def CreateTFGraphTrain(self):
#Tensorflow 4 CNN Model
#Classifier model; Architecture of the CNN
ws = [('C', [3, 3, 3, 10], [1, 1, 1, 1]), ('P', [1, 4, 4, 1], [1, 2, 2, 1]), ('C', [3, 3, 10, 5], [1, 1, 1, 1]), ('P', [1, 4, 4, 1], [1, 2, 2, 1]), ('F', 32), ('F', 16), ('F', 2)]
ims = [self.ss[1] // self.m, self.ss[0] // self.n, 3] #Images from skimage are of shape (height, width, 3)
hmcIms = 30 * 100 * 3 #Number of pixels in health/mana checker images
carg = {'batchSize': 40, 'learnRate': 1e-3, 'maxIter': 40, 'reg': 6e-4, 'tol': 25e-3, 'verbose': True}
self.OC = CNNC(ims, ws, name = 'obcnn', **carg)
self.OC.RestoreClasses(['C', 'O'])
#Used to detect enemies
self.EC = CNNC(ims, ws, name = 'encnn', **carg)
self.EC.RestoreClasses(['N', 'E', 'I'])
#CNN for detecting movement
self.MC = CNNC(ims, ws, name = 'mvcnn', **carg)
self.MC.RestoreClasses(['Y', 'N'])
#Classifier for lightning-warp
self.LC = CNNC(ims, ws, name = 'lwcnn', **carg)
self.LC.RestoreClasses(['Y', 'N'])
#Regressor for health and mana bar checker
self.HR = MLPR([hmcIms, 1], maxIter = 0, name = 'hrmlp')
self.MR = MLPR([hmcIms, 1], maxIter = 0, name = 'mrmlp')
if not self.Restore():
print('Model could not be loaded.')
self.TFS = self.LC.GetSes()
self.DIM = LoadDataset()
self.FitCModel(self.OC, {'Closed/Dried Lake':'C', 'Closed/Oasis':'C', 'Open/Dried Lake':'O', 'Open/Oasis':'O', 'Enemy/Dried Lake':'O', 'Enemy/Oasis':'O'})
self.FitCModel(self.EC, {'Open/Dried Lake':'N', 'Open/Oasis':'N', 'Enemy/Dried Lake':'Y', 'Enemy/Oasis':'Y', 'Item/Dried Lake':'N'})
self.FitCModel(self.MC, {'Move/Dried Lake':'Y', 'NoMove/Dried Lake':'N'})
#self.LC.Reinitialize()
self.FitCModel(self.LC, {'LW/Dried Lake':'Y', 'LW/Oasis':'Y', 'NLW/Dried Lake':'N', 'NLW/Oasis':'N'})
self.FitRModel(self.HR, 'HR')
self.FitRModel(self.MR, 'MR')
self.Save()
def T9():
'''
Tests if multiple MLPRs can be created without affecting each other
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis=1)**2).reshape(-1, 1)
m1 = MLPR([4, 4, 1], maxIter=16)
m1.fit(A, Y)
s1 = m1.score(A, Y)
m2 = MLPR([4, 4, 1], maxIter=16)
m2.fit(A, Y)
s2 = m1.score(A, Y)
if s1 != s2:
return False
return True
def T8():
'''
Tests if multiple MLPRs can be created without affecting each other
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis=1)**2).reshape(-1, 1)
m1 = MLPR([4, 4, 1], maxIter=16)
m1.fit(A, Y)
R1 = m1.GetWeightMatrix(0)
m2 = MLPR([4, 4, 1], maxIter=16)
m2.fit(A, Y)
R2 = m1.GetWeightMatrix(0)
if (R1 != R2).any():
return False
return True
def T13():
'''
Tests restoring a model from file
'''
m1 = MLPR([4, 4, 1], maxIter=16, name='t12ann1')
rv = m1.RestoreModel('./', 't12ann1')
return rv
def T14():
'''
Tests saving and restore a model
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis=1)**2).reshape(-1, 1)
m1 = MLPR([4, 4, 1], maxIter=16, name='t12ann1')
m1.fit(A, Y)
m1.SaveModel('./t12ann1')
R1 = m1.GetWeightMatrix(0)
ANN.Reset()
m1 = MLPR([4, 4, 1], maxIter=16, name='t12ann2')
m1.RestoreModel('./', 't12ann1')
R2 = m1.GetWeightMatrix(0)
if (R1 != R2).any():
return False
return True
def Main():
if len(sys.argv) <= 1:
return
A, Y = GenerateData(ns = 2048)
#Create layer sizes; make 6 layers of nf neurons followed by a single output neuron
L = [A.shape[1]] * 6 + [1]
print('Layer Sizes: ' + str(L))
if sys.argv[1] == 'theano':
print('Running theano benchmark.')
