy, hold, sell]
transcation_cost = 0.0005
epoch = 1000
max_memory = 5000
hidden_size = 300
batch_size = 50
look_back_term = 100
training_period = 3000
# log
time_start_epoch = datetime.datetime.now()
time_start = strftime("%Y-%m-%d-%H:%M:%S", gmtime())
log_name = '../log/DRL_Trading_Learning_v1_' + time_start + '.log'
logging.basicConfig(filename=log_name,level=logging.DEBUG)
logging.info("Time start : " + str(time_start))
logging.info("Parameter setting :")
logging.info("epsilon = " + str(epsilon))
logging.info("transaction_cost = " + str(transcation_cost))
logging.info("epoch =" + str(epoch))
logging.info("max_memory = " + str(max_memory))
#logging.info("batch_size = " + str(batch_size))
logging.info("look back term = " + str(look_back_term))
logging.info("hidden_size = " + str(hidden_size))
logging.info("training period = " + str(training_period))
print "log start"
# import return data
data = pd.read_csv("../Data/GBPUSD30.csv",header=None)
close = data[5].values
ret = (close[1:] - close[:-1])[:training_period]
train_percent = 1
ret_train = ret[:len(ret) * train_percent]
ret_test = ret[len(ret) :]
model = Sequential()
model.add(Dense(hidden_size, input_shape=(look_back_term,), activation='relu'))
model.add(Dense(hidden_size, activation='relu'))
model.add(Dense(hidden_size, activation='relu'))
model.add(Dense(num_actions))
model.compile(sgd(lr=.2), "mse")
env = FX_Market(ret_train = ret_train, look_back_term = look_back_term, transaction_cost = transcation_cost)
trading_his = Trading_Memory(max_memory = max_memory)
# Train
return_list = []
for e in range(epoch):
print "epoch : " + str(e)
env.reset()
accumulate_ret = [0.0]
for t in range(look_back_term - 1 , len(ret_train) - 2) :
state = env.get_state(t)
# decide action
if np.random.rand() < epsilon:
action = np.random.randint(0, num_actions, size=1)
else:
q = model.predict(state)
action = np.argmax(q[0])
new_state, reward = env.act(t, action)
accumulate_ret.append(accumulate_ret[-1] + reward)
trading_his.memory(state, new_state, action, reward)
inputs, targets = trading_his.get_batch(model, batch_size=batch_size)
model.train_on_batch(inputs, targets)
print "accumulate return : " + str(accumulate_ret[-1])
return_list.append(accumulate_ret[-1])
logging.info("accumulate return : " + str(accumulate_ret[-1]))
loop_time = datetime.datetime.now() - time_start_epoch
time_left = float(loop_time.seconds) / 3600.0 / float(e+1) * float(epoch - e + 1)
print "left time : " + str(time_left) + " hours"
#plt.plot(range(len(return_list)),return_list)
#plt.show()
print "finished"
#screen = pygame.display.set_mode(size)
grid_size = 15
hidden_size = 100
nb_frames = 1
gameMap = np.zeros((nb_frames, grid_size, grid_size))
gameMap[0][cheesePos[0]/20][cheesePos[1]/20] = 1
gameMap[0][mousePos[0]/20][mousePos[1]/20] = 2
nn = Sequential()
nn.add(Flatten(input_shape=(nb_frames, grid_size, grid_size)))
nn.add(Dense(hidden_size, activation='relu'))
nn.add(Dense(hidden_size, activation='relu'))
nn.add(Dense(4))
nn.compile(sgd(lr=.2), "mse")
memory = ExperienceReplay(100)
loss = 0.
def nnEvalSate(pos, gameMap, exploRate, loss):
mouseState = np.array(pos/20)
nnoutputs = nn.predict(np.array([gameMap]), batch_size=1) #nn.feed_forward(mouseState)
nnoutputs = nnoutputs[0] #nnoutputs = np.array(nn.output_layer.get_outputs())
actionId = 0
if random.random() < exploRate:
actionId = random.randrange(4)
else:
actionId = random.choice(np.argwhere(nnoutputs == nnoutputs.max()).flatten())
Rpoints, newMouseState = reward(mouseState, actions[actionId])
# --------------------------------------------
# Design the DNN model
# --------------------------------------------
#==========================================================================
model = Sequential()
model.add(Dense(output_dim=hidden_size,
#init=lambda shape, name: normal(shape, scale=0.001, name=name),
#inner_init=lambda shape, name: identity(shape, scale=1.0, name=name),
activation='linear',
input_shape=x_train.shape[1:]))
# model.add(Dense(hidden_size, activation='relu'))
model.add(Dense(hidden_size, activation='tanh'))
model.add(Dense(1, activation='linear'))
model.compile(sgd(lr=0.05), "mse")
model.fit(x_train, x_label, batch_size=mb, nb_epoch=15
)
###design DNN and fit
###save the weights
model.save_weights(root_folder_path + 'nn_keras_%d'%split + ".h5", overwrite=True)
with open(root_folder_path + 'nn_keras_%d'%split + ".json", "w") as outfile:
json.dump(model.to_json(), outfile)
stop = timeit.default_timer()
print ("The running takes %r min" %((stop-start)/60))
def run():
global ACTION_LIST
# parameters
epsilon = .1 # exploration
num_actions = len(ACTION_LIST) # [buy, hold, sell]
transcation_cost = 0.0005
epoch = 200
max_memory = 1000
batch_size = max_memory
look_back_term = 100
hidden_size = look_back_term
act_function = "relu"
learning_rate = .2
# import return data
data = pd.read_csv("../Data/GBPUSD30.csv",header=None)
close = data[5].values
ret = (close[1:] - close[:-1])[:1000]
train_percent = 1
ret_train = ret[:len(ret) * train_percent]
#model.add(Dense(hidden_size, input_shape=(look_back_term,), activation=act_function))
#model.add(Dense(hidden_size, activation=act_function))
#model.add(Dense(hidden_size, activation=act_function))
#model.add(Dense(num_actions))
#model.compile(sgd(lr=learning_rate), "mse")
model = Sequential()
#model.add(Embedding(look_back_term, embedding_dims, input_length=maxlen, dropout=0.2))
model.add(Convolution1D(hidden_size, 2, input_shape=(look_back_term,),activation=act_function))
model.add(Dense(num_actions))
model.compile(sgd(lr=learning_rate), "mse")
env = FX_Market(ret_train = ret_train, look_back_term = look_back_term, transaction_cost = transcation_cost)
trading_his = Trading_Memory(max_memory = max_memory)
logging.basicConfig(filename='DRL_Trading_Learning_v2.log',level=logging.INFO)
logging.info("Start time : " + strftime("%Y-%m-%d %H:%M:%S", gmtime()))
logging.info("Parameter setting :")
logging.info("epsilon = " + str(epsilon))
logging.info("transaction_cost = " + str(transcation_cost))
logging.info("epoch =" + str(epoch))
logging.info("max_memory = " + str(max_memory))
logging.info("batch_size = " + str(batch_size))
logging.info("look back term = " + str(look_back_term))
logging.info("hidden_size = " + str(hidden_size))
logging.info("activation function = " + act_function)
logging.info("learning rate" + str(learning_rate))
# Train
return_list = []
for e in range(epoch):
print "epoch : " + str(e)
env.reset()
accumulate_ret = [0.0]
for t in range(look_back_term - 1 , len(ret_train) - 2) :
state = env.get_state(t)
# decide action
if np.random.rand() < epsilon:
action = np.random.randint(0, num_actions, size=1)
else:
q = model.predict(state)
action = np.argmax(q[0])
new_state, reward = env.act(t, action)
accumulate_ret.append(accumulate_ret[-1] + reward)
trading_his.memory(state, new_state, action, reward)
inputs, targets = trading_his.get_batch(model, batch_size=batch_size)
model.train_on_batch(inputs, targets)
print "accumulate return : " + str(accumulate_ret[-1])
logging.info("accumulate return : " + str(accumulate_ret[-1]))
return_list.append(accumulate_ret[-1])
#===============================================================================
# result = pd.DataFrame()
# result["accumulate return"] = return_list
# result.to_csv("./DRL_result_1_14052016.csv")
#
# model.save_weights("./model2.h5", overwrite=True)
# with open("model2.json", "w") as outfile:
# json.dump(model.to_json(), outfile)
#===============================================================================
plt.plot(range(len(return_list)),return_list,"r.")