from TheanoANN import TheanoMLPR
#Create the Theano MLP
tmlp = TheanoMLPR(L, batchSize = 128, learnRate = 1e-5, maxIter = 100, tol = 1e-3, verbose = True)
MakeBenchDataSample(tmlp, A, Y, 16, 'TheanoSampDat.csv')
print('Done. Data written to TheanoSampDat.csv.')
if sys.argv[1] == 'theanogpu':
print('Running theano GPU benchmark.')
#Set optional flags for the GPU
#Environment flags need to be set before importing theano
os.environ["THEANO_FLAGS"] = "device=gpu"
from TheanoANN import TheanoMLPR
#Create the Theano MLP
tmlp = TheanoMLPR(L, batchSize = 128, learnRate = 1e-5, maxIter = 100, tol = 1e-3, verbose = True)
MakeBenchDataSample(tmlp, A, Y, 16, 'TheanoGPUSampDat.csv')
print('Done. Data written to TheanoGPUSampDat.csv.')
if sys.argv[1] == 'tensorflow':
print('Running tensorflow benchmark.')
from TFANN import MLPR
#Create the Tensorflow model
mlpr = MLPR(L, batchSize = 128, learnRate = 1e-5, maxIter = 100, tol = 1e-3, verbose = True)
MakeBenchDataSample(mlpr, A, Y, 16, 'TfSampDat.csv')
print('Done. Data written to TfSampDat.csv.')
if sys.argv[1] == 'plot':
print('Displaying results.')
try:
T1 = np.loadtxt('TheanoSampDat.csv', delimiter = ',', skiprows = 1)
except OSError:
T1 = None
try:
T2 = np.loadtxt('TfSampDat.csv', delimiter = ',', skiprows = 1)
except OSError:
T2 = None
try:
T3 = np.loadtxt('TheanoGPUSampDat.csv', delimiter = ',', skiprows = 1)
except OSError:
T3 = None
fig, ax = mpl.subplots(1, 2)
if T1 is not None:
PlotBenchmark(T1[:, 0], T1[:, 1], ax[0], '# Samples', 'Train', 'Theano')
PlotBenchmark(T1[:, 0], T1[:, 2], ax[1], '# Samples', 'Test', 'Theano')
if T2 is not None:
PlotBenchmark(T2[:, 0], T2[:, 1], ax[0], '# Samples', 'Train', 'Tensorflow')
PlotBenchmark(T2[:, 0], T2[:, 2], ax[1], '# Samples', 'Test', 'Tensorflow')
if T3 is not None:
PlotBenchmark(T3[:, 0], T3[:, 1], ax[0], '# Samples', 'Train', 'Theano GPU')
PlotBenchmark(T3[:, 0], T3[:, 2], ax[1], '# Samples', 'Test', 'Theano GPU')
mpl.show()
def T12():
'''
Tests saving a model to file
'''
A = np.random.rand(32, 4)
Y = (A.sum(axis=1)**2).reshape(-1, 1)
m1 = MLPR([4, 4, 1], maxIter=16, name='t12ann1')
m1.fit(A, Y)
m1.SaveModel('./t12ann1')
return True
def score(self, A, y):
#Number of neurons in the input layer
i = 1
#Number of neurons in the output layer
o = 1
#Number of neurons in the hidden layers
h = 32
#The list of layer sizes
layers = [i, h, h, h, h, h, h, h, h, h, o]
mlpr = MLPR(layers, maxItr = 1000, tol = 0.40, reg = 0.001, verbose = True)
def T1():
'''
Tests basic functionality of MLPR
'''
A = np.random.rand(32, 4)
Y = np.random.rand(32, 1)
a = MLPR([4, 4, 1], maxIter=16, name='mlpr1')
a.fit(A, Y)
a.score(A, Y)
a.predict(A)
return True
def CreateTFGraphTest(self):
#Tensorflow 4 CNN Model
#Classifier model; Architecture of the CNN
ws = [('C', [3, 3, 3, 10], [1, 1, 1, 1]), ('P', [1, 4, 4, 1], [1, 2, 2, 1]), ('C', [3, 3, 10, 5], [1, 1, 1, 1]), ('P', [1, 4, 4, 1], [1, 2, 2, 1]), ('F', 32), ('F', 16), ('F', 2)]
ims = [self.ss[1] // self.m, self.ss[0] // self.n, 3] #Image size for CNN model