plt.show()
if __name__ == "__main__":
# parameters
epsilon = .1 # exploration
num_actions = 3 # [move_left, stay, move_right]
epoch = 1000
max_memory = 500
hidden_size = 100
batch_size = 50
grid_size = 10
model = Sequential()
model.add(Dense(hidden_size, input_shape=(grid_size**2,), activation='relu'))
model.add(Dense(hidden_size, activation='relu'))
model.add(Dense(num_actions))
model.compile(sgd(lr=.2), "mse")
# If you want to continue training from a previous model, just uncomment the line bellow
# model.load_weights("model.h5")
# Define environment/game
env = Catch(grid_size)
# Initialize experience replay object
exp_replay = ExperienceReplay(max_memory=max_memory)
# Train
win_cnt = 0
for e in range(epoch):
loss = 0.
env.reset()
def run():
global ACTION_LIST
# parameters
epsilon = 0.1 # exploration
num_actions = len(ACTION_LIST) # [buy, hold, sell]
transcation_cost = 0.0005
epoch = 150
max_memory = 1000000
hidden_size = 600
batch_size = 50
look_back_term = 200
training_period_start = 0
training_period_stop = 10000
learning_rate = 0.1
discount_rate = 0.95
step_size = 10 # iterate step to update target_model
input_data = "GBPUSD240.csv"
# log
time_start_epoch = datetime.datetime.now()
time_start = strftime("%Y-%m-%d-%H-%M-%S", gmtime())
log_name = '../log/DRL_Learning_v6_' + time_start + '.log'
logging.basicConfig(filename=log_name,level=logging.DEBUG)
logging.info("Time start : " + str(time_start))
logging.info("Input data :" + input_data)
logging.info("Parameter setting :")
logging.info("epsilon = " + str(epsilon))
logging.info("transaction_cost = " + str(transcation_cost))
logging.info("epoch =" + str(epoch))
logging.info("max_memory = " + str(max_memory))
logging.info("batch_size = " + str(batch_size))
logging.info("look back term = " + str(look_back_term))
logging.info("hidden_size = " + str(hidden_size))
logging.info("training period = " + str(training_period_start) + " ~ " + str(training_period_stop))
logging.info("learning rate = " + str(learning_rate))
logging.info("discount rate = " + str(discount_rate))
logging.info("step_size = " + str(step_size))
print "log start"
# import return data
data = pd.read_csv("../Data/" + input_data,header=None)
close = data[5].values
ret_train = (close[1:] - close[:-1])[training_period_start : training_period_stop]
#ret_train_shared = shared(np.asarray(ret_train), name="ret_train")
#print ret_train_shared.get_value()
model = Sequential()
model.add(Dense(hidden_size, input_shape=(look_back_term,), activation='relu'))
model.add(Dense(hidden_size, activation='relu'))
model.add(Dense(hidden_size, activation='relu'))
model.add(Dense(num_actions))
model.compile(sgd(lr=learning_rate), "mse")
target_model = copy.deepcopy(model)
env = FX_Market(ret_train = ret_train, look_back_term = look_back_term, transaction_cost = transcation_cost)
trading_his = Trading_Memory(max_memory = max_memory, discount=discount_rate)
# Train
return_list = []
for e in range(epoch):
print "epoch : " + str(e)
env.reset()
accumulate_ret = [0.0]
if e % step_size == 0:
target_model = copy.deepcopy(model)
for t in range(look_back_term - 1 , len(ret_train) - 2) :
state = env.get_state(t)
# decide action
if np.random.rand() < epsilon:
action = np.random.randint(0, num_actions, size=1)
else:
q = target_model.predict(state)
action = np.argmax(q[0])
new_state, reward = env.act(t, action)
accumulate_ret.append(accumulate_ret[-1] + reward)
trading_his.memory(state, new_state, action, reward)
inputs, targets = trading_his.get_batch(target_model, model,batch_size=batch_size)
model.train_on_batch(inputs, targets)
print "accumulate return : " + str(accumulate_ret[-1])
return_list.append(accumulate_ret[-1])
logging.info("accumulate return : " + str(accumulate_ret[-1]))
loop_time = datetime.datetime.now() - time_start_epoch
time_left = float(loop_time.seconds) / 3600.0 / float(e+1) * float(epoch - e + 1)
print "left time : " + str(time_left) + " hours"
result = pd.DataFrame()
result["accumulate return"] = return_list
result.to_csv("../Result_Data/DRL_v6_result_" + time_start + ".csv")
model.save_weights("../Model/DRL_model_v6_" + time_start + ".h5", overwrite=True)
with open("../Model/DRL_model_v6_" + time_start + ".json", "w") as outfile:
json.dump(model.to_json(), outfile)
#plt.plot(range(len(return_list)),return_list,"r.")
#plt.show()
#test(model, ret_test)
time_used = datetime.datetime.now() - time_start_epoch
time_used = float(time_used.seconds) / 3600.0
logging.info("Processing time : " + str(time_used) + " hours")
print "finished"
def run(optimizer_list):
global ACTION_LIST
global floatX
global time_get_batch
# parameters
for optimizer in optimizer_list :
version = str(7)
epsilon = 0.1 # exploration
num_actions = len(ACTION_LIST) # [buy, hold, sell]
transcation_cost = 0.0005
epoch = 500
max_memory = 1000000
hidden_size = 600
batch_size = 300
look_back_term = 300
training_period_start = -10500
training_period_stop = -500
learning_rate = 0.1
discount_rate = 0.000009
step_size = 10 # iterate step to update target_model
act_function = "relu"
comment = "Learning rate test ( " + optimizer + " )"
#frame_skip = 4 # train the model with some frames intervals
input_data = "GBPUSD240.csv"
#input_data = "GBP_USD240_oanda.csv"
# log
time_start_epoch = datetime.datetime.now()
time_start = strftime("%Y-%m-%d-%H-%M-%S", gmtime())
log_name = '../log/DRL_Learning_v' + version + '_' + time_start + '.log'
logging.basicConfig(filename=log_name,level=logging.DEBUG)
logging.info("Version : " + version)
logging.info("Time start : " + str(time_start))
logging.info("Input data :" + input_data)
logging.info("Parameter setting :")
logging.info("epsilon = " + str(epsilon))
logging.info("transaction_cost = " + str(transcation_cost))
logging.info("epoch =" + str(epoch))
logging.info("max_memory = " + str(max_memory))
logging.info("batch_size = " + str(batch_size))
logging.info("look back term = " + str(look_back_term))
logging.info("hidden_size = " + str(hidden_size))
logging.info("training period = " + str(training_period_start) + " ~ " + str(training_period_stop))
logging.info("learning rate = " + str(learning_rate))
logging.info("discount rate = " + str(discount_rate))
logging.info("step_size = " + str(step_size))
logging.info("activation function = " + act_function)
logging.info("comment :" + comment)
#logging.info("frame_skip" + str(frame_skip))
print "log start"
# import return data
data = pd.read_csv("../Data/" + input_data,header=None)
close = data[5].values
# import return data from oanda data
#data = pd.read_csv("../Data/" + input_data,header=0)
#print data
#close = data["closeAsk"].values
#print close