hmcIms = 30 * 100 * 3 #Number of pixels in health/mana checker images
self.I1 = tf.placeholder("float", [self.NSS] + ims, name = 'S_I1') #Previous image placeholder
self.I2 = tf.placeholder("float", [self.NSS] + ims, name = 'S_I2') #Current image placeholder
self.TV = tf.placeholder("float", [self.NSS, 2], name = 'S_TV') #Target values for binary classifiers
self.LWI = tf.placeholder("float", [2] + ims, name = 'S_LWI')
self.LWTV = tf.placeholder("float", [2, 2], name = 'S_LWTV')
self.HRI = tf.placeholder("float", [1, hmcIms], name = 'S_HRI')
self.MRI = tf.placeholder("float", [1, hmcIms], name = 'S_MRI')
self.RTV = tf.placeholder("float", [1, 1], name = 'S_RTV')
Z = tf.zeros([self.NSS] + ims, name = "S_Z") #Completely black grid of image cells
wcnd = tf.abs(self.I1 - self.I2) > 16 #Where condition
ID = tf.where(wcnd, self.I2, Z, name = 'S_ID') #Difference between images
#Used to detect Obstacles;
carg = {'batchSize': self.NSS, 'learnRate': 1e-3, 'maxIter': 2, 'reg': 6e-4, 'tol': 1e-2, 'verbose': True}
self.OC = CNNC(ims, ws, name = 'obcnn', X = self.I2, Y = self.TV, **carg)
self.OC.RestoreClasses(['C', 'O'])
#Used to detect enemies
self.EC = CNNC(ims, ws, name = 'encnn', X = self.I2, Y = self.TV, **carg)
self.EC.RestoreClasses(['N', 'E', 'I'])
#CNN for detecting movement
self.MC = CNNC(ims, ws, name = 'mvcnn', X = ID, Y = self.TV, **carg)
self.MC.RestoreClasses(['Y', 'N'])
#Classifier for lightning-warp
self.LC = CNNC(ims, ws, name = 'lwcnn', X = self.LWI, Y = self.LWTV, **carg)
self.LC.RestoreClasses(['Y', 'N'])
#Regressor for health-bar checker
self.HR = MLPR([hmcIms, 1], name = 'hrmlp', X = self.HRI, Y = self.RTV, **carg)
self.MR = MLPR([hmcIms, 1], name = 'mrmlp', X = self.MRI, Y = self.RTV, **carg)
if not self.Restore():
print('Model could not be loaded.')
self.TFS = self.LC.GetSes()
print(stockData2) print(scale(stockDataASC)) # to get only specific columns of data from a array use : # data[:, [1, 9]] where data is array and you want columns 1, 9 (index start from 0) # scale the stock data, volume to ease the calculations and fit within the data range # Number of neurons in the input layer # 4 neurons to indicate the candle stick doji patterns # 4 neurons to indicate the previous most tested highs which are greater than previous day closing prices # 4 neurons to indicate the previous most tested lows which are less than previous day closing prices # 5 neurons to indicate the previous day open, close, high and low prices, and volume i = 17 # Number of neurons in the output layer # 5 neurons to indicate the current day open, close, high and low prices and volume o = 5 #Number of neurons in the hidden layers h = 17 #The list of layer sizes layers = [i, h, h, h, h, h, h, h, h, h, o] mlpr = MLPR(layers, maxIter=1000, tol=0.40, reg=0.001, verbose=True) mlpr.fit() #Begin prediction yHat = mlpr.predict(A) #Plot the results mpl.plot(A, Y, c='#b0403f') mpl.plot(A, yHat, c='#5aa9ab') mpl.show()