ret_train = (close[1:] - close[:-1])[training_period_start : training_period_stop]
#build model : online mode and target model
model = Sequential()
model.add(Dense(hidden_size, input_shape=(look_back_term,), activation=act_function))
model.add(Dense(hidden_size, activation=act_function))
model.add(Dense(hidden_size, activation=act_function))
model.add(Dense(num_actions))
if optimizer == "SGD" :
opti = sgd(lr=learning_rate)
elif optimizer == "RMSprop" :
opti = keras.optimizers.RMSprop(lr=0.001, rho=0.9, epsilon=1e-08)
elif optimizer == "Adam" :
opti = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
elif optimizer == "Adadelta" :
opti = keras.optimizers.Adadelta(lr=1.0, rho=0.95, epsilon=1e-08)
elif optimizer == "Adagrad":
opti = keras.optimizers.Adagrad(lr=0.01, epsilon=1e-08)
elif optimizer == "Adamax" :
opti = keras.optimizers.Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(opti, "mse")
write_model(model, version, time_start)
target_model = read_model(version, time_start)
# create market
env = FX_Market(ret_train = ret_train, look_back_term = look_back_term, transaction_cost = transcation_cost)
# create memory
trading_his = Trading_Memory(max_memory = max_memory, discount=discount_rate)
# Train
return_list = []
for e in range(epoch):
print "epoch : " + str(e)
env.reset()
accumulate_ret = [0.0] # pips earn from fx market
if e % step_size == 0:
write_model(model, version, time_start)
target_model = read_model(version, time_start)
#time_cal = list(np.zeros(6))
time_get_batch = list(np.zeros(7))
for t in range(look_back_term - 1 , len(ret_train) - 2) :
#if np.random.random_integers(1,frame_skip) == 4 :
state = env.get_state(t)
# decide action
if np.random.rand() < epsilon:
action = np.random.randint(0, num_actions, size=1)
else:
q = target_model.predict(state)
action = np.argmax(q[0])
new_state, reward = env.act(t, action)
accumulate_ret.append(accumulate_ret[-1] + reward)
trading_his.memory(state, new_state, action, reward)
#save_variable("memory_" + time_start, trading_his.get_memory())
inputs, targets = trading_his.get_batch(target_model, model,batch_size=batch_size)
model.train_on_batch(inputs, targets)
#for i in range(len(time_cal)) :
# print "process " + str(i) + " : " + str(time_cal[i]/60) + " minutes"
print "accumulate return : " + str(accumulate_ret[-1])
return_list.append(accumulate_ret[-1])
logging.info("accumulate return : " + str(accumulate_ret[-1]))
loop_time = datetime.datetime.now() - time_start_epoch
time_left = float(loop_time.seconds) / 3600.0 / float(e+1) * float(epoch - e + 1)
print "left time : " + str(time_left) + " hours" + ", optimizer = " + optimizer
time_used = datetime.datetime.now() - time_start_epoch
time_used = float(time_used.seconds) / 3600.0
logging.info("Processing time : " + str(time_used) + " hours")
#save_variable("../Temp/memory_" + time_start + ".mem", trading_his.get_memory())
#output accumulate data
result = pd.DataFrame()
result["accumulate return"] = return_list
result.to_csv("../Result_Data/DRL_v" + version + "_result_" + time_start + ".csv")
#output model
model.save_weights("../Model/DRL_model_v" + version + "_" + time_start + ".h5", overwrite=True)
with open("../Model/DRL_model_v" + version + "_" + time_start + ".json", "w") as outfile:
json.dump(model.to_json(), outfile)
outfile.close()
#plt.plot(range(len(return_list)),return_list,"r.")
#plt.show()
#test(model, ret_test)
print "finished"
self.count = self.min_space
print 'disturbed'
#return np.random.randint (self.action_space)
# always disturb right MUHAHAHAHA
return 0
return default
def __call__ (self, default):
return self.disturb (default)
disturb = Disturb ()
with open("model3.json", "r") as outfile:
model = model_from_json (json.load (outfile))
model.load_weights ("model3.h5")
model.compile(sgd(lr=.01), "mse")
def get_env ():
e = gym.make ('CartPole-v0')
return e
env = get_env ()
def eval_iter (disturbance=False):
frame_count = 0
loss = 0
done = False
obs_t = env.reset ()
while not done:
# render
env.render ()
def run():
global ACTION_LIST
# parameters
epsilon = .1 # exploration
num_actions = len(ACTION_LIST) # [buy, hold, sell]
transcation_cost = 0.0005
epoch = 500
max_memory = 6000
#batch_size = 50
look_back_term = 50
hidden_size = 300
act_function = "sigmoid"
learning_rate = 1.0
training_period = 100
# log
time_start_epoch = datetime.datetime.now()
time_start = strftime("%Y-%m-%d-%H:%M:%S", gmtime())
log_name = '../log/DRL_Trading_Learning_v1_' + time_start + '.log'
logging.basicConfig(filename=log_name,level=logging.DEBUG)
logging.info("Time start : " + str(time_start))
logging.info("Parameter setting :")
logging.info("epsilon = " + str(epsilon))
logging.info("transaction_cost = " + str(transcation_cost))
logging.info("epoch =" + str(epoch))
logging.info("max_memory = " + str(max_memory))
#logging.info("batch_size = " + str(batch_size))
logging.info("look back term = " + str(look_back_term))
logging.info("hidden_size = " + str(hidden_size))
logging.info("activation function = " + act_function)
logging.info("learning rate = " + str(learning_rate))
logging.info("training period = " + str(training_period))
print "log start :" + str(time_start)
# import return data
data = pd.read_csv("../Data/GBPUSD30.csv",header=None)
close = data[5].values
ret = (close[1:] - close[:-1])[:training_period]
train_percent = 1
ret_train = ret[:len(ret) * train_percent]
model = Sequential()
model.add(Dense(hidden_size, input_shape=(look_back_term,), activation=act_function))
model.add(Dense(hidden_size, activation=act_function))
model.add(Dense(hidden_size, activation=act_function))
model.add(Dense(num_actions))
model.compile(sgd(lr=learning_rate), "mse")
env = FX_Market(ret_train = ret_train, look_back_term = look_back_term, transaction_cost = transcation_cost)
trading_his = Trading_Memory(max_memory = max_memory)
# Train
return_list = []
for e in range(epoch):
#loop_start = datetime.datetime.now()
print "epoch : " + str(e)
env.reset()
trading_his.memory_reset()
accumulate_ret = [0.0]
for t in range(look_back_term - 1 , len(ret_train) - 2) :
state = env.get_state(t)
# decide action
if np.random.rand() < epsilon:
action = np.random.randint(0, num_actions, size=1)
else:
q = model.predict(state)
action = np.argmax(q[0])
new_state, reward = env.act(t, action)
accumulate_ret.append(accumulate_ret[-1] + reward)
trading_his.memory(state, new_state, action, reward)
inputs, targets = trading_his.get_batch(model)
model.train_on_batch(inputs, targets)
print "accumulate return : " + str(accumulate_ret[-1])
logging.info("accumulate return : " + str(accumulate_ret[-1]))
return_list.append(accumulate_ret[-1])
loop_time = datetime.datetime.now() - time_start_epoch
time_left = float(loop_time.seconds) / 3600.0 / float(e+1) * float(epoch - e + 1)
print "left time : " + str(time_left) + " hours"
result = pd.DataFrame()
result["accumulate return"] = return_list
result.to_csv("../Result_Data/DRL_result_" + time_start + ".csv")
model.save_weights("../Model/DRL_v1_model_" + time_start + ".h5", overwrite=True)
with open("../Model/DRL_v1_model_" + time_start + ".json", "w") as outfile:
json.dump(model.to_json(), outfile)
#plt.plot(range(len(return_list)),return_list,"r.")
#plt.show()
#test(model, ret_test)
time_used = datetime.datetime.now() - time_start_epoch
time_used = float(time_used.seconds) / 3600.0
logging.info("Processing time : " + str(time_used) + " hours")
print "finished !"
